A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network

Khoa Dinh Nguyen Dang, Peppino Fazio, Miroslav Voznak

In modern network security setups, Intrusion Detection Systems (IDS) are crucial elements that play a key role in protecting against unauthorized access, malicious actions, and policy breaches. Despite significant progress in IDS technology, two of the most major obstacles remain: how to avoid false alarms due to imbalanced data and accurately forecast the precise type of attacks before they even happen to minimize the damage caused. To deal with two problems in the most optimized way possible, we propose a two-task regression and classification strategy called Hybrid Regression–Classification (HRC), a deep learning-based strategy for developing an intrusion detection system (IDS) that can minimize the false alarm rate and detect and predict potential cyber-attacks before they occur to help the current wireless network in dealing with the attacks more efficiently and precisely. The experimental results show that our HRC strategy accurately predicts the incoming behavior of the IP data traffic in two different datasets. This can help the IDS to detect potential attacks sooner with high accuracy so that they can have enough reaction time to deal with the attack. Furthermore, our proposed strategy can also deal with imbalanced data. Even when the imbalance is large between categories. This will help significantly reduce the false alarm rate of IDS in practice. These strengths combined will benefit the IDS by making it more active in defense and help deal with the intrusion detection problem more effectively.