Distributed DoS Detection in IoT Networks Using Intelligent Machine Learning Algorithms

The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool.