GRU-Based DDoS Detection for Enhanced Security in Consumer Electronics
Brij Bhooshan Gupta, Kwok Tai Chui, Akshat Gaurav, Varsha Arya · 2023
As IOT devices grow in popularity in the consumer electronics industry, protecting them from cybercriminals has emerged as a top priority. Distributed denial of service (DDoS) attacks, among other cyber threats, pose serious risks to the operation and accessibility of these networked devices. When it comes to complex DDoS attacks, traditional security measures are frequently inadequate, requiring the adoption of cutting-edge machine learning algorithms for detection and mitigation. In this research, we present a new method of DDoS detection based on Gated Recurrent Units (GRUs) to improve security for consumer devices in this setting. To train and check the accuracy of our GRU model, we use the KDD-Cup dataset. With an accuracy of 98%, the proposed GRU-based DDoS detection system performs well in detecting and categorizing DDoS attacks. Our method also has a minimal processing overhead so that it may be used on low-power Internet of Things gadgets.