Enhanced Security in Smart City GAN-Based Intrusion Detection Systems in WSNs

Abstract

As intelligent urban centers continue to evolve, the reliance on wireless type sensory networks (WSNs) for data samples collections and message interaction will become paramount. However, the increasingly complexity of the networks may demand robust security measures to safeguarding against potential intrusions. In response, this chapter introduces IntelligentGuard, a novelistic intrusion identification system leverages generative based adversarial networks (GANs) for enhancement in security in WSNs within intelligent urban centers. IntelligentGuard will employs machine learning-driven techniques, including supervises learning algorithms such as support vector supportive machines (SVMs) and decision-based trees, to discern normal network behavior from anomalous patterns, thus fortifying the WSN against various intrusion scenarios. The proposed system's GAN-based architecture not only enhances identification accuracy but also adapts dynamically to evolving threat landscapes.