International Journal of Information and Computer Security (43 papers in press)

Regular Issues

• Two-Level Machine Learning Driven Intrusion Detection Model for IoT Environments  
  by Yuvraj Singh Malhi, Virendra Singh Shekhawat 
  Abstract: As a consequence of the growing number of cyber-attacks on IoT devices, the need for defences like intrusion detection systems (IDSs) has significantly risen. But current IDS implementations for IoT are complex to design, difficult to incorporate, platform-specific, and limited by IoT device’s resource constraints. This paper proposes a deployment-ready network IDS for IoT that overcomes the shortcomings of the existing IDS solutions and can detect 22 types of attacks. The proposed IDS provides the flexibility to work in multiple modes as per IoT device computing power, made possible via development of three machine learning-based IDS modules. The intrusion detection task has been divided at two levels: at edge devices (using two light modules based on neural network and decision tree) and at centralised controller (using a random forest and XGBoost combination). To ensure the best working tandem of developed modules, different IDS deployment strategies are also given.
  Keywords: deep learning; machine learning; intrusion detection system; IDS; random forest; network security; internet of things; IoT; denial-of-service.
  DOI: 10.1504/IJICS.2022.10043106
   
  
• A Comprehensive Survey on Fuzzy based Intelligent Intrusion Detection System for Internet of Things  
  by Nandhini U, Santhosh Kumar SVN 
  Abstract: Internet of things (IoT) is rapidly expanding, and it is having a greater impact on daily life. The internet of things is widely applied in a wide range of industries, from small to large. IoT-based is a collection of distributed smart devices with software that is capable of detecting data from a sensing domain, collaborate, and transmit the sensed data to a sink via internet as a backbone using multi-hop communication. Due to its resource constrained nature and also since the communication between the devices takes place via an open channel, providing security and monitoring the behaviour of the devices is a major challenge. It attracts cybercriminals attention, who have made IoT an easy target for malicious attacks. In the literature, various IoT-based intrusion detection systems (IDSs) have been proposed to address the security challenges of the IoT network. In this paper, a survey on fuzzy-based IDSs is carried out for highlighting their contribution and limitations with reference to intrusion detection accuracy, false positive rate and overall network performance. Moreover, this survey provides a detail analysis on fuzzy-based intrusion detection systems which aims in analysing intrusion detection accuracy and minimising false data rate to show the way for the future direction.
  Keywords: intrusion detection system; IDS; internet of things; IoT; security; malicious attacks; soft computing.
  DOI: 10.1504/IJICS.2022.10046235
   
  
• Unified Identity Authentication Scheme of System Wide Information Management Based on SAML-PKI-LDAP  
  by Lizhe Zhang, Zhuoning Bai, Zhijun Wu, Kenian Wang 
  Abstract: System wide information management (SWIM) is a platform to share and exchange information on the new air traffic management (ATM) services between different departments and systems in the civil aviation field. Through the connection of SWIM and various application services, a virtual information pool is formed to solve the interconnection issues of different systems. To ensure data security in the system and quick authentication of legitimate users, we propose a unified identity authentication scheme for SWIM. This scheme improves the security assertion markup language (SAML) cross-domain authentication model and integrates it with the public key infrastructure (PKI) authentication system and lightweight directory access protocol (LDAP). Experimental results show that this scheme realises the functions of user management, identity authentication, and cross-domain access, which can meet requirements of the SWIM gateway.
  Keywords: system wide information management; SWIM; security assertion markup language; SAML; identity authentication; digital certificate; directory access protocol.
  DOI: 10.1504/IJICS.2022.10046260
   
  
• Ciphertext-Policy Attribute-Based Delay Encryption  
  by Lijiao Chen, Kewei Lv 
  Abstract: Timed-release CP-ABE can provide fine-grained and timed-release access control while ensuring data confidentiality. Existing schemes usually rely on a trusted third-party called time server. This paper proposes a novel timed-release CP-ABE scheme named ciphertext-policy attribute-based delay encryption (CP-ABDE), which does not require a time server. Specifically, we formalise the notion of CP-ABDE and its system model and security model. Furthermore, we provide a formal construction that is secure under the decisional bilinear Diffie-Hellman assumption and repeated squaring assumption. Finally, performance analysis shows that the scheme performs well while achieving timed-release access control.
  Keywords: ciphertext-policy attribute-based encryption; CP-ABE; time-lock puzzle; TLP; access control; timed-release; delay.
  DOI: 10.1504/IJICS.2022.10046931
   
  
• Implementation of a Secret Sharing based Masking Scheme against Side-channel attack for Ultra-lightweight Ciphers in IoT  
  by Swapnil Sutar, Vikas Tiwari, Ajeet Singh 
  Abstract: IoT applications consist of a group of small physical devices with sensing capabilities, working collaboratively to provide a specific functionality. Collaboration is realised by sending data from one or more devices in a network to another device or group of devices. Data stored or processed across an IoT ecosystem is likely to contain sensitive information, requiring strong confidentiality. Cryptographic algorithmic modules embedded on these physical devices are particularly vulnerable to side channel analysis. The most common countermeasure for block cipher implementations is masking, which basically randomises the variables to be protected by combining them with numerous random values. In this paper, masked implementation of lightweight block ciphers PRESENT and BORON is demonstrated. In the framework, secret sharing-based masking procedure is adapted as an alternative to Boolean masking. We then conduct a security analysis and empirical observations of our framework. To prove the novelty and practical adaptability of the proposed framework, implementation and obtained results are also presented in the paper.
  Keywords: masked nonlinear transformation; lightweight block cipher; PRESENT; BORON; randomised propagation; countermeasures; secret sharing.
  DOI: 10.1504/IJICS.2022.10046932
   
