International Journal of Communication Networks and Distributed Systems (28 papers in press)
A Crowdsensing Market Based on Game Theory: Participant Incentive, Task Assignment and Pricing Guidance
by LIANGGUANG WU, Yonghua Xiong, Kangzhi Liu, Jinhua She
Abstract: The development of 5G and internet of things technologies has promoted application of crowdsensing services. Consequently, online crowdsensing markets, based on data trade, has emerged. In this article, we first investigate the status quo of the current crowdsensing and crowdsourcing markets, then analyse behavioural characteristics among participants. Thereafter, we design a unified crowdsensing market framework based on supply and demand. These are aimed at encouraging mobile users and data requesters to participate in market activities, with special attention paid to participant incentive models affected by the market environment. Next, we formally consider several new features in the crowdsensing service to provide iterative methods for solving sensing strategy of all participants, with the aim of achieving Nash equilibrium, including task assignment for mobile users and price guidance for data requesters. We validate our proposed methods by providing some numerical results, and discuss several challenges and open issues to be solved.
Keywords: crowdsensing market; task assignment; incentive mechanism; game theory.
Improvising Service Broker Policies in Fog integrated Cloud environment
by JYOTI BISHT, V.V. Subrahmanyam
Abstract: With the huge amount of internet users and accelerating IoT devices load on the network is increasing day-by-day. Cloud computing technology is offering services to its customer on demand basis with some overheads in response time. Fog integrated cloud computing is one of the solutions which can reduce the response time due to its proximity to users. However, this technology also has certain challenges like task-scheduling and load-balancing among servers due to limited resources. In this paper we are proposing two dynamic service broker policies, modified-service proximity and modified-optimise response time. These are devised after going through the gaps of existing policies service proximity (SP) and optimise response time (ORT). Further, through simulations it is proved that modified-SP gives better response time and processing time than SP and modified-ORT gives better response time than ORT at the cost of increased processing time.
Keywords: fog computing; cloud computing; service broker policy; scheduling; load balancing; resource provisioning; service proximity; optimised response time; modified SP; modified ORT.
An Investigation on Teletraffic Attributes for Channel Selection of IoT Objects in Cognitive Radio Internet of Things Networks towards 5G
by ARNAB KUNDU, Wasim Arif
Abstract: This paper investigates multiple attributes and related comparative issues regarding channel selection of IoT objects for cognitive radio IoT networks towards 5G. CU or SU as IoT nodes may intelligently manage their activity in a licensed spectrum by accessing vacant spectrum through dynamic spectrum access methodology. So that IoT is a booming developing concern to organise digital accessories. It will also provide the fastest data connections through sensor nodes by producing more data than any other emerging technology. With the continuous evolution in CRN and cognition capability, the IoT objects may think, learn, and make decisions by perceiving outside worlds. To frame the IoT with CR-based architecture in the coming future, some attributes such as intracell/intrapool handoff latency, intercell/interpool handoff latency, link continuation probability, link failure probability, switching cost, awaited number of spectrum handoff, non-execution probability, blocking probability, dropping probability, and throughput to learn the overall network attributes.
Keywords: IoT; CRN towards 5G; CRIoT; DSA; handoff; channel selection attributes.
An Autonomic Management System for IoT Platforms based on Data Analysis Tasks
by Clovis Ouedraogo, Jose Aguilar, Christophe Chassot, Samir Medjiah, Khalil Drira
Abstract: In this work, we propose an autonomic management system (AMS) for the internet of things (IoT) platforms, which uses the concept of autonomic cycle of data analysis tasks to improve and maintain the performance in the IoT platforms. The concept of autonomic cycle of data analysis tasks is a type of autonomous intelligent supervision that allows reaching strategic objectives around a given problem. In this paper, we propose the conceptualisation of the architecture of an AMS composed by an autonomic cycle to optimise the quality of services (QoS), and to improve the quality of experiences (QoE), in IoT platforms. The autonomous cycle detects and discoveries the current operational state in the IoT platform, and determines the set of tasks to guarantee a given performance (QoS/QoE). This paper presents the details of the architecture of the AMS (components, knowledge models, etc.), and its utilisation in two case studies: in a typical application in an IoT context, and in a tactile internet system.
