An efficient blockchain‐based approach to improve the accuracy of intrusion detection systems

• Published in: Electronics letters Vol. 59; no. 18
• Main Authors: Abubakar, Aliyu Ahmed, Liu, Jinshuo, Gilliard, Ezekia
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01.09.2023; Wiley
• Subjects: Accuracy; Algorithms; Artificial intelligence; Blockchain; Communications traffic; computer network security; Cybersecurity; Human error; interactive systems; Intrusion detection systems; learning (artificial intelligence); Linear programming; Machine learning; Network security; Real time; Research methodology; Support vector machines; wireless communications
• ISSN: 0013-5194; 1350-911X
• DOI: 10.1049/ell2.12888

Abstract Intrusion Detection System (IDS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false‐positive rates. To improve the accuracy of an IDS, blockchain technology can be used as it provides a secure, decentralized, immutable ledger that can track suspicious activity over time and also identify intrusions globally. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based IDS. The approach combines different intrusion detection algorithms using a blockchain‐integrated architecture. It is based on the fusion principle and weighted votes, which the authors used to determine their results. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rates, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based intrusion detection and prevention systems, which is based on combining different intrusion detection algorithms and using a blockchain‐integrated architecture. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rate, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks.