Harris Hawk Optimization-Based Distributed Denial of Service Attack Detection in IoT Networks

As Internet of Things (IoT) grows in popularity, cyber risks and distributed denial of service (DDoS) attacks have increased. DDoS attacks shut down genuine users by flooding a targeted network or service with a large volume of traffic from several hacked computers. DDoS attack can cause significant harm to a network and its users. By gathering benchmark network traffic statistics that come within the Bigdata category, the proposed method assesses and categories these attacks to detect DDoS network activities. DDoS Insights and trends that might not be immediately visible from the table are discovered and understood using an exploratory data analysis technique named Harris Hawk Optimization (HHO). The minimum fitness value based optimal fitness function helps to achieve optimal detection result is effectively. Using benchmark/NS3 DDoS data, the proposed technique is assessed and found to be successful in detecting attack features and offering defense-related knowledge. By providing more accurate and consistent detection and prevention of DDoS attacks, this technology will play a significant role in enhancing security for IoT networks.