Intrusion detection system for detecting wireless attacks in IEEE 802.11 networks

摘要: Sophisticated wireless attacks such as Wifiphishing, Evil twin and so on are a serious threat to Wi-Fi networks. These tricky enough spoof users by launching fake access point (AP) pretending be legitimate one. The existing intrusion detection schemes prone high rate of false positives they depend restricted features. Hence, an efficient system, which considers many more features is needed. Kernel density estimation (KDE) statistically models the distribution data detects in active mode. However, passive mode (without any connectivity AP), detecting attack complicated it requires prior knowledge signatures. other model used mode, namely hidden Markov (HMM), does not need initial probabilities. In this study, novel system proposed, combining KDE HMM through tandem queue with feedback. proposed KDE-HMM technique/method combines advantages both statistical probabilistic properties yield better results. performance technique has been experimentally validated found that aforementioned 98% accuracy.