Ensemble Approach for DDoS Attack Detection in Cloud Computing Using Random Forest and GWO

Abstract

When multiple technologies are added to a traditional network, it becomes increasingly difficult to meet newly imposed requirements, such as those regarding security. Since the widespread adoption of telecommunication technologies for the past decade, there have been an enhancement in the number of security threats that are more appealing. However, many new security concerns have arisen as a consequence of the introduction of the novel technology. One of the most significant of these is the potential for distributed denial of service attacks. Therefore, a DDoS detection method based on Random Forest Classifier and Grey Wolf Optimization algorithms in this work was developed to mitigate the DDoS threat. The results of the evaluation show that the Random Forest Classifier can achieve substantial performance improvements with respect to 99.96% accuracy. Comparison is also made to several state-of-the-art techniques for detecting of DDoS attacks for the real dataset.