A Study on Machine Learning and Cryptographic based Intrusion Detection System in Blockchain and Data Science Environment

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 02 | Page :
By

Varun Mishra,

Upendra Kumar,

Mohd Haris,

Divyanshu Chauhan,

  1. Student, Computer Science and Engineering, Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, India
  2. Assistant Professor, Computer Science and Engineering, Institute of Engineering and Technology, Uttar Pradesh, India
  3. Project Research Scientist, Computer Science and Engineering, Institute of Engineering and Technology, Uttar Pradesh, India
  4. Research Engineer, Computer Science and Engineering, Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, India

Abstract

The recent speedy development of Internet of Things, vehicular networks and cloud computing has greatly boosted the need of effective and safe intrusion detection systems. The shortcomings of the traditional IDS-based approaches are centralized architecture, issue of scalability, and inability to analyze the encrypted and distributed data. To address these issues, the recent studies have paid attention to the incorporation of the latest technologies such as machine learning, deep learning, cryptographic solutions, federated learning, and blockchain. In this paper, a review of the IDS frameworks used in modern times, especially focusing on cryptographic IDS and blockchain-based federated learning IDS. The cryptographic techniques will ensure the confidentiality of the information, integrity, and authentication of the data, and ML and DL models will raise awareness of more advanced and evolving cyber threats. Federated learning can be used to train models in a decentralized fashion, using no raw data, preserving privacy, and blockchain provides transparency, immutability, and trust in a distributed environment. This paper compares the existing approaches in regards to methodology, performance measures and applications and places and describes the merits and demerits of the approaches. The key issues such as computational overheads, the latency of communication, scalability issues, and the vulnerability to the poisoning attacks are also discussed, and potential future research opportunities are also outlined to develop efficient and secure solutions to the IDS.

Keywords: Federated Learning, Blockchain, Intrusion Detection System, Graph Neural Network, Explainable Artificial Intelligence, Internet of Things, Industrial IoT, Cybersecurity

How to cite this article: Varun Mishra, Upendra Kumar, Mohd Haris, Divyanshu Chauhan. A Study on Machine Learning and Cryptographic based Intrusion Detection System in Blockchain and Data Science Environment. International Journal of Wireless Network Security. 2026; 04(02):-.
How to cite this URL: Varun Mishra, Upendra Kumar, Mohd Haris, Divyanshu Chauhan. A Study on Machine Learning and Cryptographic based Intrusion Detection System in Blockchain and Data Science Environment. International Journal of Wireless Network Security. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijwns/article=2026/view=260751

References

Ahead of Print Subscription Review Article
Volume 04
02
Received 20/07/2026
Accepted 27/07/2026
Published 06/10/2026
Publication Time 78 Days

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