An improved federated transfer learning model for intrusion detection in edge computing empowered wireless sensor networks

Abstract

Intrusion Detection (ID) is a critical component in cybersecurity, tasked with identifying and thwarting unauthorized access or malicious activities within networked systems. The advent of Edge Computing (EC) has introduced a paradigm shift, empowering Wireless Sensor Networks (WSNs) with decentralized processing capabilities. However, this transition presents new challenges for ID due to the dynamic and resource-constrained nature of Edge environments. In response to these challenges, this study presents a pioneering approach: an Improved Federated Transfer Learning Model. This model integrates a pre-trained ResNet-18 for transfer learning with a meticulously designed Convolutional Neural Network (CNN), tailored to the intricacies of the NSL-KDD dataset. The collaborative synergy of these models culminates in an Intrusion Detection System (IDS) with an impressive accuracy of 96.54%. Implemented in Python, the proposed model not only demonstrates its technical prowess but also underscores its practical applicability in fortifying EC-empowered WSNs against evolving security threats. This research contributes to the ongoing discourse on enhancing cybersecurity measures within emerging computing paradigms.