  
• Artificial Neural Network based Intrusion Detection System using Multi-objective Genetic Algorithm  
  by Narottam Patel, B.M. Mehtre, Rajeev Wankar 
  Abstract: With recent advances in cyber-attacks, traditional rule-based intrusion detection systems are not adequate to meet the present-day challenge. Recently machine learning-based intrusion detection system (IDS) has been proposed to detect such advanced/unknown cyber-attacks. The performance of such machine learning-based IDS largely depends upon the feature set used. Generally, using more features increases the accuracy of attack detection and increases detection time. This paper proposes a new network intrusion detection system based on an artificial neural network (ANN), which uses a multi-objective genetic algorithm to satisfy the requirements: accuracy of attack detection and faster response. The performance of the proposed method is tested by using the KDD
  Keywords: intrusion detection system; IDS; advanced persistent threat; KDD‘99; NSL-KDD; CIC-IDS-2017; feature selection; artificial neural network; ANN; multi-objective genetic algorithm.
  DOI: 10.1504/IJICS.2022.10046933
   
  
• A Shallow based Neural Network Model for Fake News Detection in Social Networks  
  by S.P. Ramya, Eswari Rajagopal 
  Abstract: The convenience of connecting through the internet and eagerness to spread any news through online social media is very intriguing as it can be done rapidly and with very little effort. This permits the very quick spread of fake news globally and misleads the people against democracy and freedom. The content of fake news very closely resembles true news. So, technically, it is tough for a deep neural network to
  Keywords: attention mechanism; deep learning; optimisation; natural language processing; NLP; convolution neural networks; CNN.
  DOI: 10.1504/IJICS.2022.10046935
   
  
• Outsourcing decryption of KP-ABE using elliptic curve cryptography  
  by Dilip Kumar, Manoj Kumar 
  Abstract: Internet of things (IoT) has changed our lives greatly and impacted almost everything in the digital world. Devices used in IoT are connected with each other through the internet for communication. These devices are vulnerable to various interceptions and suffer from resource limitations. Key policy attribute-based encryption (KP-ABE) is a modern cryptographic scheme that provides security and access control mechanism in an IoT environment. Therefore, an outsourcing scheme is proposed to outsource decryption of KP-ABE to reduce decryption overhead for resource-constrained devices (RCDs). In our scheme, computational complexity is reduced by using elliptic curve cryptography (ECC) and a linear secret sharing (LSS) scheme is utilised to represent the access policy. The security of our scheme is given under the replayable chosen-ciphertext attacks (RCCAs) model. The implementation shows that our proposed scheme reduces the complexity of decryption and computational time as compared to other schemes.
  Keywords: internet of things; IoT; key policy attribute-based encryption; KP-ABE; elliptic curve cryptography; ECC; point scalar multiplication; PSM; access structure; LSS scheme.
  DOI: 10.1504/IJICS.2022.10046937
   
  
• Dynamic Group Signature Scheme Using Ideal Lattices  
  by Abhilash M. H, Amberker B. B 
  Abstract: Group signature scheme is a cryptographic primitive that allows its registered group members to generate signatures on behalf of the whole group without revealing their identity. Ling et al. (2018) proposed the first constant size group signature scheme using ideal lattices, where signatures size is independent of number of users N in the group. This is a partial dynamic scheme that supports only registration of new users. It doesn’t allow revocation of group users. In this paper, we construct an ideal lattice based constant size dynamic group signature scheme that supports both revocation and registration. In addition, an efficient revocation technique based on time bound signing keys is proposed to reduces the verification cost. The security of the proposed scheme is proved in the random oracle model based on the hardness of Ring Short Integer Solution (RSIS) and Ring Learning With Errors (RLWE) assumptions.
  Keywords: Group signature scheme; Lattice based cryptography; Ideal lattices; Dynamic group signature scheme; VLR; Time bound keys.
  DOI: 10.1504/IJICS.2022.10047374
   
  
• A Method of Speech Information Hiding in Inactive Frame Based on Pitch Modulation  
  by Zhijun Wu, Chenlei Zhang, Junjun Guo 
  Abstract: To solve the problem that the speech information hiding algorithm based on random position selection and matrix coding has insufficient hiding capacity, the paper proposes a novel pitch modulation steganography method based on inactive frame. In this method, the least significant bit (LSB) replacement method is adopted for inactive frames, and the speech information hiding algorithm based on random position selection and matrix coding is adopted for active frames, which realises the pitch modulation information hiding method based on inactive frames. Finally, simulation experiments are carried out for the pitch modulation information hiding method based on inactive frames. The results indicate that the maximum hiding capacity of the algorithm in this paper can reach 241.67 bps, which significantly improves the hiding capacity, and at the same time the concealment has also been improved to a certain extent.
  Keywords: information hiding; random position selection; matrix encoding; pitch modulation; inactive frame.
  DOI: 10.1504/IJICS.2022.10047375
   
  
• JPBlock: Augmenting Security of Current Journal and Paper Publication Processes using Blockchain and Smart Contract  
  by Justice Odoom, Huang Xiaofang, Richlove S. Soglo 
  Abstract: Scholarly journals (SJ) play an indispensable role in the scrutiny and dissemination of research. However, the current SJ and academic paper publishing infrastructure is fragmented and dependent on centralized servers compelling researchers to hop from one journal platform to another for familiarity, account registration and subsequent usage. In this work, we advance a secure blockchain-based multi-tenant decentralized framework dubbed JPBlock to facilitate paper submission through to publication and discoverability. Leveraging fundamental attributes of blockchain, smart contract and decentralized storage technology, we advance novel capabilities including paper tracking, proof of authorship and voting on reputation of journals while enforcing core security principles on-chain and off-chain. Open access papers are made available on a global scale while non-open access papers are securely encrypted supporting the subscription-like model. Proof-of-concept implementation reveals that the framework is feasible and satisfies fundamental security requirements with journals and authors incurring overall costs of $46.66 and $25.61 respectively.
  Keywords: Authors; Blockchain; Decentralized applications (DApps); Ethereum; Journals; Smart contract.
  DOI: 10.1504/IJICS.2022.10047376
   
