Detecting WLANs' DoS Attacks Using Backpropagate Neural Network

During evaluation stages IEEE 802.11 wireless networks (WLANs) there are  many vulnerabilities including service availability security issues, Denial-of-Service (DoS) attacks which are the most dangerous for the WLANs’ availability . MAC frame contains useful features for detecting the DoS attacks. In this paper, a backpropagate neural network (BNN) has been used as a model for anomaly-based intrusion detection system for the wireless networks. We setup experiments for four different DoS attacks, the accuracy of the BNN model is too close to 100% and the false negative and the false positive rates are very small