The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accurate than traditional classification methods. This is a quantitative research applies Logistic Regression, Decision Tree, Random Forest, Ada Boost, Gradient Boost, KNN, and Naive Bayes classification algorithms to detect DDoS attacks on the CIC-DDoS2019 data set, which contains eleven different DDoS attacks each containing 87 features. In addition, evaluated classifiers’ performances in terms of evaluation metrics. Experimental results show that AdaBoost and Gradient Boost algorithms give the best classification results, Logistic Regression, KNN, and Naive Bayes give good classification results, Decision Tree and Random Forest produce poor classification results.

Computational Intelligence (CI) is an as of emerging area in addressing complex real world problems. The WOA has taken its root from the collective intelligent foraging behavior of humpback whales (Megaptera Novaeangliae). The standard WOA is suffers from the selection of best agent while whales searching and encircling prey. This research paper deals with the multi-swarm cooperative strategies for finding the best agents which balances the two phase’s exploration and exploitation. The performance of invoked Multi-Swarm cooperative strategies into standard WOA i.e, MsWOA is first benchmarked on a set of 23 standard mathematical benchmark function problems which includes 7 Uni-Modal, 6 Multi-modal and 10 fixed dimension multi-modal functions. The obtained graphical and statistical results have been portrayed along with the previously established techniques. The obtained results depicts that the proposed cooperative strategies for WOA outperforms in solving optimization problems of standard benchmark functions. This paper also studies the numerical efficiency of proposed techniques on the problem of data clustering where 10 real data clustering problems have been taken from data repository https://archive.ics.uci.edu.data. Statistical results for the obtained cluster centroids, intra-cluster distances and inter-cluster distances confirms that the cooperative strategies for best whale agent selection improves the performance WOA for function optimization problems as well as data clustering problems.