Pairing an Autoencoder and a SF-SOINN for Implementing an Intrusion Detection System

Intrusion Detection System are systems aiming to detect intrusions within individual computers or networks. These systems are of fundamental importance nowadays, as the number of attacks on networks is ever increasing. In this paper, a prototype of a new Intrusion Detection System is presented. The key novelty is the architecture of this system, pairing an Autoencoder and a Soft-Forgetting Self-Organizing Incremental Neural Network. A fusing scheme is applied to exploit the classification capabilities of the two approaches. The proposed system, tested in different conditions using the NSL-KDD dataset, has achieved excellent performance in detecting attacks, demonstrating its ability to evolve its knowledge and to recognize attacks never seen before.