Keywords: autonomic computing; internet of things; IoT; data analysis tasks; autonomic management system; AMS; quality of experiences; QoE.
Load Balancing Algorithms with Cluster in Cloud Environment
by Kshama S. B, Shobha K. R
Abstract: Load balancing is one of the important aspects of cloud computing. Its main goal is to improve system performance and to reduce its cost. Cloud computing has a dedicated load balancer to attain this. Sometimes, heavy traffic may overwhelm the load balancer and disrupt it to achieve its goal. The repeated disruption has an impact on customer service and results in poor performance. The clustering technique gives a solution to this by reducing the burden on a single machine and improves system performance. In this paper, an analysis has been made to test the performance of the load balancer with and without the cluster. The previously proposed capacity-based load balancing (CBLB) and artificial bee colony_CBLB algorithms are considered during this experiment. The results are compared by applying clusters on existing load balancing algorithms. In addition to this, the performance of the ABC_CBLB with cluster is also compared with two existing cluster load balancing algorithms. The results of CBLB and ABC with cluster are better than the other comparing algorithms.
Keywords: load balancing; cloud computing; cluster; capacity-based load balancing; CBLB; artificial bee colony; ABC; virtual machine; cloudlets.
Research on an Optimized Encryption Algorithm for Network Information Security Communication
by Ju Li
Abstract: At present, one of the commonly used encryption algorithms is the block cipher AES method, but in the design, we often only consider the bounded attack opponent, but in the face of the needs of the development of artificial intelligence, it is difficult to meet the secure communication of network information. This time, an optimised and improved GANs encryption algorithm based on neural network is proposed. The encryption algorithm can improve the objective function and learning model, so as to achieve better algorithm security performance. Through simulation analysis, it can also be seen that with the increase of training times, the neural network training effect of Bob, Alice and Eve is better. The proposed optimisation algorithm can realise face generation in the case of non artificial knowledge, which has significant advantages compared with the traditional encryption algorithm.
Keywords: network information; safety; encryption algorithm; GANs model; neural network.
CFEP-MObile: Competitive Fruit Fly optimizer-based Energy aware routing Protocol under mobile sink based WSN
by APURVA RUSHIKESH SAOJI, Giduturi Srinivasa Rao
Abstract: Wireless sensor networks (WSNs) are comprised of numerous promising applications from where the sensed data are collected from remote applications. With respect to the promising applications related to data gathering, the desired information from the unreachable or remote locality, WSN poses a major challenge for the energy-efficient routing in data for improving communication. The literature presents several energy-aware routing protocols to maximise the lifespan of the sensor nodes. Consequently, an energy-efficient clustering approach, based on competitive fruit fly optimiser (CFFO) is developed in this paper to enhance the energy and the total lifetime of nodes such that the cluster head is chosen optimally. The hybrid optimisation approach named, CFFO is developed for controlling the convergence rate of the CFFO with the newly designed fitness function by considering the four various objectives like energy, distance, delay, and link lifetime. However, the developed CFFO algorithm is designed by the integration of the competitive swarm optimiser (CSO) and the fruit fly optimisation algorithm (FFOA). This method achieved efficient performance in terms of metrics like PDR, energy, and delay with the maximum PDR of 100%, high energy of 0.367 J, and minimum delay of 0.0925 sec, respectively.
Keywords: wireless sensor network; WSN; routing; fruit fly optimiser algorithm; competitive swarm optimiser; CSO; clustering.
Accuracy Evaluation of Supervised Machine Learning Classification Models for Wireless Network Traffic
by Elans Grabs, Ernests Petersons, Dmitry Efrosinin, Aleksandrs Ipatovs, Janis Kluga, Valentin Sturm
Abstract: The article contains results of training and testing machine learning models with captured network traffic data. The main goal is to perform classification of video traffic in computer networks. Multiple performance metrics have been evaluated for commonly used classic supervised machine learning algorithms, as well as more advanced convolutional neural network model (for comparison). The article describes in detail the experimental setup, traffic pre-processing procedure, features extraction with different traffic window length and model parameters for training/testing. The article provides some experimental results in form of tables and 3D surface plots. The conclusion of the article summarises the main findings and outlines the future study directions.
Keywords: accuracy; classification models; features extraction; network traffic; performance metrics; statistical parameters; supervised machine learning; traffic intensity; window length; wireless networks.