  
• Tree Derived Feature Importance and Bayesian Optimization For Improved Multi-class classification of DDoS Attacks in Software Defined Networks  
  by Ancy Sherin Jose, Latha R. Nair, Varghese Paul 
  Abstract: Software defined networking (SDN) is an emerging networking paradigm which mitigates the inadequacies of traditional networks. The centralised controller in SDN allows for the global view of network as well as for controlling the network operations from a single point. Like the traditional networks, SDN is also prone to network vulnerabilities. Intrusion detection based on machine learning techniques is effectively used in traditional networks and have found promising results. The research in security of SDN is in its early stages and researchers from academia and industry are working for this cause. In this paper, machine learning-based intrusion detection is attempted for multi-class classification of distributed denial of service (DDoS) attacks in a software defined networking (SDN) environment. The feature importance derived from tree-based classifiers has been used for the feature selection to reduce the feature space which in turn reduces the time and space complexities. Hyperparameter tuning with TPE driven Bayesian optimisation (BO) has also been used for performance enhancement of the classifier. This multistage machine learning model achieves DDoS detection accuracy of 99.87%. The experimental evaluation is performed with SDN DDoS dataset and the results have been tabulated.
  Keywords: software defined networking; SDN; DDoS attack detection; machine learning; ML; multi-class classification; Bayesian optimisation; feature importance.
  DOI: 10.1504/IJICS.2022.10047378
   
  
• Outlier Detection in WSN with SVDD via Multi-Interpolation Auto-encoder  
  by Bhanu Chander, Kumaravelan Gopalakrishnan 
  Abstract: Due to limited resources and harsh deployment environments, data outliers frequently rise in wireless sensor networks (WSNs). Hence, the collected data observations contain poor data quality and reliability. In recent years, research attempts have focused on utilising temporal and spatial correlation of the sensed data in WSNs but ignored the dependencies among the sensor node’s attributes, which reduce overall communication. Instead of transmitting every sensed data of a corresponding sensor node to the base station, this paper pursues a novel approach to incorporating a representation method using an auto-encoder to identify the redundant data in its transmission path through cluster head (CH). With this scenario, this paper also empirically assesses the integration of auto-encoders and SVDD to learn a condensed form of a low dimensional data point by interpolating the convex combination of the sensed data, which can semantically mix their characteristics in a distributed manner and identify the outlier respectively.
  Keywords: wireless sensor network; WSN; anomaly detection; outlier; auto-encoder; support vector data description; Parzen neural networks.
  DOI: 10.1504/IJICS.2022.10047379
   
  
• A Comparative Study of Deep Transfer Learning Models for Malware Classification using Image Datasets  
  by Ranjeet Kumar Ranjan, AMIT SINGH 
  Abstract: This paper proposes deep convolution neural network-based malware classification approach. The proposed work is a transfer learning approach, where we have developed multiple deep learning classification models. The classification models are built by adapting multiple pre-trained convolutional neural networks, namely; Xception, VGG19, InceptionResNetV2, MobileNet, InceptionV3, DenseNet, and ResNet50. In the current work, weights of pre-trained models are embellished by adding three fully connected (FC) layers. The proposed models have been evaluated on two different malware datasets, Microsoft and MalImg, consisting of malware images. The focus of this paper is to analyse the performance of fine-tuned CNN models for malware classification. The results of our experiments show that InceptionResNetV2 and Xception models have performed considerably well for the Microsoft dataset with accuracy equal to 96% and 95%, respectively. In the case of the MalImg dataset, InceptionResNetV2, InceptionV3, and Xception models have achieved excellent performance with an accuracy of up to 96%.
  Keywords: cyber security; malware classification; deep learning; transfer learning; convolutional neural network; malware image dataset.
  DOI: 10.1504/IJICS.2022.10047490
   
  
• Enriching Blockchain with Spatial Keyword Query Processing  
  by Muhammad Kashif Azhar, Bin Yao, Zhongpu Chen 
  Abstract: Recently, after successfully revolutionising financial services, blockchain is now transforming a variety of other domains. However, current working abstraction requires technology to have more maturity from several key perspectives, and linear data processing is one of them. Blockchain, with its core characteristics like immutability, traceability, and decentralisation, has the potential to support various types of data. Currently, we found this design an ideal model to support spatial kind of data structures, which to the best of our knowledge is a novel feature. We lead this opportunity to enrich blockchain with efficient spatial keyword data. We introduce spatial keyword index for block (SKIB), which is a cryptographically signed tree, thus maintaining the storage and integrity of original data from its spatial topological contexts. To demonstrate our work, we implement both textual first and spatial first pruning techniques. The comprehensive evaluation shows that SKIB provides efficient spatial keyword data processing on blockchains.
  Keywords: blockchain; query processing; spatial keyword data; indexing.
  DOI: 10.1504/IJICS.2022.10047992
   
  
• A Message Encryption Scheme inspired by Sudoku Puzzle (MESP)  
  by Shadi Masadeh, Hamza Abbass Al-Sewadi, Mohammed Abbas Fadhil Al-Husainy 
  Abstract: Lightweight cryptographic systems are recently sought for applications with limited computation capabilities, storage space, and power constraints. Such applications include Radio-frequency identification devices, remote sensing networks, health care instruments, smart cards, etc. This paper proposes a lightweight symmetric stream cipher cryptosystem that is inspired by the Japanese game
  Keywords: symmetric cryptosystems; Sudoku puzzle; pseudo-random numbers; lightweight cryptography; dynamic permutation.
  DOI: 10.1504/IJICS.2022.10047993
   
