Convolutional Neural Networks for Multi-class Intrusion Detection System

Advances in communication and networking technology leads to the use of internet-based technology in Industrial Control System (ICS) applications. Simultaneously to the advantages and flexibility, it also opens doors to the attackers. Increased attacks on ICS are clear examples for the need of developing strong security mechanisms to develop defense in depth strategies for industries. Despite several techniques, every day a novel attack is being identified and this highlights the importance and need of robust techniques for identifying those attacks. Deep learning-based intrusion detection mechanisms are proven to be efficient in identifying novel attacks. Deep learning techniques such as Stacked Autoencoders (SAE), Deep Belief Networks (DBN) are widely used for intrusion detection but the research on using Convolutional Neural Networks (CNN) is limited. In this paper, the efficiency of CNN based intrusion detection for identifying the multiple attack classes using datasets such as NSLKDD and UNSW-NB 15 is evaluated. Different performance metrics such as precision, recall and F-measure were calculated and compared with the existing deep learning approaches.