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Adversarial Defense Mechanisms for Detecting and Mitigating Cyber-Attacks in Wireless Sensor Networks
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Author(s): Patel Janit Umeshbhai (LJ University, India), Panchal Yash Kanubhai (LJ University, India), Shaikh Mohammed Bilal (LJ University, India)and Shanti Verma (LJ University, India)
Copyright: 2024
Pages: 13
Source title:
Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)
Source Author(s)/Editor(s): Sivaram Ponnusamy (Sandip University, Nashik, India), Jilali Antari (Ibn Zohr Agadir University, Morocco), Pawan R. Bhaladhare (Sandip University, Nashik, India), Amol D. Potgantwar (Sandip University, Nashik, India)and Swaminathan Kalyanaraman (Anna University, Trichy, India)
DOI: 10.4018/979-8-3693-3597-0.ch003
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Abstract
This chapter introduces ML-Defend, a novel defense mechanism tailored for detecting and mitigating cyber-attacks in wireless sensor networks (WSNs). By combining support vector machines (SVMs) for anomaly detection and convolutional neural networks (CNNs) for classification, ML-Defend harnesses the complementary strengths of these algorithms. This hybrid approach significantly enhances the system's ability to detect and mitigate cyber threats, thereby bolstering the security of WSNs. Through simulations and experiments, the authors demonstrate the efficacy of ML-Defend in accurately identifying and neutralizing attacks while minimizing false positives. This amalgamation of SVMs and CNNs presents a promising avenue for bolstering the cyber-defense capabilities of wireless sensor networks, ensuring their resilience against evolving threats in dynamic environments.
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