International Journal of Internet Technology and Secured Transactions (57 papers in press)
An Integrated Approach For Network Traffic Analysis Using Unsupervised Clustering And Supervised Classification
by Chokkanathan Kothandapani
Abstract: Traffic classification and analysis is a significant task to control the network traffic in a heterogeneous manner. The unsupervised learning system or network environment fails to expand the supervised classification model for network analysis. The several data mining techniques identified the network traffic pattern and classified the network traffic accurately using unsupervised learning approach. However, the continuous evaluation of network traffic on multi-dimensional data is a difficult task in real time data traffic. In order to overcome the problem in traffic analysis, An Integrated K-means Unsupervised Clustering and Supervised C4.5 Classification (KUC-SC) technique is introduced. An integrated technique is used to evaluate the network traffic conditions to classify the patterns of real time and non-real time traffic. An integrated KUC-SC technique performs two types of processing steps such as clustering and classification. At first, K means unsupervised learning algorithms is applied in KUC-SC technique to form a k number of clusters using the different input data point with the nearest mean. The clustering approach is used for classifying the given data. After that, C4.5 is used to classify the data whether it is real time or non-real time traffic through the construction of decision-tree. At every node of the tree, C4.5 algorithm classifies the data point that most efficiently divides the set of samples into subsets with similar characteristics. This in turn improves the classification accuracy in network traffic data analysis. An experimental result shows that the proposed KUC-SC technique obtains the better performance in terms of classification accuracy, classification time, true positive rate and communication overhead compared to the state-of-the-art works.
Keywords: K-means Unsupervised Clustering; Supervised C4.5 Classification; real time and non-real time traffic analysis.
Storage and Query Over Encrypted Sensitive Association Rules in Database
by Meenakshi Bansal, Dinesh Grover, Dhiraj Sharma
Abstract: In this modern era of increasing need of data sharing along with data security, processing over encrypted data is highly desirable. Data encryption is the most common technique used for maintaining the data privacy. However,when processing is required to be done on this encrypted data it becomes a critical task. In most of the existing theories data need to be decrypted before it is being processed. Decryption before processing leads to serious threat to data. To avoid this, a new mechanism has been proposed and implemented which provides strong data protection. This mechanism helps to query over encrypted data without decrypting it. Experimental results show that the new technique outperforms the existing one in terms of query performance, computation time and storage memory.
Keywords: query; encryption; database; decryption; security; key mapping; elliptic curve cryptography; ECC; sensitivity; association rules; storage.
Analysing Thalamus and its Sub nuclei in MRI Brain image to distinguish Schizophrenia subjects using back propagation neural network
by ArivuSelvan K, Sathiya Moorthy E
Abstract: In this paper we presented precise and proficient techniques for measuring the human thalamus, medial dorsal and the pulvinar nucleus with magnetic resonance image (MRI). In spite of the fact that thalamic nuclei are not straightforwardly visible on traditional MRI image, it is conceivable to watch contrasts between the nuclei using Diffusion tensor imaging (DTI). We applied a novel and competent image pre-processing techniques to enhance the visual quality of MRI image. In addition to this we have used various segmentation algorithms to accurately extract entire thalamus from brain MRI images. Diffusion MRI is used to extract various nucleus of thalamus. Several optimal features such as textures, morphological are derived from thalamus and medial dorsal regions which are then used to train the artificial neural network model (ANN). Our artificial neural network model accurately classifies between schizophrenic and healthy subjects based on thalamic anatomy for larger sample sizes.
Keywords: Thalamus; Artificial neural networks; MRI; Schizophrenia; GLCM;Sobel; Level set; Fast marching.
A Public Key based Encryption and Signature Verification Model for Secured Image Transmission in Network
by JAYANTHI RAMASAMY, Johnsingh Kumaresan
Abstract: Image transmission is a challenging task due to the large file size and the security issues. Medical images are communicated through internet from the laboratories to the hospitals for analysis and treatment. The performance of such transmission is affected based on the size of the image files which are transmitted. Hence, it is necessary to compress the medical images while sending and to decompress them at the receiver side. Moreover, the sensitiveness of medical applications needs to apply only lossless compression techniques so that the diagnosis will be accurate. The security issues pertaining to medical images including privacy and prevention of attacks can be tackled by performing user authentication and encryption of medical images. Most of the existing systems on medical image transmission use symmetric key cryptography where the key is shared to multiple recipients leading to security leakage. This has been addressed by applying public key encryption schemes. However, a secured communication technique which uses compression, user authentication and encryption / decryption is more powerful than the existing systems on medical image transmission. Therefore, a new secured transmission algorithm which performs compression using Singular Value Decomposition (SVD), user authentication using digital signatures and encryption/decryption using AES and RSA algorithms for image encryption and Hill-Cipher for data encryption which are coordinated using intelligent agents for communication and rules for decision making is proposed in this paper. The major advantages of the proposed model include fast transmission, increase in security and non-loss compression and decompression and intelligent decision making on medical image transmission.
Keywords: Compression; Security; Encryption; Decryption; Image Transmission; Intelligent Agents; Authentication; Publickey Cryptography;.
ERAC-MAC Efficient Revocable Access Control for Multi-Authority Cloud storage system
by Sudha Senthilkumar, Madhu Viswanatham
Abstract: Abstract: In the recent scenario, there is an appreciable escalation in the utilization of cloud computing by critical industrial applications due to its cost-effective storage and computing. However, due to an unreliable server in a cloud, the access control turns out to be the challenging issue to ensure the confidentiality of sensitive data. The Ciphertext Policy Attribute Based Encryption (CP-ABE) is deliberated to be an apt technique to enforce the access control for encrypted cloud outsourced data. But, due to the computation complexity of decryption, user revocation and complexity of key management for achieving granularity, prevailing CP-ABE schemes when applied directly to multi authority attribute system, incurs more computational costs in the order of NP. In this paper, an efficient CP-ABE based multi authority attribute system is put forth that supports decryption and user revocation by CSP with the advent of a blind encryption/decryption technique and a novel coloring scheme for predicting user behavior analysis. Security and Performance of ERAC-MAC was analyzed and found to be much better than the other prevailing schemes. The implementation was done using the paring based cryptography library of the Stanford University in Ubuntu environment.
Keywords: Ciphertext Policy Attribute Based Encryption; Attribute Revocation; Attribute Authority; Multi Attribute Authority.
A Novel Data Aware Task Clustering Mechanism for Scientific Workflow Applications in Cloud
by Soma Prathibha, B. Latha, G. Sumathi
Abstract: Scientific applications modeled as Directed Acyclic Graphs (DAG) are composed of complex calculations and a large amount of data transfer. It is very difficult to execute these applications in traditional distributed computing platforms. For such applications cloud provides a reliable solution due to its unique characteristics such as availability of heterogeneous resources, on-demand provisioning, pay-per-use. For effective provisioning of resources and to improve the performance, task clustering is performed which combines two or more tasks into a single executable unit. Task clustering can help to reduce the system overheads such as queue delay, engine delay and so on. Existing clustering algorithms in this domain focus more on computational granularity of the tasks without considering the data dependency among the tasks. In this paper, a data aware clustering algorithm has been proposed which combines the tasks depending on the size of data transferred between interdependent tasks. Experiments were conducted to compare the proposed clustering algorithm with the existing baseline and balanced clustering algorithms and it was observed that proposed algorithm gave better makespan and cost for data intensive workflow applications.
Keywords: Directed Acyclic Graphs(DAG); Cloud Computing; Task clustering; Billing model; Scheduling.