Energy Efficient Cluster-Based Routing Scheme using Type-2 Fuzzy Logic in Underwater Wireless Sensor Networks
by Roshani Bhaskarwar, Dnyandeo Pete
Abstract: To enhance the energy and network lifetime in underwater wireless sensor networks, an optimum cluster-based routing protocol using fuzzy logic for UWSNs (OCR-FLU) is developed in the proposed work. Initially, the clusters are formed using K means algorithm. The existing literature in UWSNs has used type-1 fuzzy logic model (T1FL) in clustering approaches. In the proposed protocol, type-2 fuzzy logic (T2FL) system which is more accurate than the T1FL has been implemented to select an appropriate cluster head (CH) considering three parameters such as residual energy, distance to the surface sink, and packet delivery ratio. The CH then gathers data from each of its cluster members in a TDMA fashion and sends it to the sink via forwarder nodes based on maximum fitness value. The simulation results show that the proposed OCR-FLU outperforms the existing algorithms MLCEE and FBR in terms of energy efficiency, throughput, PDR and network lifetime.
Keywords: underwater wireless sensor network; UWSN; cluster; cluster head; CH; type-2 fuzzy logic; T2FL; energy efficiency; packet delivery ratio; PDR.
Utilization Aware VM Placement Policy for Workload Consolidation in Cloud Data Centers
by Dipak Dabhi, Devendra Thakor
Abstract: In recent years, the demand for cloud services has risen. Data centres must have a growing number of servers to accommodate rising demand for cloud services, and data centres consume a lot of energy. Virtual machine consolidation (VMC) is a strategy for reducing energy consumption in data centres by shutting down underutilised servers while maintaining service levels (SLA). The VMC process is separated into four policies: overloaded host detection, underloaded host detection, virtual machine selection, and virtual machine deployment. The utilisation aware VM placement (UAVMP) technique is presented in this research work, which efficiently selects the destination host for VMs migrating from overload/underload hosts based on the hosts utilisation and resource skewness. The performance is assessed using the CloudSim simulator. When we compare UAVMP results with power-aware best fit decreasing (PABFD), modified best fit decreasing (MBFD), first fit (FF) and least fit (LF), we find that UAVMP outperform all.
Keywords: cloud computing; VM consolidation; quality of service; QoS; service level agreement; SLA; VM selection; overload host detection; VM placement; underload host detection.
Cat swarm optimization-based mobile sinks scheduling in large-scale wireless sensor networks
by Srinivasulu Boyineni, Kavitha K., Sreenivasulu Meruva
Abstract: In wireless sensor networks (WSNs), the hotspot problem is one of the major challenging issues because it isolates some network parts and interrupts the data routing. The hotspot problem is mitigated through a mobile sink, where it visits a set of nodes in the network called rendezvous points, whereas the remaining nodes traverse their data to it. In large-scale WSNs, the travelling distance of MS is longer, and it increases the delay of reaching an RP. So, the data overflow may occur due to a limited buffer of sensor nodes. This problem is avoided by increasing the number of mobile sinks in the WSNs. In this context, a cat-swarm optimisation algorithm is used to decide the optimal set of mobile sinks and a simple geometric method to determine the optimal visiting order for each mobile sink. The proposed work is compared with start-of-art literature, and the proposed work outperforms them.
Keywords: wireless sensor networks; WSNs; data acquisition; multiple mobile sinks; cat swarm optimisation; ant colony optimisation.
Energy consumption profiles of wireless sensor nodes in smart cities using CupCarbon (V 5.0) simulator
by HANSHITA PRABHAKAR, Asna Furqan
Abstract: In the development of smart cities, smart devices are integrated with sensors and embedded systems to provide innovative services which enhance the internet of things (IoT) also the power consumed by these devices for specific operations will be a primary concern. Wireless sensor network (WSN) consists of enormous numbers of sensor nodes densely deployed over smart city sensor nodes that are efficient in collaborating and computing the condition of their surrounding environments. The proposed work of this paper analysing that how the different profiles of wireless sensor networks for intelligent cities consume energy as routing, communication of messages between two sensors, broadcasting of transmissions, and mobility is shown by the graph of energy consumption in joule (J) versus simulation time in seconds (S). We have performed and analysed the results on the network simulator called CupCarbon simulator and the novelty of this paper is we could examine and estimate the errors while the transmission and reception happen in between the sensor nodes with the help of the console output messages.
