Reduction of False Positive Intrusions by using Neural Nets

The main idea of this paper is to propose a new solution for a wireless intrusion detection prevention system (WIDPS). The proposed WIDPS has a high degree of autonomy in tracking suspicious activity and detecting positive intrusions. Our focus was the reduction of detected false positive intrusion by implementing adaptive self-learning neural net in the system. Once it is fully developed and tested, this WIDPS would enable real-time response against threats, even to zero-day attacks.