  
• Formal Verification of Software-only Mechanisms for Live Migration of SGX Enclaves  
  by Oualid Demigha, Nabil Haddad 
  Abstract: Live migration is not supported by current Intel?R SGX implementations. So, software emulation is unavoidable to enable deployed hypervisors migrating live virtual machines running SGX enclaves in the cloud. However, copying the running state of an enclave requires read/write of enclave’s memory from outside, which is impossible. Therefore, software-only mechanisms, as opposed to hardware extensions, are not transparent to the virtual machines, and need to deduce some hardware-defined metadata by emulation. Also, they need to synchronise enclaves’ stop/resume between two remote platforms. In this paper, we formally verify the only solution proposed in the literature that uses such a mechanism, in which we identify a severe critical situation where the whole live migration process gets stuck. Moreover and due to a specification flaw, we determine a condition that leads to the violation of instance uniqueness of enclaves. That may induce vulnerabilities for fork and rollback attacks. To remedy to these design shortcomings, we propose some easy-to-implement solutions to remove deadlock situations and ensure state uniqueness at the expense of a longer downtime.
  Keywords: trusted execution environment; trust cloud computing; Intel?R SGX; hardware-assisted security; SPIN; PROMELA; model-checking; live migration; fork attack; rollback attack.
  DOI: 10.1504/IJICS.2022.10048819
   
  
• Sentiment Analysis in Social network data using Multilayer Perceptron Neural Network with Hill-Climbing Meta-Heuristic Optimization  
  by Samson Ebenezar Uthirapathy, Domnic Sandanam 
  Abstract: Social networks such as Twitter, Facebook, and Instagram are the most widely used communication media in which users share their feelings and opinions about current events in society. For example, the occurrence of COVID19, its causes, symptoms, precautions, safety measures, prohibitions, etc. This work proposes a multi-class sentiment classification model to classify the tweets under various polarisation categories of the social network data. For Twitter data classification, this work proposes a model based on a multilayer perceptron neural network with hill-climbing optimisation. The heuristic hill climbing is used at the backpropagation for learning. TF-ID method is used for feature extraction from the dataset. This work compares the proposed method with the existing sentiment classification models of social network data. The proposed multi-class sentiment classification model has shown improvements in performance with the measures namely f1-score, precision, accuracy and recall over the existing sentiment classification models.
  Keywords: sentiment analysis; Twitter; classification; hill-climbing algorithm; optimisation; multilayer perceptron; MLP; coronavirus.
  DOI: 10.1504/IJICS.2022.10049056
   
  
• A Secure Access control model based on Improved Attribute-Based Cryptosystem in Cloud Environment  
  by Garima Verma, Ashutosh Kumar 
  Abstract: To handle cloud security challenges, an improved access control system has been proposed for secure data and resource access using attribute-based encryption (ABE) to handle the above-discussed challenges. The owner will broadcast the message and specific users with certain attributes and parameters will be able to access that message. At the initial stage, the encryption will be done based on the attributes of the users. Then, a secret share of each user will be created as per their identity information. Then, the decryption key and ciphertext share will be distributed into the distributed hash table (DHT). Finally, the authorised group of users can extract the broadcasted ciphertext only by using secret shares and required attributes. The results of this experimental study are further compared with some existing state of art systems. It has been found that the proposed model is more effective than other schemes.
  Keywords: security; confidentiality; identity; attribute-based encryption; ABE; distributed hash table; DHT; ciphertext; biometrics; attacks.
  DOI: 10.1504/IJICS.2022.10049533
   
  
• On Construction of Weighted Orthogonal Matrices over Finite Field and its Application in Cryptography  
  by Shipra Kumari, Hrishikesh Mahato 
  Abstract: In this article, we propose a method to construct self-orthogonal matrix, orthogonal matrix and antiorthogonal matrix over the finite field. Orthogonal matrices have numerous applications in cryptography, so here we demonstrate the application of weighted orthogonal matrix into cryptography. Using the proposed method of construction we see that it is very easy to transmit the private key and can easily convert the encrypted message into original message and at the same time it will be difficult to get the key matrix for intruder.
  Keywords: orthogonal matrix; self-orthogonal matrix; antiorthogonal matrix; finite field; Hill ciphers.
  DOI: 10.1504/IJICS.2022.10049669
   
  
• Lightweight Integrated Cryptosystem based on Reconfigurable Hardware for IoT Security  
  by Abiy Tadesse, Yalemzewd Negash, Suresh Kumar Pendem 
  Abstract: Small footprint security protocols are essential for trustworthy information exchanges in IoT network. Cryptography is a crucial security technique, but a single algorithm is insufficient to address the security requirements. Moreover, it is important to scale existing complex cryptosystems considering the resource, performance and security requirements of applications. In this research, lightweight and efficient cryptographic architectures are proposed and implemented based on FPGA targeting IoT security. The major contributions include FPGA-based small footprint architecture for an authenticated encryption algorithm, efficient and lightweight architecture for elliptic curve point multiplication of ECC, and an integrated cryptosystem architecture using only two algorithms with robust security, reduced hardware complexity, less space, and minimised key management and key storage issues. It involves prevention techniques against attacks including side-channel attacks. Compared to existing outcomes, 55% reduction of slices utilisation for the integrated system, 15% and 15.7% throughput improvements for ECC and ASCON implementations, respectively, are achieved.
  Keywords: authenticated encryption with associated data; AEAD; BRAM; DSP slices; ECC; fog-based IoT; FPGA; information security; integrated cryptosystem.
  DOI: 10.1504/IJICS.2023.10050225
   
  
• Performance analysis of privacy preservation based authentication scheme and cryptographic-based data protocols for DSaC in cloud  
  by Ankush Balaram Pawar, Shashikant U. Ghumbre, Rashmi M. Jogdand 
  Abstract: Advanced technology is the cloud computing that has the ability to preserve massive amount of data and shares the data resources over the internet. Cloud computing has a wide range of applications like business fields, software infrastructure, government agencies, and financial industries. The main challenging factors in cloud computing are data privacy and security. Such issues are addressed through the developed method named privacy preservation-based data security that secure protocol for data storage that is distributed and communication (DSaC) in the cloud such that this method provides effective authentication and secure data storage in cloud. An effective performance analysis is done between done between privacy preservation-based data security approach for authenticated encrypted access and secure data protocol for DSaC in cloud and the approaches are compared with the traditional schemes like Ins-PAbAC, homomorphic proxy re-encryption (HPRE), LAM-CIoT, SA-EDS, advanced encryption standard (AES) and data encryption standard (DES).
  Keywords: cloud computing; privacy preservation; authentication; data encryption; data security; data storage and communication; DSaC.
  DOI: 10.1504/IJICS.2023.10050594
   