Keywords: smart city; CupCarbon; V 5.0 simulator; wireless sensor network; internet of things; energy consumption.
A Rapidly-exploring random tree-based intelligent congestion control through an alternate routing for WSNs
by P. Suman Prakash, D. Kavitha, Chenna Reddy P.
Abstract: In wireless sensor networks (WSNs), congestion is a challenging issue, and it degrades the efficiency in terms of packet loss, energy wastage, throughput, etc. The primary cause of the congestion in WSNs is the data routing of the many-to-one pattern. It means multiple nodes can send their data to a single sink using multi-hop transmissions. To control the congestion in WSN, we use a rapidly-exploring random tree (RRT)-based mechanism to divert the data packets from the congested nodes. Initially, we use a mathematical model to determine the congested nodes in the WSNs. Further, we identify the routing path using the RRT algorithm in which the algorithm can construct a dynamic routing while avoiding the congested nodes in the routing path. We estimate the efficiency of our approach using simulation runs and compare the results using the recently published algorithm. We notice the improved performance in our method.
Keywords: wireless sensor networks; WSNs; congestion factor estimation; intelligence congestion control; rapidly-exploring random tree; routing; quality of service.
A search ranking algorithm for web information retrieval.
by Shanshan Zhi, Huanhuan Wang
Abstract: The development of the internet has seen an explosion in the amount of information, which has increased the scope of queries for users but greatly increased the difficulty of searching for valid information. In order to retrieve effective information faster, search ranking algorithms are needed to rank the retrieved information and return it to the user. This paper briefly introduced the RankNet algorithm among web information search ranking algorithms and optimised the loss function to improve its retrieval ranking performance. Simulation tests were carried out with Microsoft public data set MSLR-WEB30K. The improved RankNet algorithm was compared with the ranking support vector machine (SVM) algorithm and the traditional RankNet algorithm. The results showed that as the number of returned retrievals increased, the retrieval ranking performance of all three search ranking algorithms tended to decrease; under the same number of returned retrievals, the improved RankNet algorithm had the best performance.
Keywords: search ranking; rank learning; RankNet; pairing loss; support vector machine; SVM.
Grey Prediction-based Energy-Aware Opportunistic Routing in WSN
by NAGADIVYA S, R.Manoharan Rajendiran
Abstract: Opportunistic routing (OR) protocol is widely applied for wireless sensor network (WSN) for maximising energy as well as network lifetime. OR selects the capable forwarder set of node for multi-hop forwarding, based on residual energy and transaction history. This selection process using the forwarding set will continue for every hop, till the destination is reached. This paper proposes a new routing protocol, namely grey prediction-based energy-aware OR protocol for WSN. The grey-prediction model is a proven model which needs minimum data for prediction and is used to select the nodes for the forwarding set used in every hop. The new protocol is simulated and compared with existing ones for performances. The observations suggested that the advocated protocol shows superior performance as against the already accepted protocols about network lifetime, throughput, and energy consumed and remaining.
Keywords: opportunistic routing; wireless sensor network; WSN; grey prediction; energy efficiency.
Utilization Aware Virtual Machine Selection Policy for Workload Consolidation in Cloud Data Centers
by Dipak Dabhi, Devendra Thakor
Abstract: Large-scale virtualised data centres have been constructed throughout the world in response to the rising demand for service-oriented computing and the expansion of cloud computing technologies. These vast data centres consume a significant amount of power and have a significant carbon footprint, which must be reduced to the greatest extent feasible. The dynamic virtual machine consolidation provided by live migration leads to significant energy savings. However, it also constitutes a violation of the service level agreement (SLA). The process of selecting virtual machine (VM) for migration is critical in the realm of energy-aware cloud computing. This study proposes a novel utilisation aware VM selection (UAVMS) policy that aids in VM selection for migration using server utilisation and skewness value. We use the CloudSim toolkit to build our UAVMS policy and compare its performance with existing methods. The experimental results shows that UAVMS reduces the energy usage and SLA violations.
Keywords: cloud computing; VM consolidation; VMC; QoS; service level agreement; SLA; VM selection; overload host detection; VM placement; underload host detection.
