Finding an elite feature for (D)DoS fast detection—Mixed methods research

Abstract

Distributed Denial of Service (DDoS) attacks are a persistent security issue in the cyber world due to their diverse character. Emerging technologies such as the Internet of Things and Software Defined Networking leverage lightweight strategies for the early detection of DDoS attacks. This study proposes mathematical modeling to extract the best single feature for fast DDoS attack detection since feature selection is important in developing a lightweight detection model. The primary goal of this article is to demonstrate the importance of the proposed single feature for DDoS attack detection through the use of a mixed methods approach. The qualitative analysis is performed by pinpointing the reasons for various DDoS attacks in order to derive the best single feature, and the quantitative analysis of the derived feature delivers superior results in both the proposed framework evaluation and comparative analysis. All observations are statistically proven by the analysis of variance tests.