  
• A Comprehensive Survey on Effective Key Management schemes for secured Authentication in Vehicular Ad Hoc Network  
  by Jayashree S, Santhosh Kumar SVN 
  Abstract: ad hoc network (VANET) refers to the network structure of ad hoc networks where different moving vehicles of different mobility communicate with each other over the wireless medium to exchange the information with one another. Since, communication in VANET takes place by using wireless medium which is open channel and it is vulnerable to various types of attacks during the data transmission. Hence, providing efficient authentication to the VANET is one of the important challenges needed to be addressed. Among all the security requirements, this work focuses on providing the comprehensive analysis on various authentication schemes employed for providing efficient authentication in the VANET. In this paper, a comprehensive analysis on various authentication schemes in VANET is carried out to highlight their advantages and limitations. Moreover, this paper also provides detailed analysis of various authentication protocols with respect to anonymity, integrity, reliability and authenticity to pave the way for the future researchers for designing efficient authentication protocol for achieving secure communication in VANET.
  Keywords: vehicular ad hoc network; VANET; key management; authentication and security.
  DOI: 10.1504/IJICS.2023.10050597
   
  
• A Secure Three-Factor Authentication Protocol for Mobile Networks  
  by Devender Kumar, Satish Chand, Bijendra Kumar 
  Abstract: User authentication is a necessary mechanism to communicate securely for mobile networks Recently, Xie et al have discussed a three-factor authentication (3FA) scheme using ECC for mobile networks and claimed that it is secure even if the user's two factors are known to the attacker However, in this paper, we cryptanalyze their scheme and find the off-line password guessing and user impersonation attacks in it We also propose a secure 3FA scheme for mobile networks using ECC by removing the weaknesses of their scheme We show the formal security verification of the proposed scheme using the ProVerif tool We discuss its informal security analysis to show that it is resistant to the various known attacks We also present its performance analysis along with the related schemes in terms of computational cost and security features, and show that it offers more security features as compared to the related schemes.
  Keywords: Authentication; user impersonation attack; biometrics; off-line password guessing attack;  mutual authentication.
  DOI: 10.1504/IJICS.2023.10050706
   
  
• HEMC: A Dynamic Behavior Analysis System for Malware based on Hardware Virtualization  
  by ZHENQUAN DING, H.U.I. XU, L.E.I. CUI, HAIQIANG FEI, YONGJI LIU, ZHIYU HAO 
  Abstract: Since many malwares disguise themselves by encrypting, obfuscating and recompiling, it is not easy for static analysis methods to recognise new or unknown malwares. This paper proposes a novel dynamic analysis technology based on hardware virtualisation to analyse more malwares with lower computational resources. Firstly, it intercepts the system-call functions to achieve on-demand behaviour analysis by setting special permissions in their physical addresses, which can be dynamically acquired when system-call functions are loaded into memory, as well as only monitoring high-risk functions, which take a small part of the whole functions. Then, this paper utilises copy-on-write technique and incremental image capability to reduce hard drive consumption and hard disk replication time. Finally, this paper proposes a novel approach to capture the return value of system-call functions to deeply analyse the poisoned results of malware samples. Meanwhile, a prototype system, called HEMC, is implemented based on QEMU/KVM. The experiments demonstrate that proposed methods outperform existing methods in efficiency and performance on malware dynamic analysis.
  Keywords: malware; dynamic analysis; hardware virtualisation; high-risk functions.
  DOI: 10.1504/IJICS.2023.10050989
   
  
• A Novel Stream Cipher Based on Quasigroups and QG-PRNG  
  by Umesh Kumar, V. Ch. Venkaiah 
  Abstract: Stream ciphers that use the XOR-function are vulnerable to known-plaintext and reused-key attacks. To overcome such shortcomings of the existing ciphers, we hereby propose a novel stream cipher based on a quasigroup and a pseudo-random number generator (QG-PRNG). The QG-PRNG is also defined in this paper. Novelty of the proposed cipher is that a keystream once generated can be reused multiple times. The proposed cipher is analysed against various attacks, including reused-key, chosen-ciphertext, chosen-plaintext, and known-plaintext attacks and found it to be resistant to these attacks. The proposed cipher and QG-PRNG are implemented in C++, and the performance of the proposed cipher is compared with some existing quasigroup-based stream ciphers, finding that the proposed cipher is more efficient than the existing proposals. We also evaluated both the QG-PRNG and the proposed cipher using various statistical tests of the NIST-STS, and we found that both the schemes pass these tests.
  Keywords: cryptography; Latin square; NIST test; QG-PRNG; quasigroup; stream cipher.
  DOI: 10.1504/IJICS.2023.10050991
   
  
• Trilinear Pairing-Based Cryptosystem Authentication: For Electronic Health Record Security in Healthcare System  
  by RAJA SOHAIL AHMED LARIK, Yongli Wang, Irfan Ali Kandhro, Nabila Sehito, Ghulam Ali Mallah, Fayyaz Ali 
  Abstract: Electronic health record (EHR) provides us medical information about the patient and provides the overall history of the individual. EHR works automatically. It gives access to health records and workflow of physician. Authentication, timeliness and accessibility to information between doctors and patients are also maintained by this healthcare cryptosystem. The strong cryptosystem consists of doctors’ and patients’ authentic interaction. Hence, we propose trilinear pairing-based cryptosystem security and authentication using elliptic curve cryptography (ECC) and elliptic curve discrete logarithm problem (ECDLP) techniques. Our proposed model authenticates the patients and doctors by key sharing, transmission and authentication to secure the healthcare system. The analysis outcome shows in the performance, accuracy, time and attacks.
  Keywords: electronic health record; EHR; elliptic curve cryptography; ECC; key sharing; authentication; security.
  DOI: 10.1504/IJICS.2023.10051700
   
