Security and privacy-aware Artificial Intrusion Detection System using Federated Machine Learning

Abstract

Beyond 5G networks integrating 5G technology offer efficient services globally with sustainable higher capacity and much lower latency across various applications. Moving into the beyond 5G networks, edge intelligence solutions with data-driven machine learning algorithms and cybersecurity paradigms have become crucial among various real-time applications, including smart transportation, smart health, etc. Since the data gets transferred continuously from the edge devices to the dedicated computing in any edge computing environment, it experiences a higher risk of vulnerability and complexity measures. In this view, this paper firstly defines a federated machine learning mechanism for privacy-enhanced edge intelligence model Beyond 5G networks with Paillier Homomorphic Encryption and differential privacy. Secondly, an Artificial Immune Intrusion Detection System has been designed to monitor and classify the nodes resulting in an anomaly in the edge network so that the network can experience smooth and secure data transmission as per the requirement. The experiments and comparison results show that the proposed system is more optimal and secure than the existing edge security models.