A Novel High-efficiency Searchable Encryption Scheme Under Robot Cloud Computing Environment
by Zhongli Wang, Aiyun Ju
Abstract: This paper proposed a high-efficiency searchable encryption scheme under cloud computing environment. Through the matching calculation of keyword index set and keyword trapdoor generated by the data user, the searchable encrypted mechanism is realised. By utilising the powerful computing resources of the cloud server, the pre-decryption operation is introduced to reduce the computing time cost of the data user. The cloud server does not know anything about the original file in the cloud through a trapdoor corresponding to the user-provided keyword (the trapdoor retrieves files containing a specific keyword from a large number of encrypted files). Security analysis shows that the new scheme cannot leak data privacy information and has stronger security than other state-of-the-art schemes.
Keywords: searchable encryption; cloud computing; keyword search; pre-decryption operation.
Small target detection method based on feature fusion for deep learning in state grid environment evaluation
by Di Su, Yuan Zhang, Liwei Wang, Fei Wang, Wei Sun, Zhenhao Zhang, Juan Feng, Changhao Sun
Abstract: Aiming at the problem that small and medium-sized targets cannot be detected in real time in high-resolution images, a new target detection network model is proposed. Firstly, the residual network RESNET is used as the basic network structure, an additional pyramid network model is added, and the pool layer is used to increase the number of hierarchical feature mapping. Then, the feature map is deconvoluted, and the high-level semantic feature map information and shallow feature map information are fused. Finally, the target is detected. Based on the analysis of the experimental results, compared with the existing target detection network model, the deep learning network model using feature fusion techniques has a detection accuracy of 80.2% on the standard dataset Pascal voc2007, and the detection speed reaches 27 frames per second, which meet the requirements of high-resolution image real-time monitoring and small target detection.
Keywords: feature fusion; residual network; pyramid network; small target detection; deconvolution.
CRLMDA: CRL Minimization and Distribution Algorithm in Cluster-based VANETs
by Dinesh Singh, Ashish Maurya, Ranvijay Ranvijay, R.S. Yadav
Abstract: This paper proposes an algorithm called CRLMDA (CRL Minimization and Distribution Algorithm) to minimize CRL in cluster-based VANETs. The algorithm monitors the use of certificates during communication. A cluster head (CH) vehicle informs regarding the certificate to the certificate authority to avoid lazy processing of safety applications. The CH vehicle that identifies a vehicle as malicious immediately initiates a local and global revocation process to avoid mishappening in network. The proposed algorithm compresses the CRL using Bloom filter and distributes it to the other vehicles via roadside units and the CH vehicles. Also, we give a message authentication algorithm of the received event reporting messages at the CH vehicle. The performance of our proposed CRLMDA is compared with two well-established algorithms, HHL and SCRLE, in varying transmission ranges and vehicle densities. The reported results show that the CRLMDA performs better in revocation overhead and CRL distribution delay than existing algorithms.
Keywords: Vehicular Ad-hoc Network (VANET); Cluster Head (CH); Malicious Vehicle; Safety Applications; Certificate Authority.
An Energy Efficient Dynamic Small Cell On/Off Switching with Enhanced k-means Clustering Algorithm for 5G Hetnets
by Janani Natarajan, B. Rebekka Issac
Abstract: The massive growth in the current and envisaged cellular traffic lead to innovations in 5G heterogeneous networks (HetNets) and implementation technologies. The small cells (SCs) or small base stations (SBS) aided macro base station (MBS) topology in HetNets effectively accomplish capacity growth and spectrum reuse at the cost of network complexity, power consumption and energy efficiency. In this paper, we propose a mechanism to maximise the system energy efficiency jointly by enhanced k-means clustering and dynamic load based SC switching algorithm for HetNets. The clustering algorithm optimises the initial centroids for maximum inter-cluster separation. Within each cluster, SC switching is decided based on a permissible threshold of inter-cluster interference. Further, the intra cluster SC coordination is formulated as a cooperative game for load balancing. Simulation results illustrate performance improvement of the proposed scheme up to 20% in energy efficiency and 16% in system throughput compared to conventional k-means clustering approach.
Keywords: small cells; heterogeneous networks; HetNets; 5G; cluster; energy efficiency; macro base station; MBS.