REVIEW OF CYBER ATTACKS CLASSIFICATIONS AND THREATS ANALYSIS IN CYBER-PHYSICAL SYSTEMS
by MOHAMMED NASSER AL-MHIQANI, Rabiah Ahmad, Zaheera Zainal Abidin, Nabeel Salih Ali, Karrar Hameed Abdulkareem
Abstract: Cyber-physical systems (CPSs) have been widely used in many different critical areas like smart grid, healthcare, aircraft and etc. and they played a significant role in our daily lives. However, the CPS systems currently are one of the critical hackers targets that have a lot of incidents due of the high impacts of these systems. Several works have been conducted in CPS, but still, there is a lack of theories and tools that organisations and researchers can use to understand the nature of the new threats and the impacts of each danger. This article provides description of CPSs usage areas and security challenges in some of the critical CPS fields. Likewise, discusses frameworks and taxonomies that have been used for classifying cyber-attacks or incidents. As well, study and analyse threats that have been stated in the previous studies and research to understand the current status of the risks on CPS.
Keywords: cyber-physical systems; CPSs; cyber-attacks; CPS security challenges; incidents; threats analysis.
Comparison of Security Related Methods in Open Interconnect Consortium (OIC) and one Machine-to-Machine (oneM2M) for Security Interoperability
by Dong In Kim, Hyungu Lee, Jaehwan Lee, Goutham Reddy Alavalapati, Ji Sun Shin
Abstract: The services and applications of the Internet of Things (IoT) support various areas such as smart home, smart car and so on. Recently, several groups of companies have been trying to integrate and standardize the IoT platforms and two major platforms among them are one Machine-to-Machine (oneM2M) and the Open Interconnect Consortium (OIC). oneM2M and OIC standards enable connected devices to communicate each other regardless of their manufacturers and operating systems. Also, there are attempts to interwork two open platforms. However, security interoperability has been less focused in the interworking. In this paper, we present the comparison of security related methods between oneM2M and OIC standard specifications for the security interoperability. We compare the basic concepts and terminology of two standards by clarifying the same-spelled basic terms with different meanings and different terms having similar concepts.
Keywords: Open Interconnect Consortium (OIC); Security; Internet of Things (IoT); Interworking.
Efficient user authentication, server allocation and secure data storage in cloud
by MANOJ TYAGI, Manish Manoria, Bharat Mishra
Abstract: With the increased popularity and the need to store the sensitive data over the cloud, the security of stored data is a very big challenge. This work has not only investigated and rectified the susceptibility of the data in the cloud, but also proposed the technique for user authentication and server allocation efficiently. Cuckoo Search Algorithm (CSA) is applied for efficiently allocating the server. For maintaining the privacy of clients data over the cloud, Improved Attribute Based Encryption (IABE) can effectively do attribute adjunction/revocation, reduce the time complexity as well as safeguard from collusion attacks. The minimum set generation, proxy key generation, file updation, and secret key updation, is modified for efficient attribute revocation/adjunction. The combination of IABE with CSA achieves the security objective and the process is enhanced efficiently.
Keywords: Cloud computing; Encryption; Decryption; Secure data Transmission; Secure storage; Attribute revocation; Server allocation; Cuckoo search algorithm; IABE.
A Secure and Robust Smart Card Based Remote User Authentication Scheme
by Katayon Dowlatshah, Mojtaba Alizadeh, Mehrdad Ahmadzadeh Raji
Abstract: In new era of technology, smart cards play a critical role in economic and social interactions. Security vulnerabilities of these smart is a main concern for users and tech experts. Authentication as one of the basic security solution is used to protect the data from unauthorized access. In recent years, research on smart card-based password authentication get more attention. This paper, reviews different smart card authentication methods and proposes an improvement of the Yassin et al.  scheme to cover its security weaknesses like session key attack vulnerability. Finally, the proposed method is analyzed and compared to the related works.
Keywords: Smart card; authentication; impersonation; server; session key.
An exhaustive study of DDOS attacks and DDOS Datasets
by Joshua Nehinbe, Solomon Onyeabor
Abstract: Conceptually, frequent Distributed Denial of Service (DDOS) attacks on corporate networks are serious challenges that are recently demanding urgent explanations. The attacks destroy and deny users from accessing computer and mobile networks. Consequently, several organizations have lost long-standing reputations they have built over time. Some firms have also incurred huge financial resources, reduction in the numbers of customers and annual patronage within a short space of the attacks. Unfortunately, experiences learned by victims are not often made public. Besides, the strengths and weaknesses of the available DDOS traces are not recently discussed in contemporary literatures. Therefore, feelers have begun to question and ponder about the resolute and validity of the existing models despite the fact that they were duly evaluated with some standard DDOS datasets. Thus, this paper discusses the rudiments of DDOS attacks and elaborately explicates some of the challenges associated with DDOS datasets. We use C++ programming language to empirically demonstrate potential datasets that researchers can adopt to investigate DDOS attacks. The results suggest that researchers can secure informative DDOS datasets by merging different DDOS datasets together. Finally, the review will be helpful to investigators, analysts, data donors and litigators in the determination and enforcement of legal rights against intruders.
Keywords: Distributed Denial of Service (DDOS); Intrusion; Intrusion Detection System (IDS)Intrusion Prevention System (IPS); datasets.
Performance evaluation for the hash generation phase of a democratic blockchain
by Luis Lugo, Cesar Pedraza
Abstract: Distributed ledger technologies (DLTs) have the potential to transform different areas of research and industry. Initially created to support a peer-to-peer electronic cash system more commonly known as bitcoin blockchains provide a decentralised transactions and data management technology. This decentralised technology is secure, anonymous, and transparent. Nonetheless, the blockchain protocol has a number of technical weaknesses such as power computing dependency and high power consumption. An alternative protocol proposes a democratic approach in which the computing power is not a determinant factor in user participation. The democratic protocol has three phases: hash generation, hash broadcast, and hash validation. We evaluate the performance of the hash generation phase on CPU, GPU, and cluster platforms. A considerable speedup is achieved with GPUs when using final nonce values similar to a real blockchain application. Also, it provides the best power efficiency.
Keywords: distributed ledger technology; DLT; hash generation; parallel computing.
Key-Dependent Permutation Layer Based on Two Dimensional Discretized Chaotic Maps for Lightweight Block Ciphers
by Hue Ta Thi Kim
Abstract: This paper proposes a design of a key-dependent permutation layer. Based on the comparison of the properties for different two-dimensional discretized chaotic maps, the Standard map is chosen with good diffusion properties to construct a chaos-based permutation layer in block ciphers. The proposed construction has both high security strength and hardware efficiency. The result of the hardware implementation shows that it is suitable for lightweight block ciphers due to its low resource utilization.
Keywords: Chaos-based cryptography; Lightweight block cipher; Key-dependent; Permutation layer.
Dynamic Software Management practices using Genetically Augmented Neural Networks
by Jasem M. Alostad
Abstract: The aim of research is to investigate real time projects and the utilisation of Ppc software packages for educational institutions. The main motivation behind the study is to resolve the scheduling problem in Ppc software projects, which is considered useful for students curriculum. The scheduling problem is been a prominent issue in software projects, which creates a trade-off between project duration and skills. Hence, this work removes the trade-off between project duration and skills using genetic augmented neural network algorithm (GANN). The process runs in a structured
way to obtain a better solution in software project duration over education sector. The result with GANN over software validation seems flexible and accurate and can work as a flexible tool for automated software management practices.
Keywords: project scheduling; educational sector; genetic neural network algorithm; software project management.
