An intelligent hybrid model for cyber attack classification with selected feature set

Cyber security evolving as a severe problem almost in all sectors of cyberspace, due to the time-to-time increase in the number of security breaches. Numerous Zero-days attacks occur continuously, due to the increase in multiple protocols. Almost all of these attacks are small variants of previously known cyber attacks. Moreover, even the advanced approach like Machine Learning (ML), faces the difficulty in identifying those attack’s small mutants over time. Recently, Deep Learning (DL) has been utilized for multiple applications related to cybersecurity fields. Making use of this DL to identify the cyber attack might be a resilient mechanism for novel attacks or tiny mutations. Thereby, a novel cyber attack classification model named DCNN-Bi-LSTM-ICS is proposed in this work. This proposed DCNN-Bi-LSTM-ICS has five working stages. Firstly, in the data acquisition stage, the input data (considering the datasets) for attack classification has been collected. These raw data are pre-processed in the second stage, where an improved class imbalance balancing processing is conducted which makes use of the Improved Synthetic Minority Oversampling Technique (ISMOTE). In the third stage, along with the conventional mutual information and statistical features, Improved holo-entropy-based features are extracted. To choose the appropriate feature from those retrieved features, an Improved Chi-Square (ICS) processing is developed in the fourth stage. In the final classification stage, a hybrid classification model that combines both the Deep Convolutional Neural Network (DCNN) and Bi-directional Long Short Term Memory (Bi-LSTM) has been developed. The outcomes show that the proposed DCNN-Bi-LSTM-ICS can offer outstanding performance in the cyber attack classification task.