Cyber Defense Using Attack Graphs Prediction and Visualization
by Shailendra Mishra
Abstract: The use of the internet and other related technologies has increased dramatically in recent years. Since sensitive and critical data is readily available on these systems, this information can easily be accessed. Information leaks or attacks on networked devices are becoming more common every day. This research explores the visualisation of attack graphs in public cyberspace to predict exploit paths across networks. Vulnerability analysis reveals various aspects of the system that are exploited. By combining graph adjacency matrices cyber-attack graphs are created. With the attack graph, grey areas and research points can be easily identified. Cybersecurity and network administration can be achieved by analysing M-steps. Moreover, machine learning algorithms such as SVM, RF, KNN, LR, and multilayer perceptron (MLP) are used to detect the attack and analyse the performance of the proposed system. In terms of accuracy, recall, precession, and F-score, RF and MLP were the best classifiers.
Keywords: IDS network security; attack graph; adjacency matrix; intrusion detection system; machine learning; cyber defence.
Time and Position Aware Resource Search (TPARS) Algorithm for the Mobile Peer-to-Peer Network using Ant Colony Optimization (ACO)
by Dharmendra Kumar, Ajay Kumar Dubey, Mayank Pandey
Abstract: The progressive growth of wireless mechanisms and the wide popularity of intelligent devices are attracting particular attention to peer-to-peer mobile ad hoc networks (P2P MANET). It has an important research topic to enhance the efficiency to search in P2P MANETs. Mostly, existing researches are given more emphasis on position-based clustering techniques. These researches do not give preference to the time. Due to it, the efficiency to search resources in P2P MANETs using the existing approaches is not good. This paper suggests a navel resource search approach that first usages position-aware peer clustering strategy. Then, using an ant colony optimisation method with pheromones, it chooses the preeminent neighbors peer that is of time-aware neighboring peers resource preferences and the time-aware neighboring peers availability. Based on the results of the experiment, this approach outweighs other approaches in terms of search delay, overhead traffic, and search success rate.
Keywords: resource discovery mechanism; peers resource preferences; ant colony optimisation; position aware; time aware; mobile P2P ad hoc network; P2P MANET.
IEEE 802.15.4 MAC protocol optimisation in body sensor networks: a survey, outlook and open issues
by Abdulwadood Mohamed Othman Alawadhi, Mohd. Hasbullah Omar, Noradila Nordin
Abstract: Body sensor networks (BSNs) have sparked a surge in interest and demand. BSNs is made up of miniature biosensors that gather and send data over a wireless network, allowing medical professionals to watch patients as they go about their regular lives and provide real-time opinions for medical diagnosis. This paper describes network architecture by BSN standard with many nodes that serve as biosensors in medical research to monitor patients health. Furthermore, IEEE 802.15.4 based on networks are often executed near other wireless networks operating in the same industrial, scientific and medical (ISM) band. In addition, this paper explained the media access control (MAC) protocol based on the IEEE 802.15.4 with operation modes distinguished by a variety of ruggedness which has led to its acceptance in many BSNs, and several restrictions recreate the crucial role in weakening its execution. This paper highlights, outlines, and critically evaluates two different MAC protocol optimisation approaches. A research plan will be demarcated for each approach for MAC protocol that has been rigorously studied the research challenges, previous solutions, and open research issues in this article.
Keywords: duty cycle; media access control protocol; body sensor network; BSN; IEEE 802.15.4; backoff.
Blockchain-based security in smart grid network
by Shailendra Mishra
Abstract: The cybersecurity threats in the smart grid network are prominent with conventional approaches providing integrated security control and communication protection for smart grids that are vulnerable to some types of attacks and limit their use in real-time applications. In this study, the statistical function predicts the system performance of the future timestamp and compares it with the actual performance to detect the attack. The asymmetric encryption function is used to find the user authentication, which is complex and increases the network latency. In this paper, a blockchain-based methodology is proposed for cybersecurity threat detection in smart grid networks without increasing the network latency. The proposed model is based on blockchain-based secure user authentication, lightweight data encryption and quantum key distribution multi-constraint-based edge selection, bi-fold intrusion detection system, and optimal user privacy management. The results show that the accuracy of the proposed model is 98%.