A Hybrid Model Collaborative movie Recommendation System using K-means clustering with Ant Colony Optimization
by Sandeep Kumar M, Prabhu J
Abstract: Movie recommendation system offers a mechanism to allocate the user to attain the famous film by getting an opinion from similar users or past rating by user. This produces recommender systems has a crucial part of website and e-commerce application. The primary objective of the system to prefer a recommender system by data clustering and computational intelligence. We proposed a hybrid model collaborative movie recommendation system that performs with a combination of K- means clustering with Ant colony optimization technique (ACO-KM) that has employed in Movie dataset. The proposed system compared with existing works, and its efforts have been analyzed. The evaluation process of movie recommendation system that offers improved result from ACO-KM collaborative movie recommender system based on precision, recall, Mean square error(MSE), and accuracy. By Comparison of speed (in seconds) of various approaches in Movielens dataset, our approach gives best result 42.24 compared with existing one 53.22. The Outcome of this experiment from Movielens dataset that offers scalability and efficiency in a recommendation by decreasing cold start issues.
Keywords: Recommender system; nearest neighbor; Ant colony optimization; cluster; K-means; Collaborative filtering.
A Multi-Controller Placement Strategy in Software Defined Networks using Affinity Propagation
by Sminesh C. N., Grace Mary Kanaga E., Sreejish A. G.
Abstract: In software defined networking (SDN) when the number of network elements and traffic flows escalate, a single controller cannot efficiently handle the growing load, resulting in the deployment of multiple controllers. To develop computationally less intensive solutions for the controller placement problem in WAN, clustering-based network partitioning algorithms can be employed. The proposed method partitions the network using a modified-AP clustering algorithm which automatically computes the number of clusters and uses the candidate exemplars identified for the placement of SDN controllers. The similarity measure that considers both the Euclidean distance and link bandwidth is the input to the proposed algorithm, and the simulation is carried out using standard network topologies from the Internet Topology Zoo. The simulation results show that the observed number and location of SDN controllers minimises average case, worst-case, inter-controller latency and improved controller imbalance factors which ensure the optimal number and placement of SDN controllers.
Keywords: software defined networking; SDN; controller placement; traffic engineering; affinity propagation.
An Efficient Smart Card Implementation of the AES Algorithm Robust against Differential Side Channel Analysis
by Massoud Masoumi
Abstract: This paper presents a novel and efficient implementation the masked AES (Advanced Encryption Standard) rnS-Box on smart card. The proposed scheme has advantages of easy software implementation and lower memory requirement compared to conventional existing implementations. The experimental results and also the results of Welchs T-Test statistical analysis demonstrate that the proposed scheme has less information leakage than the conventional first-order masking routines. The target device for evaluating the efficiency of the proposed countermeasure is the smart card of Side Channel Attack Standard Evaluation Board (SASEBO). However, the proposed implementation can be used for other typical platforms, as wellrn
Keywords: AES Algorithm; Power Analysis Attack; Electromagnetic Analysis Attack; Smart Card Implementation.
Efficient Parking Control Algorithms for Self-Driving Cars
by Shahroz Tariq, Hyunsoo Choi, Heemin Park, Jae Won Lee
Abstract: We explored the problems which will soon arise while parking in car parks. These include structure of parking lot suitable for autonomous cars, finding the closest parking slot available, and navigation to the location. In this paper, we explored the problems which are soon to be faced while parking autonomous cars in parking lots. We provide two solutions for two different kind of parking method which are named as guided conventional parking & guided blocked parking. Both methods use a central server and the graph of the parking lot to guide the cars to the closest parking slots. With experiments, we have shown that our proposed methods should be effective for the guided parking for self-driving cars. We also created simulations to visually analyse the changes made in the parking Lot at each step of the process. We compared both solutions and discussed their trade-offs in detail.
Keywords: Self-Driving Cars; Autonomous vehicle; Intelligent Parking.
Bitcoin Price Prediction using ARIMA Model
by Naveen Chilamkurti
Abstract: Bitcoin is a highly volatile cryptocurrency with rising popularity. Itrnis a turning point in the way currency is seen. Now the currency, rather thanrnbeing physical is becoming more and more digital. Bitcoin has removed therncentral party and has given the control to the users. Due to high variance of solornmining, the number of users joining top most famous Bitcoin mining pools arernincreasing due to the fact that users together under a Bitcoin pool will have arnhigher chance of generating next block in the Bitcoins blockchain by reducingrnthe variance and earning the mining reward. Furthermore,emerging mining farmsrnwith strong mining resources and fast processing power is another trend towardsrncentralization. This trend clearly illustrates that the pure, decentralized protocolrnof Bitcoin is going towards centralization in its distribution network, where anyrnkind of centralization should be considered carefully due to the 51% attack. Therncentralization caused by bitcoin cloud wallets giving new users easy access to thernbitcoin network should not be ignored either. This might cause various securityrnissues in the bitcoin technologies due to the hackable applications and websites.rnThere has been much research on the bitcoin
Keywords: Cryptocurrency; Predictive model; ARIMA; Block chain; Bit Coin.
A Brief Review of Blockchain-based DNS Systems
by Saif Al-Mashhadi, Selvakumar Manickam
Abstract: One of the crucial parts of the Internet is the Domain Name System, which works as a phonebook of the Internet. The protocol is designed to be fast, reliable, and not shielded with a security mechanism, DNSSEC which adds authentication later. However, threats utilising DNS such as DoS/DDoS are increasing daily. On the other hand, blockchain-based DNS is secure by design. By reviewing and comparing it with the current DNS and its ecosystem, it is concluded that blockchain currently has challenges that need to be addressed before it can be adapted as a replacement for the existing DNS.
Keywords: DNS; Blockchain; Namecoin; ENS;.
Wide Band Time Optimal Spectrum Sensing
by Rama Murthy Garimella, Rhishi Pratap Singh, Naveen Chilamkurti
Abstract: Conventional methods for spectrum sensing do not consider historical traffic data. Equal amount of time is allocated to each channel of interest for sensing. In this research paper, we formulate the time optimization problem for spectrum sensing keeping historical traffic data into account. We have solved the problem for interesting constraints. For the solution of these constraints stochastic programming formulation has been done. The problem is also formulated as a quadratic/hybrid programming problem where the variance of discrete random variable constitutes a quadratic form associated with a laplacian like matrix. Using this result, time optimal spectrum sensing is formulated as a multi-linear objective function optimization problem.
Keywords: Spectrum Sensing; Pareto Front; Integer Programming; Source Coding; Stochastic Optimization.
Android Application Security: Detecting Android Malware and Evaluating Anti-Malware Software
by Sangeeta Rani
Abstract: Mobile devices have become widespread computing technology, people prefer to use than desktop devices. These connected devices and their features like an exchange of data, video calling etc have made our lives simple but, this has also increased data security concerns. At present, Googles Android is dominating the market share of mobile devices; as a result, it has become a big target for malware writers. Android users can download applications from official or third-party stores. Google implements various security policies to ensure secure distribution of applications but third-party application stores have less efficient or no such policies. This makes such markets more attractive for malware writers. This paper investigates Android application security by analysing 1946 free most downloaded Android applications in the year 2016: 1300 from Google Play store and 646 from third-party Android applications. 100 samples from 33 different malware families (with variants) prominent in the Android market during January 2016 to April 2016 were also collected that acted as a template for malware detection. Further, based on detected malware samples, an evaluation-based study on ten anti-malware applications is performed to identify how well they protect users from malware.
Keywords: Android; Malware; Applications; Permission; Anti-malware software.
