Intrusion Detection System Based on Network Traffic Using Deep Neural Networks

Nowadays, the small-medium enterprises security against cyber-attacks is a matter of great importance and a challenging area, as it affects them financially and functionally. Novel and sophisticated attacks are emerging daily, targeting and threatening a large number of businesses around the world. For this reason, the implementation and optimization of the performance of Intrusion Detection Systems have attracted the interest of the scientific community. The malicious behavior detection in terms of DDoS and malware cyber-threats using deep learning methods constitutes an extended and the most important part of this paper. The experimental results for the real-time intrusion detection system showed that the proposed model can achieve high accuracy, and low false positive rate, while distinguishing between malicious and normal network traffic.