  
• Cryptanalysis of Common Prime RSA with Two Decryption Exponents: Jochemsz and May Approach  
  by Ravva Santosh Kumar, Srm Krishna 
  Abstract: RSA is a well-known public key cryptosystem in modern-day cryptography. Common prime RSA (CP-RSA) is a variant of RSA which is introduced by Wiener to avoid the small secret exponent attack on RSA. Lattice-based reduction algorithms were successfully used for cryptanalysis for RSA and its variants. In this paper, we mount an attack on CP-RSA by following the Jochemsz and May approach. Jochemsz and May approach is the standard way to construct the lattices for the attacks on RSA and its variants. Our attack improves the bounds of attacks on standard RSA and CP-RSA.
  Keywords: CP-RSA; Jochemsz and May approach; cryptanalysis; RSA; lattice reduction.
  DOI: 10.1504/IJICS.2023.10051701
   
  
• Performance Evaluation of Various Deep Convolutional Neural Network Models Through Classification of Malware  
  by Zareen Tasneem, Maria Afnan, Md Arman Hossain, Md. Mahbubur Rahman, Samrat Kumar Dey 
  Abstract: Malware, a collective name for malicious programs, is a piece of software, system or scripts, causing damage to the system. Lately, use of internet has favored criminal activities like malware assaults. Hence malware classification comes in the first line of defense. Machine learning (ML) techniques have drawn attention to malware classifiers over all other techniques in the last decade. Very little investigation highlights the results of the existing studies in malware classification using ML approach. The progress is slow due to difficulties of developing a deep learning system: dataset collection, labelling, feature extraction, model construction, training and testing the models, and evaluation. A systematic way of summarising the current knowledge also lacks in latest methods. This study utilises a systematic literature review and presents implementation of different CNN models for malware classification into their respective families. Its objective is to analyse the most popular architectures for and evaluate their results.
  Keywords: malware; classification; image; deep learning; convolutional neural network.
  DOI: 10.1504/IJICS.2023.10052186
   
  
• PBDG: A Malicious Code Detection Method Based on Precise Behavior Dependency Graph  
  by Chenghua Tang, Mengmeng Yang, Qingze Gao, Baohua Qiang 
  Abstract: Using behaviour association or dependency to detect malicious code can improve the recognition rate of malicious code. A malicious code detection method based on precise behaviour dependency graph (PBDG) is proposed. We create a stain file index by filtering the stain source blacklist, which not only saves storage space, but also quickly locates instructions. An active variable path verification algorithm is proposed to verify and purify the Source?Sink path. The PBDG and its matching algorithm are constructed to identify the malicious code family of the source program. The experimental results on six data sets show the effectiveness of this method. The introduction of active variable paths reduces the number of paths that need to be traversed by 91.2% at most. In terms of the detection effect of malicious code, especially for web applications, it has a good detection accuracy and a low false positive rate.
  Keywords: malicious code; stain file; path space; behaviour dependency graph; vulnerability detection.
  DOI: 10.1504/IJICS.2023.10052187
   
  
• Re-Evaluation of PhishI Game and its utilization in Eliciting Security Requirements  
  by Rubia Fatima, Affan Yasin, Lin Liu, Wang Jianmin 
  Abstract: The COVID-19 coronavirus pandemic has sparked considerable alarm amongst the general community and has significantly affected the societal attitudes and perceptions. In the current era, social engineers are applying various strategies to exploit human weakness. Phishing, a social engineering technique, is one of the most widely used and effective ways to undermine human assets. In this research study, firstly, we aim to educate the participants regarding phishing attacks; secondly, the dangers associated with excessive online sharing, and thirdly, how to utilise game scenarios developed by the participants to elicit security requirements. We have employed various research methods, such as, survey, observation, personas development, and scenario-based technique to achieve these objectives. Our re-evaluation results show that the PhishI game effectively educates participants regarding phishing attacks and dangers associated with disclosing excessive online information.
  Keywords: social engineering; phishing attack; awareness; security requirements elicitation; serious game; online information disclosure; human factor.
  DOI: 10.1504/IJICS.2023.10052188
   
  
• Deep multi-locality convolutional neural network for DDoS detection in smart home IoT.  
  by Mohammed Almehdhar, Mohammed M. Abdelsamea, Ruan Na 
  Abstract: Internet of things (IoT) devices usually offer limited resources such as processing, memory, and network capacity, bringing more security threats to the environment. Distributed denial of service (DDoS) signal attacks are among the most serious threats. Software-defined networking (SDN) is a promising paradigm that could offer a scalable security solution optimised for the IoT ecosystem. However, investigating a robust security solution is still one of the most challenging problems that a smart home environment faces in SDN. In this paper, we introduce a multi-locality deep learning model for the detection of DDoS signals in an SDN-based smart home. It employs convolutional neural networks (CNNs) by learning different levels of local information from the data. In this work, an ensemble of two CNNs to detect malicious traffic flows with low computation overhead framework is proposed. Experimental results demonstrate the robustness, effectiveness, and efficiency of our solution in detecting DDoS attacks in SDN smart home.
  Keywords: smart home; internet of things; IoT; deep convolutional neural networks; distributed denial of service; DDoS.
  DOI: 10.1504/IJICS.2023.10052345
   
