Authors - Bhavya Balakrishnan, Srinivasa HP Abstract - The massive deployment of heterogeneous, resource- con-strained and always-on devices underlying the Internet of Things (IoT) has introduced complex cybersecurity challenges. The rapid growth of the Internet of Things (IoT) due to the large-scale deployment of heterogeneous, resource-constrained and always-on devices has resulted in complex cybersecurity challenges. The physical and digital components in the IoT systems are tightly bound which increases the attack sur-face and makes them highly prone to threats of malware infections, data theft, unauthorized access and distributed denial of service. Traditional security mechanisms and rule-based intrusion detection systems cannot manage the dynamic, large-volume and evolving IoT traffic. The solutions provided by machine learning have been widely concerned due to its capability of learning data patterns and finding abnormal and malicious activities. However, existing machine learning models have serious constraints such as lack of labelled information, extreme class imbalance, and inability to generalize to new and never-seen attacks. In recent years, Generative Adversarial Networks (GANs) have emerged as a promising paradigm to improve the cybersecurity of IoT through artificial generation of realistic synthetic data, adversarial sample enhancement, alleviating data imbalance and modelling adversarial attack-defense dynamics. GAN based models have showed great gains in intrusion detection, anomaly detection and malware analysis in the IoT networks . However, modern studies are still divided on this issue due to variations in GAN architectures, datasets, evaluation procedures, and experimental procedures. In addition, most of the researches have been more concentrated on offline benchmark databases, with less focus on checking through realistic IoT testbeds, which could be more precise in capturing the actual deployment conditions.
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