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

Abstract

SummaryIntrusion 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.