ADFT: An Adaptive, Distributed, Fault-Tolerant Routing Algorithm for 3D Mesh-Based Networks-on-Chip
by Zahra Mogharrabi-Rad, Elham Yaghoubi
Abstract: Nowadays, Three Dimensional Network-on-Chips (3D-NoCs) have been introduced as the most efficient communication architectures. In these architectures, it is likely to encounter some faults. Therefore, an important goal in designing such architectures is to make them more tolerable against faults. In this paper, an adaptive, distributed and fault-tolerant routing algorithm called ADFT is proposed for 3D Mesh-based NoC that is able to tolerate permanent faults and does not require any additional circuit for fault-tolerant. In order to evaluate the performance of the ADFT, we compared it with LOFT in terms of latency and throughput. Simulation results show improvement of the proposed method.
Keywords: Three-dimensional Network-on-Chip; Routing algorithm; Permanent fault; Fault-tolerant.
Efficient & Secured Information Transfer for Congestion Avoidance and Collision detection in Vehicular Ad Hoc Networks (V2V) Methodology
by Jaya Subalakshmi Ramamoorthi, Arun Kumar Sangaiah
Abstract: Vehicular Ad Hoc Networks (VANET) connects the vehicle and the infrastructure in solving various critical issues on the travel path. This includes Collision of vehicles, congestion due to various circumstances and blockage of vehicles. In order to avoid these critical issues, an effective vehicle to vehicle communication and vehicle to infrastructure communication have been established in the vehicular ad hoc networks, in which the vehicle reports the infrastructure in case of collision or congestion. In this paper, the multi-hop routing protocol is established to transfer the information among vehicles when the infrastructure is out of range from the source. This increases the efficiency and reduces the cost of dependent on more number of establishing infrastructure mechanism at various points. In addition, the key concern that needs to be addressed in the V2V methodology is the success rate of message transfer among the vehicles. For that purpose, we have proposed and designed a payload format with the essential parameters such as position status, speed, heading, vehicle identification number etc. This gives the complete information about the vehicle thereby updating the database for future reference. The effectiveness of the proposed payload format with the multi hop routing strategy is validated through the simulations using NS-2 simulator in coordination with SUMO.
Keywords: Vehicle to Vehicle; Communication; Multi-Hop; payload; Congestion; Collision detection; NS-2.
New Hybrid Framework to detect phishing web pages, based on rules and variant selection of features
by Youness Mourtaji, Mohammed Bouhorma, Daniyal Alghazzawi
Abstract: Phishing phenomena are increasing day after day due to its simplicity to use; it is enough for hackers to clone legitimate website and send it by e-mail to victims to access it within the use of social engineering techniques to lure them and gain their confidence. Hackers use the lack of knowledge of regular users when surfing on the internet and understanding the role of URL (Uniform Resource Locator) of a web page. This fact let hackers create malicious forms of URLs, like very long ones or containing some suspicious characters. Despite cloning webpages, hackers can inject malicious codes into this web page for nefarious uses, so detecting or preventing this kind of web pages is the objective of this paper. We present a new hybrid framework to identify phishing web pages based on different ways and methodologies for features extraction techniques using only the URL as the main entry, and without having any visual experience before, also we use hybrid analysis to be complete and accurate.
Keywords: Malicious Web Page; Phishing; Hybrid Analysis; Machine Learning; Network Security Intelligence.
Parallel Visible Light Communication System Using Video Camera and LED for Communicating and Indoor Positioning
by Sadeq Moradzadeh, Gholamreza Abaeiani, Amir Hooshang Mazinan, Mojtaba Alizadeh
Abstract: Visible Light Communication (VLC) is a desirable indoor communication system to transfer data using visible light. In this paper, visible light communication system using LED and video camera is considered as a transmitter and receiver for communicating and positioning, respectively. A new method is proposed for indoor positioning by the help of statistics and image processing tools. Furthermore, the proposed method is utilized based on neural networks and in variant moments in order to find and follow new location of the transmitter. The effectiveness of the proposed method is evaluated and proved by conducting experiments.
Keywords: visible light communication; neural networks; invariant moments; indoor positioning; LED.
Special Issue on: ICGHIT 2018 Advances in Green and Human Information Technology
Effective feature selection based on MANOVA
by Trong-Kha Nguyen, Vu Duc Ly, Seong Oun Hwang
Abstract: Effectiveness in classifying malware is a critical issue which can overheat a classifier or reduce performance in real-time malware detection systems. However, the effectiveness in feature selection stage was not studied so far. As effectiveness should be taken into account at the earliest possible stages, in this paper, we focus on the effectiveness of feature selection. Firstly, we perform an analysis on instruction levels which consists of most frequencies mnemonics. Secondly, we propose new methods to select effective features by MANOVA statistical tests. Furthermore, we use those selected features fed to a classifier. Our approach reduces significantly the number of features from 390 to 4, which explains 99.4% variation of the data. With the selected features, we classify malware samples and have achieved 96.2% of accuracy and 0.6% of false positive.
Keywords: malware classification; statistical analysis; security.
Optimizing Group Key Management for Frequent-Membership-Change Environment in VANET
by Baasantsetseg Bold, Young-Hoon Park
Abstract: In a Vehicular Ad hoc Network (VANET), secure and efficient communication is one of the most important concerns. For both security and efficiency, multicast applications have been widely applied to the VANETs. To realize the multicast, a group key (GK), which is shared only by members of a special group, is used for encrypting and decrypting transmitted data. To reduce the communication cost for rekeying, many researchers have proposed group key management (GKM) schemes. Among the various GKM schemes, tree-based management schemes have received considerable attentions because of they reduce the communication cost for rekeying compared with other GKM schemes. However, the existing tree-based approaches are not suitable for the VANET environment because of an other efficiency related problem can be occurred due to high vehicular mobility.rnIn this paper, to overcome the limitation for the VANET environment, we propose a tree-based optimal group key management algorithm using a batch rekeying technique, which changes a set of keys at a certain time interval. In batch rekeying, the server collects all the joining and leaving requests during the time interval, and generates rekeying messages. In the proposed algorithm, when a vehicle joins a group, it is located at a certain leaf node of the key-tree according to its leaving time. This reduces the size of the rekeying messages because the vehicles that leave during the same time interval are concentrated on the same parent node. Using simulations, we demonstrate that our scheme remarkably reduces the communication cost for updating the group key.
Keywords: Vehicular Ad Hoc Network; Batch Rekeying; Group Key Management; Key-tree.
Key-factors of the Constrained Management for the Internet of Underwater Things
by Khamdamboy Urunov, Soo-Young Shin, Jung-Il Namgung, Soo-Hyun Park
Abstract: Indeed, we provide a comprehensive research of management system in the internet of underwater Things (IoUT) specification. The main contributions act as lightweight management mechanism based on underwater acoustic network. Several compression algorithms and implementation steps are a solutions of the underwater SNMP (uSNMP) integrations to the embedded system via u-MIB.
Keywords: underwater-network management system; U-NMS; network management system; NMS; internet of underwater things; IoUT; underwater-management information base; u-MIB; underwater-simple network management protocol; u-SNMP; management information base; MIB; simple
network management protocol; SNMP; lightweight mechanism; cognitive algorithm.
Special Issue on: NISS2018 Mobile Networks and Information Systems Security
Assessing node mobility impact on routing performances in MANETs
by Younes Ben Chigra, Abderrahim GHADI, Mohamed BOUHORMA
Abstract: Mobile Ad Hoc Network plays a major role in enabling data communication in infrastructures-less areas. Network performance relies on the capacity of mobile nodes to process data packet and deliver it to the right destination at lower cost. Since nodes are free to move toward random destination, data delivery from a source to a destination might be disturbed due to frequent topology changes. Hence, mobility in mobile ad hoc network represents the main issue that should be addressed carefully while designing routing protocols. The purpose of this paper is to study the impact of node mobility on the performance of well-known routing protocols such as DSDV, DSR and AODV. We assessed the efficiency of each protocol under high mobility environment using various values of speed and pause. Performance assessment is based on the conventional metrics such as latency, Throughput, Packet delivery ratio and routing overhead. Moreover, we introduced two new metrics called path change factor (PCF) and route repair influence (RRI) for accuracy purpose. The study demonstrates that AODV has better performances in high mobility environment.