Keywords: smart grid; intrusion detection system; blockchain; quantum key distribution; QKD.
Hybrid ensemble techniques used for classifier and feature selection in intrusion detection systems
by Ankit Kharwar, Devendra Thakor
Abstract: The data security of networks is a universal problem for governments, companies, and persons. The frequency of internet attacks has grown substantially, as have attacker strategies. The solution to this problem is intrusion detection, a typical and successful methodology for planning intrusion detection systems (IDS) with machine learning. The proposed IDS method consists of three stages: pre-processing, feature selection, and classification. We remove duplicate data and normalised data in our method's first stage. Sequential forward floating selection (SFFS) with extra-tree use for feature selection removes unwanted features in our method's second stage. LogitBoost with extra-tree classification to use selected features in our method third stage. The proposed method is evaluated on standard datasets KDD CUP'99, NSL-KDD, UNSW-NB15, CICIDS2017, and CICIDS2018. The experimental results show that the proposed method outperforms the existing work in terms of accuracy, false alarm rate, and detection rate.
Keywords: intrusion detection; anomaly detection; machine learning; ensemble methods; extra-tree; feature selection; sequential forward floating selection; SFFS; boosting algorithm; LogitBoost algorithm; network security.
A preference-based comparison of select over-the-top video streaming platforms with picture fuzzy information
by Sanjib Biswas, Dragan Pamucar, Samarjit Kar
Abstract: Over the last few decades, the media industry has witnessed a paradigm shift. Gradually, with massive developments in information and telecommunication technology, the over-the-top (OTT) platforms have emerged as a widely accepted substitute for televisions. The present paper aims to focus on a list of popular OTT platforms in India and carry out a comparative analysis of their acceptability to the consumers in the market. For this purpose, this paper presents a novel extension of the hybrid full consistency method (FUCOM)-multi-attributive border approximation area comparison (MABAC) framework using actual score measures of picture fuzzy (PF) information for multi-criteria decision making (MCDM). We use the framework of quality of experience (QoE) for deriving the attributes. It is observed that the consumers give more importance on viewing experience, convenience and popularity aspects while Amazon Prime, Netflix and Disney+Hotstar are found as better choices to the regular viewers. The result shows consistency and stability.
Keywords: internet video streaming; over-the-top; OTT; platforms; quality of experience; QoE; full consistency method; FUCOM; multi-attributive border approximation area comparison; MABAC; picture fuzzy numbers; PFN; multi-criteria decision making; MCDM.
A novel privacy protection method based on node segmentation for social networks
by Zhongli Wang, Aiyun Ju
Abstract: In order to solve the problem of privacy disclosure of weight sequence in weighted social networks privacy protection, this paper proposes an anonymous weighted sequence method based on node segmentation to realise privacy protection of network structure, edge weight and weight sequence. In this method, the anonymity of edge weight sequence is realised mainly through the diameter distance within the group and the relative distance between nodes, which makes up for the privacy disclosure of weight sequence and improves the privacy protection mechanism of weighted social network. Under the premise of privacy security, this method can guarantee the structural features needed for social network analysis, the validity of the published data and the effective resistance to the attack of weight sequence. We also make comparison with other methods in terms of execution time and recognition rate, the results show that the proposed method can obtain shorter time and high node recognition.
Keywords: social networks; privacy protection; node segmentation; diameter distance.
A new adaptive routing algorithm using partitioning scheme in x-Folded TM topology to avoid deadlock
by Mehrnaz Moudi, Mohamed Othman
Abstract: A critical issue for interconnection networks is providing the shortest path without deadlock in each topology, and efficient routing algorithms are essential to avoid deadlock. In this paper, a new adaptive routing algorithm in x-Folded TM topology is presented. Using a partitioning scheme and admitting deadlock-free zones are the main factors in producing such an algorithm. The partitioning scheme exposes the relation of this routing to the x-Folded TM topology. To perform extensive simulation experiments, the traffic patterns and a wide range of injection rates are analysed. The simulation results show performance improvements when the performance is mainly dependent on the topology and the applied routing performance in each network. The efficiency of new adaptive routing to avoid deadlock in the x-Folded TM topology has been proven in this paper.
Keywords: adaptive routing; partitioning scheme; x-Folded TM topology; deadlock.