By quick computer and communication technology improvement, Distributed Denial of Service (DDoS) attack harm is getting more important. DDoS attacks research is the important study domain; a number of methods exist which have been presented like the algorithm of evolutionary as well as artificial intelligence in literature to diagnose attacks of DDoS. Unfortunately, new popular models of DDoS diagnosis are deteriorating for validating DDoS attacks objective and prior identification. Because of DDoS attack modes diversity as well as various attack traffic amount, still there is not the technique of diagnosis with promising accuracy of diagnosis currently. By choosing the best subset of features, a feature selection (FS) approach helps to shorten computing times and increase computational complexity. For mitigating attacks of denial of service, this paper applies honey badger algorithm (HBA) with algorithm of machine learning known as HBIDS. Present strategy is given the making intrusion detection system (IDS) for meeting controlled area needs and could recognize between attack and normal traffics. Moreover, HBIDS chooses the most related from basic dataset of IDS which could aid recognizing normal low-speed DDoS attacks, then chosen features are conveyed to classifiers like decision tree, multilayer perceptron, na\"ive Bayes, and support vector machine for identifying attack kind. Generally accessible dataset as CIC-IDS 2017 and KDD Cup 99 are applied for our experimental research. From simulation outcomes, this is obvious that HBIDS with decision tree needs high diagnosis with the low false–positive rate (0.001) and accuracy (99.9).
al-Mayyahi et al. (Tue,) studied this question.