Keywords: Mobile Ad Hoc Networks (MANET); Routing protocols; Mobility; QoS; NS2; Metrics.
A Scatter Search Algorithm to configure Service Function Chaining
by Adel Bouridah, Hacene BELHADEF
Abstract: Network virtualization Functions (NFV) emerges to deal with the challenges of reducing both the capital expenses (CAPEX) and operational expenses (OPEX) of Cloud providers. The NFV is done by implementing network functions and providing them as software commodities. Hence, the required network services are provided by chaining a set of network functions together while considering the network resource limitations and the functions requirements. The key problem will be how and where network functions should be placed in the network and how traffic is routed through them. This is well-known as the service function chaining problem and its aim is to deliver an efficient placement with the appropriate routing that increases system capacity while also minimizing the delay seen by flows. Whereas, the service function chaining is known to be NP-Hard and exhaustive search algorithms have no significant benefit in large-scale context. This paper proposes a scatter search based solution to configure service function chain. The aim is to produce the optimal number and placement of the required virtual network functions with the dynamic traffic steering over them. this is done while respecting computing and network resources constraints.
Keywords: Network Function Virtualization; NFV; Service Function Chaining; Scatter Search.
Enhancing Multipath Routing using an Efficient Multicriteria Sorting Technique
by Layla AZIZ, Said RAGHAY, Hanane AZNAOUI
Abstract: Saving energy in MANET represents a critical issue due to its material architecture. This paper aims to improve Ad-hoc On-demand Multi path Distance Vector Routing (AOMDV) using multicriteria analysis method. In order to construct effective disjoints paths, our approach focuses on sorting the different available paths considering several important criteria. Our objective is improving the network performances and routing stability using an efficient multicriteria method called Electre Tri. The use of this technique enhances data reach ability and minimizes the required delay during packets forwarding. The proposed approach is evaluated comparatively to well known routing protocols considering various metrics. Simulation results prove that our approach improves significantly the network lifetime and the transmission delay.
Keywords: AOMDV; MANET; Multicriteria analysis; Multipath Routing ;Electre Tri.
Towards an agent based framework for urban traffic congestion management
by Sara Berrouk, ABDELAZIZ EL FAZZIKI, Zakaria Boucetta
Abstract: This paper introduces an integrated solution to the road congestion problem by modelling the road network, using real-time traffic data and drivers parameters to compute the proposed congestion index for each segment in the road network and finally generating recommendations to road users to avoid the most congested trajectories. Combining the benefits of the multi-agent system, traffic data from the available sources and big data tools, it aims to reduce and optimize the traffic flow in urban areas. The computed congestion indexes are used in the road network generation which is represented by a weighted graph. The edges costs are computed based on the congestion indexes and the edges properties and vary when new traffic records are retrieved. In this work, the Hadoop framework is used in the data gathering and analysis. It allows the proposed framework to reach a higher level of performance. The choice of the least congested pathfinding algorithm fell on an improved version of Dijkstra over Hadoop MapReduce.
Keywords: Road network modelling; Traffic congestion; Dijkstra; Hadoop MapReduce; Multi-agent systems.
Special Issue on: Machine Learning Algorithms for the Era of Integrated Internet of Things and Mobile Edge Computing
Hybrid Machine Learning model for Healthcare Monitoring Systems
by Nallakaruppan Kailasanathan, Senthilkumaran Ulaganathan
Abstract: Health Monitoring is very important in todays world because of the rise in the number of health problems all around the world. The increasing stressful life is taking a toll on everybodys health. The life time of the human being is challenged by environmental conditions, life style and also commercialization of things. The human life is facing a daunting task to handle the physical ailments. The late diagnosis of many diseases lead to serious complications on the human health. The lack of medical awareness is the primary cause of the lack of diagnosis and treatment which allows the disease grow easily. The prescribe work provides a solution to physically challenged or elderly people through web-based remote health monitoring facilities. The system collects the data, classifies them, apply the machine learning algorithms to ensure the data integrity. The reports are then generated and supplied to doctors for further examination of the patient record for taking medical decisions. We form a hybrid cluster of machine learning algorithms to ensure the increased accuracy and reduced error rate on the patient data measurement.
Keywords: SVM (Support Vector Machine),BAN(Body Area Network),IoT(Internet of Things),GSM(Global System for Mobile Communication),WSN(Wireless Sensor Networks),GPRS(General Packet Radio Services.
An early prevention method (EPM) for Node Failure in Wireless Sensor Networks
by Siva Rama Krishnan Somayaji, Arunkumar Thangavelu
Abstract: Wireless sensor networks are used to monitor physical or environmental conditions such as temperature and pressure as well as to study the quality of certain environmental and natural entities like air and water bodies by collecting data about the various components present in the air/ water at a given spot and time. This data is further used to determine the purity and in turn the advised usage of the water, and the time and extent of the necessary measures to be undertaken for protection and safety. Sensor nodes play a pivotal role in this process of data collection, and any fault in the nodes can damage the quality of service. As such, node failure is a critical issue which affects the accuracy of the entire network and may change the net result. Also, the data being transmitted on the network to the sink consumes a lot of energy and thus reduces the life span of the nodes and the network. But the complete data generated by the nodes in each iteration is not always useful, as most of them give the redundant information or details which doesnt provide any essential information, just bulge up the amount of data being transmitted. Therefore, this paper aims to formulate an early prevention method (EPM) which not only gives a way to detect failed nodes, but also increases the overall efficiency of the network by reducing the overhead at the sink. This paper aims to provide a method to detect failed nodes in Wireless Sensor Networks (WSNs) using Recommendation Routing Algorithm. It is also proposed to increase the overall efficiency of the Wireless Sensor Network by decreasing the overhead, and only the data relevant for monitoring the environment shall be transmitted over the network to the sink. This will be achieved through data aggregation and bucketing. As a result, this method can be used to detect changes in the environment with all relevant information being obtained by transfer of the least possible amount of data, along with an effective mechanism to detect the failure of nodes if need be.
Keywords: Node failure; network efficiency; recommendation routing.
Performance Evaluation of ICI Self Cancellation Schemes in Fractional Wavelet Based OFDM System
by Ayeswarya Ramalingam, Amutha Prabha Nagarajan
Abstract: The widely used multi carrier modulation technique in the current wireless scenario is orthogonal frequency division multiplexing (OFDM). It is severely affected by frequency offsets and leads to inter carrier interference (ICI). Fractional wavelet transforms (FrWT) based OFDM model is proposed to mitigate the effect of ICI by its orthogonal fractional wavelets. The performance of FrWT involved OFDM model is comparatively analyzed through various mapping schemes of ICI self cancellation in order to mitigate frequency offsets. This system overcomes the drawback of ICI self cancellation and shows improved bandwidth efficiency without any cyclic prefix. Simulation results are carried out for the proposed model and compared with exiting Fourier transform and wavelet transform based OFDM system. The analysis is performed for a range of normalized frequency offsets values. The proposed model performance is tested with various mapping schemes against a frequency offset of 0.05. It is proved that FrWT based OFDM with ICI self cancellation gives reduced bit error rate (BER) of 10-5 at 8 dB signal to noise ratio (SNR). From various graphs, it states that weighted conjugated transformation results in effective mitigation of ICI with BER achievement of 10-4.4 at frequency offset of 0.1.
