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

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.

Intelligent load balancing in data center software‐defined networks

“…Addressing hardware constraints and seamless integration with existing network management systems will also be crucial for successful deployment. Additionally, we recommend investigating promising research directions for SDN‐based data centers, such as AI‐driven network management, Federated Learning for Decentralized SDN, cybersecurity considerations, and scalability and performance improvements 22,23 . These efforts will contribute to the ongoing evolution of data center networking, improving flexibility, programmability, and overall efficiency in network operations.…”