Defense against distributed DoS attack detection by using intelligent evolutionary algorithm

Abstract

Modern society is prominently dependent on information and communication technology over the last few decades has carried it with more vulnerability to an enormous variety of cyber-attacks. One of the attacks is a Distributed Denial-of-Service (DDoS) that exploits the power of thousands and sometimes hundreds of thousands of compromised computers to attack information-providing services and online commercial sites, often resulting in significant downtime and financial losses and thereby denying services of legitimate clients. The study of DDoS attacks is a significant area of research; there are a number of techniques that have been proposed such as evolutionary algorithm and artificial intelligence in the literature for detecting DDoS attacks. Unfortunately, the modern well-known DDoS detection schemes are deteriorating to validate the objective and prior recognition of DDoS attacks. In order to mitigate denial of service attacks, in this paper, we use grasshopper optimization algorithm (GOA) with machine learning algorithm called GOIDS. This approach is based on creating an intrusion detection system (IDS) to fulfill the requirements of the monitored environment and able to distinguish between normal and attack traffics. Furthermore, GOIDS selects the most relevant features from the original IDS dataset that can help to distinguish typical low-speed DDoS attacks and then, selected features are passed to the classifiers, i.e. support vector machine, decision tree, naïve Bayes, and multilayer perceptron to identify type of attack. The publicly available dataset as KDD Cup 99 and CIC-IDS 2017 are used for our experimental study. From the results of the simulation, it is clear that GOIDS with decision tree acquires high detection and accuracy with a low false–positive rate.