Keywords: BER; Frequency offsets; FFT; FrWT; ICI self cancellation; OFDM.
Prevention of Rushing Attack in MANET using Threshold Based Approach
by Sankaranarayanan S, Murugaboopathi G
Abstract: Mobile Ad-Hoc Networks is a collection of mobile nodes that works without the centralized infrastructure. Every mobile node not only acts as host, it also works as router to forward packets that are received from neighbor nodes. Mobile Ad-Hoc networks are useful in military environments, automated battlefields, emergency, rescue operations, disaster recovery, educational, home and entertainment applications. Here data must be routed via intermediate nodes. Rushing attack is one of the network layer attacks in MANET. In this attack, when the attacker node receives the route request packet, it immediately forwards the route request packet to its neighbors without processing the packet. Threshold based approach is used to detect rushing attack in MANET. Proposed method provides better packet delivery ratio and throughput in presence of rushing attacker. Simulation results show that our Modified DSR protocol performs better compared to Secure DSR algorithm.
Keywords: Rushing Attack; MANET; Modified Dynamic Source Routing (MDSR).
A Queuing Theory Model for E-Health Cloud Applications
by Sathish Kumar, Iyappa Raja M
Abstract: A healthcare application plays a significant role in peoples healthier life in recent times. Cloud computing is an ease technology that provides resources to users on demand. In high-performance computing, cloud has been outgrew technology for providing its services to e-health applications by pay-as-you-go model. Workload management for e-health applications in the cloud is one of the major areas to focus on to achieve availability in e-health cloud. To manage the workload, elasticity is the key characteristics where the workloads are scaled up and down on demand. This can be achieved by effectively allocating and de-allocating virtual machines (VM). Henceforth VM allocation and de-allocation are the major issues in e-health cloud. In this paper, a markovian based queueing model is presented to manage the elasticity of an e-health cloud. There are two conditions were analyzed in which virtual machine is failed and the virtual machine can be recovered and cannot be recovered. The proposed model helps to improve the virtual machine scaling without changing the type of machine.
Keywords: Health care; E-Health; Cloud; Queuing; Markov.
EC(DH)2: An Effective Secured Data Storage Mechanism for Cloud based IoT applications Using Elliptic Curve and Diffie-Hellman
by Balasubramanian Prabhu Kavin, Sannasi Ganapathy
Abstract: In the rapid growth of cloud computing with Internet of Things (IoT) technology utilization in this fast world, security is an essential issue in cloud data storage and access the encrypted data. In the past, several researchers have worked on data storage mechanisms and came up with different ideas to fulfill the cloud user's expectation. Even though, they have failed to provide the sufficient security on cloud. For providing better security over the cloud, we propose a new Elliptic Curve and Diffie-Hellman based data storage mechanism called EC(DH)2. Here, the standard Diffie-Hellman algorithm is used twice as DH2 and it is combined with the standard Elliptic Curve Cryptographic algorithm for performing secured storage in cloud based IoT applications. The experiments have been conducted by using the various sizes of documents and files for evaluating the proposed EC(DH)2 algorithm and is proven that it provides better protection and increased key size.
Keywords: Elliptic Curve Cryptography (ECC); Diffie-Hellman (DH); Cloud Computing; Data Storage; Security; Internet of Things (IoT).
AUTOMATED INTELLIGENT PUBLIC LIGHTING SYSTEM
by Sudha Senthilkumar
Abstract: Intelligent street lighting refers to public street lighting that adapts to movement by pedestrians, cyclists and cars. This type of lighting is different from traditional, stationary illumination, or dimmable street lighting that dims at predetermined times. Intelligent street lighting, also referred to as adaptive street lighting, dims when no activity is detected, but brightens when movement is detected. Apart from that in this model of Intelligent Street Lighting the lights will be configured to interact with each other to inform further lights to turn on or off when activity is detected near them. This ensures that people always walk in well-lit areas but area where activity is not detected remains dark thus saving energy. The lights will also be fitted with sensors to detect available sunlight initially and only come into action when required instead of predetermined times, it will also have access to a web API that provides average sunrise and sunset times in the region to save energy by reducing usage of the sensor itself.
Keywords: GSM Model; Sensor Light ; IoT; Microcontroller.
Survey of methodologies for quantifying software reliability
by Ganesh Viswanathan, J. Prabhu
Abstract: An important problem that arises in the testing of software programs is that given piece of source code is reliable or not. Software reliability is an important segment of software quality. So software reliability must be quantified. Quantification refers to measurement. A number of software metrics and statistical reliability models have emerged during the past four decades but no model can solve the issue. In this paper, we conduct a survey on various software reliability metrics and models.
Keywords: software reliability; software reliability metrics; software reliability assessment; survey of software reliability quantification.
Special Issue on: Cloud Computing, Big Data and Data Science
A Scalable Fine-Grained Analytic Model for Container Cloud Data Centers
by Bingwei Liu, Yu Chen
Abstract: Cloud Computing is today's main-stream computing paradigm because of many attractive features. Although Cloud service providers have deployed numerous large scale Cloud data centers around the world, research in performance modeling for Cloud data centers are still in its infancy. A precise model of a Cloud data center can help the service providers improve their service quality, capacity planning, load balance and reduce operation costs. Most studies in literature focused on modeling hypervisor based cloud, typically IaaS. With the growing popularity of containers in Cloud service providers, there is a need to develop performance models specifically for these systems. A novel Cloud analytic model (CAM) for container-based Cloud data center was proposed. In the model, all schedulers in the Cloud logical hierarchy are modeling as unified Markov Chains with different model inputs. We identified the process at a scheduler as a Quasi-Birth-Death (QBD) process and provided algorithmic solutions using matrix-geometric analytic methods for infinite and finite cases. Physical machines (PMs) are modeled differently due to their underlying characteristics. CAM was able to capture the critical features in the Cloud. We utilized these interactive stochastic models to analyze the performance of the system in terms of mean job delay and probability of job rejection. Finally, a Container emulation framework, ConSim, was developed and tested against the analytic model. ConSim runs on actual container Cloud hardware and measures desired performance matric such as number of rejected jobs and delay of each job. Experimental development using real data were compared with theoretical calculation. The results showed promises in using the proposed analytic model to help service planning in container Clouds.
Keywords: Cloud Computing; container; virtualization; performance modeling; quasi-birth-death process.
DEADLINECREDIT AWARE HEURISTIC FOR DYNAMIC RESOURCE PROVISIONING IN A VIRTUALIZED CLOUD ENVIRONMENT
by Kamali Gupta, Vijay Katiyar
Abstract: Cloud computing as a paradigm has led to its adoption in large scale
parallel processing and distributed computing. The consumer's computational
needs serviced by the providers resulted in significant rise in its demand as latest
services can be accessed with different pricing models, value-added features
and instance types. Resource selection is a tedious task and places momentous
challenges of resource management before consumers and service providers.
As a remedy to this vanguard issue, brokers stipulates resource provisioning
options to ease the task of selecting the best resource that can match the
submitted requests by facilitating a standardized management interface across
Keeping in consideration this point, a Deadline-Aware Sufferage (DSufferage)
algorithm is proposed and implemented at platform level in this research work.
