Forthcoming and Online First Articles

International Journal of Internet Technology and Secured Transactions

International Journal of Internet Technology and Secured Transactions (IJITST)

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International Journal of Internet Technology and Secured Transactions (3 papers in press)

Regular Issues

  • Performance analysis of consensus protocols in distributed systems   Order a copy of this article
    by D. Sumathi, T. Poongodi, Ramasamy Lakshmana Kumar, Balamurugan Balusamy 
    Abstract: Consensus algorithms play the significant role in any blockchain system since it has to prove its performance and security. Hence, there is a need to identify the curbs of various consensus algorithms. The features of several kinds of blockchain systems rely on the consensus techniques. A detailed analysis of the algorithms has been done which might throw the limelight to researchers to devise a novel consensus algorithm. Issues due to the adoption of the consensus and solutions to overcome have been discussed. The main contribution of this work is to provide a deep insight into the consensus algorithms and its properties. There are several metrics considerations for evaluating the performance of consensus protocols. Additionally, the energy consumption of Bitcoin and Ethereum of several countries has also been presented in this work. Finally, the most commonly used consensus algorithm has been analysed along with its variant information.
    Keywords: blockchain; consensus algorithms; cryptocurrency; Bitcoin; Ethereum.
    DOI: 10.1504/IJITST.2023.10053139
  • Security of internet of vehicles in smart cities: authentication and confidentiality aspects   Order a copy of this article
    by Roumissa Sahbi, Salim Ghanemi, Mohamed Amine Ferrag 
    Abstract: With the rapid development of smart devices, communication technologies, and network elements, vehicles become more autonomous, more connected, and more intelligent. Internet of vehicles (IoV) is a new area of the internet of things (IoT) equipped with powerful multi sensors platform, connectivity and communication technologies. The communication and the cooperation between vehicles and different IoT objects can increase security risks and make vehicles more vulnerable to serious attacks (authentication attacks, confidentiality attacks, availability attacks, routing attacks, and data authenticity attacks). In this paper, we propose security schemes to detect and prevent attacks in IoV system using software defined network (SDN) and elliptic curve cryptography (ECC); first, we identify IoV concepts, then we design a network model which illustrates the global IoV architecture, after that we propose an authentication and confidentiality schemes. Finally, we provide formal and informal proof to demonstrate that our schemes can resist against various attacks.
    Keywords: internet of things; IoT; internet of vehicles; IoV; network model; security; authentication; confidentiality; attacks.
    DOI: 10.1504/IJITST.2023.10053899
  • An improved deep autoencoder-based network intrusion detection system with enhanced performance   Order a copy of this article
    by Bidyapati Thiyam, Shouvik Dey 
    Abstract: The evolutions in information and communication technology (ICT) devices have led to the rise in cyber-attacks in network systems. Ensuring cybersecurity in these devices is one of the most critical and complex issues. Besides, with technological improvement, hackers have been introducing complex and malicious malware attacks into the network system, making intrusion detection a complicated task. However, various conventional IDS face severe challenges to discern and counteract such risks. Statistical research and an enhanced autoencoder classifier for the network intrusion detection system are introduced in this paper (NIDS). This work uses an optimised autoencoder feature extraction process to extract correlated features to simplify the classification process. The proposed method is estimated using the NSL-KDD, UNSW-NB15, and CIC-DDoS2019 datasets, and its performance is compared to that of current machine learning algorithms. The results indicate that the proposed AE-NIDS outperforms shallow machine learning classifiers regarding detection accuracy and false positive rate.
    Keywords: network intrusion detection system; NIDS; machine learning; autoencoder; NSL-KDD dataset; UNSW-NB15 dataset; CIC-DDoS2019 dataset; statistical study.
    DOI: 10.1504/IJITST.2023.10056364