Hybrid classification model with tuned weight for cyber attack detection: Big data perspective

Abstract

Cybercrime using big data is growing at an unprecedented rate, posing a serious threat to the Internet sector and global data. Traditional ways of mitigating cyber risks are becoming inadequate due to the more complex attack and offensive methods employed by cyber attackers, as well as the expanding importance of data-driven and intellect competitors. This work introduces new cyber attack detection (CAD) model in Big data that includes: “Preprocessing, Feature Extraction, Feature Selection, and Detection, Mitigation”. The preprocessing is done by using the improved class imbalance process. The variety of 3 features is extracted as “flow-based features, improved entropy-based features, and higher-order statistical features”. For feature selection, the Improved Independent component analysis (ICA) is used. Finally, the hybrid classifier includes LSTM and Deep Max out (DMO) in the detection process. Once the presence of an attack is detected, mitigation takes place via the proposed Bait mitigation process. The weights of Long Short-Term Memory (LSTM) are optimized by using the Self-Enhanced Sea Gull Optimization (SE-SGO) model. The maximum accuracy has been achieved (0.94) for the suggested approach which is 38%, 14.6%, 7.36%, 38.7%, and 10.5% superior to the other existing approaches like HC + SGO, HC + SSOA, HC + DHOA, HC + DOX, and HC + FF, respectively.