  
• User Driven General Framework to Cap the Joins in Secure Group Communication  
  by Payal Sharma, Purushothama B. R 
  Abstract: In the literature, secure group key management schemes have focused on either rekeying cost or security requirements: forward and backward secrecy. There is little or no work that adds new features to the secure group key management scheme. In the existing key management schemes, any user can join or leave the group any number of times during the group's lifetime. There is a need to restrict the number of times a user joins the group during the group's lifetime. We propose a user-driven general framework wherein a cap is enforced on the number of times a user can join the group. Any existing key management scheme can use this framework. In the proposed scheme, the user is prevented from joining a group, say, more than t>0 times. We analyze the scheme and show that the proposed scheme indeed caps the number of times a user can join the group.
  Keywords: Secure Group Communication; Entry Restriction; Shamir's Secret Sharing; Verifiable Secret Sharing.
  DOI: 10.1504/IJICS.2023.10052719
   
  
• Image Tampering Detection based on Feature Consistency Attention  
  by Gu Junlin, Xu Yihan, Sun Juan, Liu Weiwei 
  Abstract: Recently, the development of computer image technology makes image tampering more and more convenient, causing a large number of image tampering accidents. The existing scheme uses manual features and depth features to analyse the forgery traces, and has achieved good results. However, the existing schemes lack the analysis of essential traces and have defects in generalisation performance. In this paper, an image tampering detection scheme based on feature consistency attention is proposed. The inconsistency between the real region and the background region is used to improve the detection ability of the algorithm for unknown images. The scheme uses the feature extraction module to extract the deep semantic features of the image, and then calculates the feature correlation between the tampered region and the background region to maximise the correlation within the region and minimise the correlation between the background region and the tampered region. The scheme can learn the common traces of tampering process, which is expected to achieve better generalisation effect. Experimental results show that the proposed scheme is superior to several existing schemes in detecting tampered images.
  Keywords: image tampering detection; deep neural network; feature consistency.
  DOI: 10.1504/IJICS.2023.10053595
   
  
• A New hybrid chaotic System and its analysis  
  by Mandeep Kaur Sandhu, Surender Singh, Manjit Kaur 
  Abstract: In this paper, we introduce and examine a new hybrid hyper-chaotic system named eight-dimensional (8D) hyper-chaotic system. The proposed system is designed by combining the hyper-chaotic systems with some modifications. 8D hyper-chaotic system is sensitive to its initial parameters. Therefore, a grey wolf optimisation algorithm is used to tune its initial parameters. The proposed 8D hyper-chaotic system shows hyper-chaotic behaviour and a unique equilibrium with a large range of parameters and six positive Lyapunov exponents. The analyses of the proposed 8D hyper-chaotic system are achieved using amplitude fluctuations, auto and cross-correlation, pseudo phase space trajectories, and equilibrium points. Besides, Lyapunov exponents, bifurcation diagrams, and butterfly effects are also evaluated by defining the initial conditions and specific parameters. Experimental analyses reveal the presence of chaotic and hyper-chaotic attractors, high accuracy, and stable performance of the proposed 8D hyper-chaotic system. Finally, the proposed 8D hyper-chaotic system is used to design the secret key for showing the application in the field of image encryption. It provides a large keyspace and can resist various security attacks.
  Keywords: hyper-chaotic; chaotic; Lyapunov exponents; bifurcation.
  DOI: 10.1504/IJICS.2023.10053890
   
  
• On the Performance of AES Algorithm Variants  
  by Mohammed N. Alenezi, Haneen Alabdulrazzaq, Hajed M. Alhatlani, Faisal A. S. AlObaid 
  Abstract: Advanced encryption standard (AES) is frequently used to encrypt data transmission over the internet since it is not prone to practical attacks. Many variants of AES exist with different key sizes and block cipher modes. Choosing an AES variant depends on several factors such as speed, the extent of security required, and the type of the application. As such, it becomes vital to test the performance of these variants to help users choose the most suitable one for their needs. This research presents a performance evaluation of encryption/decryption time and throughput of AES-128, AES-192, AES-256 using modes such as CTR, CBC, CFB, and OFB in Python on various file sizes ranging from 1 MB to 50 MB. The results show a natural pattern where an increase in key size and/or file size prolonged encryption and decryption time. Furthermore, CBC mode was the highest in performance for all key sizes.
  Keywords: security; advanced encryption standard; AES; cryptographic algorithms; block cipher modes; throughput.
  DOI: 10.1504/IJICS.2023.10054850
   
  
• A new architecture with a new protocol for m-payment  
  by BOUKERS Saâd, Abdelkader Belkhir 
  Abstract: The use of mobile payments by a significant number of consumers in recent years has encouraged without exception all other m-payment actors to invest more in this area. The reasons for this use are diverse and multiple. At the same time, this new situation requires all mobile payment actors to increase their cyber resilience in front of the rapid evolution of the techniques used by cybercriminals to properly secure financial transactions and consumer data in order to further increase the popularity of this payment means in the world. In this context, our contribution consists in offering a new architecture with a new protocol for mobile payment that further strengthens confidence in m-payment. Our solution is universal in its design and implementation and uses the SMS service. It is mainly based on the public key infrastructure (PKI), the session key and national and international digital identities of consumers and merchants.
  Keywords: mobile payment; SMS; digital identity; public key infrastructure; PKI; encryption; digital certificate; certification authority; block cipher; end-to-end encryption.
  DOI: 10.1504/IJICS.2023.10054871
   
  
• A Bio-Inspired Algorithm for enhancing DNA Cryptography  
  by Kheira Lakel, Fatima Bendella 
  Abstract: In this era, information security plays a crucial and sensitive task as this data is potentially vulnerable such that different types of attacks may happen and affects the data. This paper presents a new hybrid cryptosystem for DNA cryptography based on GA and a coding table. The encryption algorithm provides multi-layer security (jamming with spiral matrix, generating coding table, coding of DNA characters, XOR-crossover operation) for DNA sequence. The decryption algorithm follows these steps: binary and segment the ciphertext, applied XOR-crossover operation, Transform each block to ASCII code, decoding of characters, remove jamming and generate the plaintext. The performance evaluation of this algorithm is based on confusion and diffusion, avalanche effect, and encryption time. The experimental results show that these algorithms yield an average time 0.835 ms/0.78 ms for 1,000 bases. The result shows outperformance in security and a weak correlation coefficient between ciphertexts generated and plaintext.
  Keywords: information security; DNA; encryption; decryption; cryptography; one time pad; ASCII code; jamming; scrambling; coding table.
  DOI: 10.1504/IJICS.2023.10055396
   