The algorithm is an improvisation in the existing sufferage heuristic. Deadline
parameter has been inculcated to assign precedence levels to the jobs to be
submitted to the machines apart from minimum completion time. The novelty of
the current research study is that the heuristic is centered towards both user and
providers goals in comparison to the existing batch-mode heuristics. The efficacy
of the algorithm has been verified using CloudSim tool and is concluded that
it is proficient enough to allocate resources to users tasks with in constraints of
deadline, resource utilization maximization and SLA violation avoidance.
Keywords: Cloud Computing; Resource Management; Scheduling; Heuristic; Makespan.
Financial Default Payment Predictions Using A Hybrid of Simulated Annealing Heuristics and Extreme Gradient Boosting Machines
by Bichen Zheng
Abstract: Online Peer-to-Peer (P2P) lending platforms face multiple challenges in today's e-commerce, but one of the most outstanding concerns evaluating loan risk based on borrowers' financial status and histories. Traditionally, financial experts assess borrowers' risk of default payments manually, but this process is tedious and time consuming, which are not widely applicable concerns for online P2P platforms. This paper proposes a hybrid of the Simulated Annealing and the Extreme Gradient Boosting Machine models in order to predict the likelihood of default payments based on users' finance histories. Based on the experimental results, the proposed model demonstrates its predictability with high recall scores. The proposed model not only out-performs over conventional algorithms including Logistic Regressions, Support Vector Machines, Random Forests, and Artificial Neural Networks, but it also provides an efficient method for optimizing hyper-parameters in the machine learning algorithms. Through the utilization of the proposed data-driven models, the necessity of tedious and potentially inaccurate human labor can be significantly reduced, and service level agreements (SLAs) can be further improved by time reduction made possible through the introduction of advanced data mining approaches.
Keywords: Big Data; Data Mining; Extreme Gradient Boosting Machines; Credit Risk; Credit Scoring; Simulated Annealing.
Agile Polymorphic Software-Defined Fog Computing Platform for Mobile Wireless Controllers and Sensors
by Haymanot Gebre-Amlak, Abdoh Jabbari, Yu Chen, Baek-Young Choi, Chin-Tser Huang, Sejun Song
Abstract: Softwarization approaches in networks, storage systems, and smart devices aim to optimize costs and processes and bring new infrastructure definitions and functional values. A recent integration of wireless and mobile cyber-physical systems, with dramatically growing smart sensors, enable new types of pervasive smart and mobile urban surveillance infrastructures that open up new opportunities for boosting the accuracy, efficiency, and productivity of uninterrupted target tracking and situational awareness.Wireless sensors provide the tool for communications and security applications. They offer low-power, multi-functioning and computational capabilities.
In this paper, we present a design and prototype of an efficient and effective fog system using light-weight agile software-defined control for mobile wireless nodes. Fog Computing or edge computing, a recently proposed extension and complement for cloud computing, enables computing at the network edge in a smart device without outsourcing jobs to a remote cloud. We investigate an effective softwarization approach in the Fog environment for dynamic big data driven, real-time urban surveillance tasks of uninterrupted target tracking. We address key technical challenges of node mobility to improve the system awareness. We built a preliminary proof-of-concept Light-weight controller architecture on both Android- and Linux-based smart devices and tested various collaborative scenarios among the mobile nodes.
Keywords: Software-Defined Network (SDN); Internet of Things (IoT); Fog Computing; Cloud Computing; Wireless Sensors; Network Softwarization.
Outlier Detection Techniques for Big Data Streams: Focus on Cyber Security
by Fatima-Zahra Benjelloun, Ayoub AIT LAHCEN, Samir Belfkih
Abstract: In recent years, detecting outliers in Big Data streams has become a main challenge in several domains (i.e., medical monitoring, government security, information security, natural disasters, and online ﬁnancial frauds). In fact, unlike regular static data, streams raise many issues like high multidimensionality, dynamic data distribution, unpredictable relationships, data sequences, uncertainty and transiency. Most of the proposed approaches can handle some of these issues but not all. In addition, they provide limited considerations with regard to scalability and performance. Real-world applications require high performance, resources optimization and real-time responsiveness when detecting outliers. This is useful to extract knowledge, detect incidents and predict patterns changes. In this paper, we review and compare recent studies in detecting outliers for streaming. We investigate how researchers improved the outcome of different models and monitoring systems, especially in the context of cyber security.
Keywords: Outlier Detection; Data Streams; Streaming; Big Data; High Dimension; Cyber Security.
Improving Cloud Computing Services Indexing based on BCloud-Tree with Users Preferences
by Ahmed Khalid Yassine SETTOUTI, Fedoua DIDI, Mohammed HADDAD
Abstract: Wireless Sensor Networks and Cloud Computing are different but complementary. In a hand, the wireless nodes are resources limited and battery constrained. In the other hand, Cloud computing is unlimited in terms of computing, storage, network and power resources. Integrating such different concepts results obviously some troubles; especially for WSN owners who want to pick up the most suitable Cloud Computing provider. In addition, we suppose that both of the clients (WSN owners) and services are heterogeneous, various and dissimilar. In this paper, we propose an indexation method of public IaaS virtual machines in an AVL-Tree. For that, we employ a Z-order function to arrange services in the structure and make the research more efficient. Experiments prove the performance superiority of the proposed approach in comparison with similar works in the literature.
Keywords: Cloud Computing; Service Selection; User Preference; Quality Measure; Public IaaS; Wireless Sensor Networks; Service Ranking; Indexing; BCloud-tree.
Autonomic Resource Management Framework for Virtualised Environments
by Raman Bane, Annappa B.
Abstract: Virtualisation enables multiples virtual machines (VMs) to co-locate
on a same physical machine with total isolation. Hence using VMs to launch
web services or applications is the common trend nowadays in enterprise
information technology (IT). Data centre provides infrastructure to create,
configure and manage VMs. It has seen as a utility that clients can pay for only
as needed. The growing complexity of modern networked computer systems
with virtualisation technology necessitates the needs efficient resource
management. We have proposed an intelligent resource manager to control the
resource allocation in Xen virtualised environment for dynamically allocating
resources to individual VM. Our resource management architecture comprises
of fuzzy logic based controller. Experimental results shows that with the
proposed system data centre can efficiently allocate CPU resources to VMs that
have been produced by customers. The scaling of CPU resources is
automatically done in accordance with dynamically changing workload at a
minimum granularity of 2 seconds. It improves the resource utilisation by 30%
as compared to the ideal method while maintaining throughput as equivalent to
the ideal workload allocation.
Keywords: autonomic computing; resource management; virtualisation; fuzzy
logic; Kalman filter; service level agreement; SLA.
Special Issue on: IoT Services for Trustworthy Secured Crowd Sourcing Applications
Response Time Based Resource Allocation According to Service Level Agreements in Cloud Computing
by G. Hemanth Kumar Yadav, K. Madhavi
Abstract: Cloud computing is a technology which offers various services as and when required by the user through various cloud providers. The scalable nature of cloud has made it to reach various domains and have a strong root in every organisation. The resource provisioning has become a challenging task for many cloud providers. This work proposes an efficient framework for handling storage, application and computation services, offering service level agreements (SLA) backed performance and uptime promises for their services in cloud computing. Further, this tries to benefit both the users as well as cloud providers by enhancing the features for the customers and by gaining profit for the providers. The proposed SLA based resource provisioning system is found to perform better than the existing other resource provisioning systems in terms of response time and other QoS parameters.
Keywords: cloud computing; resource allocation; service level agreement; SLA.
