An efficient SVM based DEHO classifier to detect DDoS attack in cloud computing environment

Distributed denial of service (DDoS) attacks is rising exponentially and creates a severe threat to security. Generally, the DDoS attack may appear uncomplicated but they are hard to prevent and considered as one of the most significant cybersecurity issues. Hence, addressing against DDoS attacks turns out to be imperative. The major goal of this paper involves the optimal detection of data samples as normal data samples and malicious/ attacked data samples. This paper proposes a security algorithm against DDoS attacks by employing four significant phases namely the Database training phase, Data pre-processing phase, Feature selection phase and Classification phase. Initially, the data samples are to be trained before using it for attack detection. Later, the sample group is created for every file and the data samples are pre-processed in the data pre-processing phase. Secondly, in feature selection phase, the selected features are optimized by employing kernel principal component analysis (KPCA) to obtain optimal features. Later, in the classification phase, a support vector machine-based discrete elephant herding optimization (SVM-DEHO) classifier is utilized to detect the data sample as normal data and attacked or malicious data. Finally, the proposed approach is examined for four different databases namely NSL-KDD, UNSW-NB15, ISCX ID and CIC-IDS2017 databases respectively. The experimental analyses are performed for various simulation metrics and the outcome reveals that the detection system performances are high using SVM-DEHO approach than other approaches.