  
• CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with ML  
  by Synim Selimi, Blerim Rexha, Kamer Vishi 
  Abstract: The rapid evolution of the internet, particularly the emergence of Web3, has transformed the ways people interact and share data. Web3, although still not well defined, is thought to be a return to the decentralisation of corporations’ power over user data. Despite the obsolescence of the idea of building systems to detect and prevent cyber intrusions, this is still a topic of interest. This paper proposes a novel conceptual approach for implementing decentralised collaborative intrusion detection networks (CIDN) through a proof-of-concept. The study employs an analytical and comparative methodology, examining the synergy between cutting-edge Web3 technologies and information security. The proposed model incorporates blockchain concepts, cyber non-fungible token (cyberNFT) rewards, machine learning algorithms, and publish/subscribe architectures. Finally, the paper discusses the strengths and limitations of the proposed system, offering insights into the potential of decentralised cybersecurity models.
  Keywords: decentralisation; blockchain; Web3; intrusion detection; machine learning; non-fungible token; NFT; cyber security; cyberNFT; publish-subcribe systems.
  DOI: 10.1504/IJICS.2023.10055729
   
  
• On Generating New Key-Dependent XOR Tables to Improve AES Security and Evaluating the Randomness of the Output of Block ciphers  
  by Luong Tran Thi, Linh Hoang Dinh 
  Abstract: Although block ciphers are widely used and are quite secure, there are still many types of attacks against components of block ciphers, and the Advanced Encryption Standard (AES) block cipher has no exception. To improve the security of AES, there have been many studies in the literature on methods of making this block cipher dynamic. There have been many works focused on the methods of making dynamic at the S-box and the MixColumn transformation of AES. In this paper, we propose a method to make dynamic at the Addroundkey transformation of AES using new key-dependent XOR tables. We also propose a procedure to evaluate the randomness of the output of a block cipher and apply this procedure to evaluate the randomness of the modified AES block cipher using new XOR tables. The proposed dynamic method based on new XOR tables can help improve the security of the AES block cipher against many of today’s strong attacks on block ciphers.
  Keywords: XOR table; Advanced Encryption Standard; AES; modified AES; randomness assessment.
  DOI: 10.1504/IJICS.2023.10055730
   
  
• A Robust Feature Points Based Screen-shooting Resilient Watermarking Scheme  
  by Ruixia Yan, Yuan Jia, Lin Gao 
  Abstract: Screen-shooting will lead to information leakage. Anti screen-shooting watermark, which can track the leaking sources and protect the copyrights of images, plays an important role in image information security. Due to the randomness of shooting distance and angle, more robust watermark algorithms are needed to resist the mixed attack generated by screen-shooting. A robust digital watermarking algorithm that is resistant to screen-shooting is proposed in this paper. We use improved Harris-Laplace algorithm to detect the image feature points and embed the watermark into the feature domain. In this paper, all test images are selected on the dataset USC-SIPI and six related common algorithms are used for performance comparison. The experimental results show that within a certain range of shooting distance and angle, this algorithm presented can not only extract the watermark effectively but also ensure the most basic invisibility of watermark. Therefore, the algorithm has good robustness for anti screen-shooting.
  Keywords: blind watermarking; screen-shooting; robustness; invisibility; feature points; QR code; discrete cosine transform; DCT.
  DOI: 10.1504/IJICS.2023.10056328
   
  
• Technique for Detecting Hardware-Based Trojans Using a Convolutional Neural Network  
  by Ravichandran C, Nagalakshmi T.J., Shyamala Bharathi P, Siva Kumaran 
  Abstract: The hardware Trojan, also known as HT, has emerged as a danger to the integrated circuit (IC) sector and the supply chain, leading to the creation of a plethora of Trojan detection strategies. The detection of HT is very necessary to ensure both the chip’s functionality and its safety. This article discusses a recently discovered risk to integrated circuits (ICs) safety. Using a deep convolutional neural network, the authors of this research offer a novel partial RE-based HT detection algorithm. This method can identify Trojan horses in IC layout photos (DCNN). The suggested DCNN model is made up of many convolutional and pooling layers that are stacked on top of one another. By giving proof of concept implementation of the various approaches to FPGAs, we demonstrate the practicability of the presented strategies by demonstrating how they may be implemented.
  Keywords: hardware Trojan; security; deep neural network; FPGA.
  DOI: 10.1504/IJICS.2023.10056329
   
  
• SLAK: Secure Lightweight scheme for Authentication and Key-agreement in Internet of Things  
  by Oussama Nahnah, Sarra Cherbal 
  Abstract: Internet of things connect unlimited number of heterogeneous devices in order to facilitate services and hence touching most of daily life fields. However, security concerns are a major obstacle to the development and rapid deployment of this high technology. Thus, securing the authentication process has become very important, as it is necessary to prove the legitimacy of the communication devices. Recently, researchers are proposing several mutual authentication and session key agreement protocols. In this regard, we propose our own improved protocol that relies on login, mutual authentication and the agreement of session key in a safety way to secure communications. For the security evaluation of the proposal, we use the authentication BAN logic and the widely used AVISPA tool. The results prove the achievement of mutual authentication and session key agreement securely. In addition to its safety against some known attacks as eavesdropping and replay attacks. For a performance evaluation, we compare the proposal with recent related works in terms of computational and communication costs. The results show the lightness of our protocol and thus its suitability to heterogeneous IoT devices.
  Keywords: authentication; internet of things; security; elliptic curve cryptography; session key; AVISPA.
  DOI: 10.1504/IJICS.2023.10056330