Special Issue on: Recent Technologies for Networking and Advanced Systems
Virtual Network Functions Placement System for 5G Mobile Network Architecture
by Sara Retal, Abdellah Idrissi
Abstract: The mobile telecommunications market is experiencing new trends taking advantage of network virtualisation and cloud computing techniques. This article advances one of the most crucial challenges which are the placement of virtual network functions over the cloud. In this vein, we propose a virtual network functions placement system which is designed to have the maximum level of flexibility for meeting the operators preferences and adjusting to the users behaviour. The system finds a fair solution respecting the constraints conforming to the 3GPP standards which are minimising serving gateways relocations cost and the cost of the path between packet data network
gateways and eNodeB base stations. Furthermore, the system aims at reducing the incurred cost of virtual machines. The proposed approach to implement the system solver is constraint programming and is compared to Boolean satisfiability, and game theory approaches. The proposed system solver is evaluated through computer simulations, and encouraging results are obtained.
Keywords: 5G mobile network architecture; virtual network functions placement; constraint programming; multi-objective optimisation.
A New Model for Communities' Detection in Dynamic Social Networks Inspired From Human Families.
by Rachid Djerbi, Mourad Amad, Rabah Imache
Abstract: Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature. However, most solutions have common problems: the stability and the community structures quality. In this paper, we propose a new model to find communities based on a new concept called
Keywords: dynamic social networks; community detection; communities overlap; large families; quality of community structures; stability.
Industrial Internet of Things over IEEE 802.15.4 TSCH networks: Design & Challenges
by Mohamed Mohamadi, Badis Djamaa, Mustapha Reda Senouci
Abstract: Using Internet of Things technologies in manufacturing provides a promising opportunity to build powerful industrial systems and applications. The quest for mobility, flexibility, and low-energy consumption has created a strong push toward using low-power wireless solutions to enable the Industrial Internet of Things (IIoT). This paper presents a survey of the emerging research concerning IIoT with a focus on the most promising solutions. It, first, outlines the main requirements to be addressed in order to build a powerful IIoT system. The paper, then, presents an overview accompanied by a comparative study of the most prevalent IIoT communication technologies. The study reveals the potential of the IEEE 802.15.4 standard with its Time Slotted Channel Hopping (TSCH) mode to lead the way through the IIoT, thanks to its latency, reliability and low-power characteristics along with the support of multi-hop communications. Based on this outcome, the paper provides an in-depth look at TSCH mechanisms and outlines the most challenging issues. Finally, the paper concludes the need to propose new solutions addressing such issues in order to make successful IIoT systems.
Keywords: IEEE 802.15.4e; Time Slotted Channel Hopping; Scheduling; Internet of Things; Industrial Internet of Things; Low power and Lossy Networks; Communication technologies.
A Novel Hybrid Broadcasting Protocol Based on Coverage Area Segmentation and Delay Adjustment for VANETs
by Houda HAFI, Wahabou ABDOU, Salah MERNIZ
Abstract: We propose a reliable dissemination protocol for broadcasting safety messages in Vehicular Ad~hoc Networks called SDBP (Segment Delay Based Broadcasting protocol). The protocol has a twofold goal: limiting the risk of interference and reducing the dissemination time. To achieve these goals, two mechanisms are proposed. The first one divides vehicle's coverage area into several segments depending on the local density. Thereafter, the priority to relay a message is given to nodes that are in the farthest segment from the source node. The second mechanism allows reducing the waiting time thanks to a periodic update process. The performances of SDBP have been studied in an environment with multiple concurrent data traffics. The goal was to validate its capacity when the radio channel becomes overloaded. The comparison study (in terms of delivery ratio, dissemination time, forwarders and redundancy packets ratio) shows that SDBP outperforms two VANETs' broadcasting protocols.
Keywords: VANETs; safety applications; broadcasting protocol; road segmentation; delay adjustment.
Fault Tolerance in Grid Computing by Resource Clustering
by Khaldi Miloud, Rebbah Mohammed, Meftah Boudjelal, Debakla Mohammed
Abstract: Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, the characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. In this paper, we propose FT-GRC a fault tolerance model that seeks to find the best substitute for the failed node by the clustering of the grid resources. This model is based on dynamic coloured graphs without replication of computer resources. The proposed fault tolerance mechanism uses scoring function to
determine the appropriate substitute for each failed node by calculating the performance level of each node, and later exploits clustering to determine optimally the choice of substitute. Experimental results show the efficiency of the scoring method and the gain obtained by looking for the substitutes in the same cluster and then by the research for the nearest substitutes.
Keywords: grid computing; dynamic coloured graph; fault tolerance; clustering; scoring.
Vehicular-Cloud Simulation Framework for Predicting Traffic Flow Data
by Abdelatif Sahraoui, Makhlouf Derdour, Ahmed Ahmim, Philippe Roose
Abstract: The traffic flow prediction has become an important process tailored with the exponential development of cities and the transportation systems. The main purpose of the prediction task is to improve the logistic services and reduce the cost of the road congestion. In this paper, we propose a Vehicular-Cloud simulation framework with a layer of traffic cloud services to predict accurate traffic flow data. Learning of supervised traffic flow data from several data sources is the core of these services. Particularly, we focus on a particular type of dependency (i.e., monotone dependency) between the learning traffic inputs and its responses. The learning algorithm we propose aims to solve the regression problem by predicting values of a continuous measure. The accuracy of the proposed cloud services have been tested under congestion conditions, where the results show better performances over short periods and daily forecasts.
Keywords: Traffic Data; iCanCloud Framework; Data Prediction; Vehicular Network.
Comparative study of Topk based on Fagins algorithm using correlation metrics in cloud computing QoS
by Kaoutar El Handri, Abdellah Idrissi
Abstract: With the exponential growth of cloud computing services recently, several internet technologies began to require the processing of multi-criteria ranking. The collaborative filtering methods and Topk selection computations have been proven to be more effective in information retrieval. In addition, they are widely used to evaluate the QoS for cloud services recommendation. However, the biggest challenge is not only to reduce the size of skyline results, but also to have a good response quality that reflects the user requirement. To deal with these problems, we propose in this paper an approach based on Topk algorithm combined with the weighted sum method. This approach is introduced for refining the skyline result using the Topk query advantages. Then in order to evaluate the performance of our approach, we compared the proposed algorithm with Fagins one. The experimental results show the
efficiency of our algorithm particularly in comparing the runtime results and using specific metrics of correlation.
Keywords: Topk; skyline; weighted sum method; cloud service; Fagin’s algorithm; quality of service; QoS; correlation.
Microblaze-based parallel implementations of Elliptic Curve Scalar Multiplication over Fp on FPGA
by Ahmed Mohamed Bellemou, Nadjia Benblidia, Mohamed Anane, Mohamed Issad
Abstract: This paper presents flexible software/hardware parallel architectures for embedded elliptic curve cryptosystem (ECC) on FPGA as multi-processor system on programmable circuit (MPSoPC) design. The implementations perform elliptic curve scalar multiplication (ECSM) over arbitrary prime fields (Fp) using montgomery power ladder (MPL) algorithm and Chudnovsky projective system. Our aim is to achieve the best trade-off between flexibility, area and speed. In fact, the integration of multi Microblaze processors allows not only the flexibility of the overall system but also the exploitation of the parallelism in ECSM computation with several degrees. At the low abstraction level, the critical finite field operation which is Montgomery modular
multiplication (MMM) is implemented in hardware Accelerator MMM (AccMMM) core based on the modified high radix-r (r = 232) MMM algorithm. The proposed architectures have been implemented in Xilinx Virtex-5 FPGA. The execution times for performing 256-bit and 521-bit ECSM are 19.98 ms
and 81.42 ms, respectively.
Keywords: embedded elliptic curve cryptosystem; parallel scalar multiplication; montgomery power ladder; MPL; projective coordinates system; montgomery modular multiplication; MPSoC; FPGA.