Classification of Channel Access Attacks in Wireless Networks: A Deep Learning Approach

Abstract

Coping with diverse channel access attacks (CAAs) has been a major obstacle to realize the full potential of wireless networks as a basic building block of smart applications. Identifying and classifying different types of CAAs in a timely manner is a great challenge because of the inherently shared nature and randomness of the wireless medium. To overcome the difficulties encountered in existing methods, such as long latency, high data collection overhead, and limited applicable range, a deep learning-based CAA detection framework is proposed in this paper. First, we show the challenges of CAA classification by analyzing the impacts of CAAs on wireless network performance using an event-driven network simulator. Second, a state-transition model is built for the channel access process at a node, whose output sequences characterize the changing patterns of the node’s transmission status in different CAA scenarios. Third, a deep learning-based CAA classification framework is presented, which takes state transition sequences of a node as input and outputs predicted CAA types. The performance of three deep neural networks, i.e., fully-connected, convolutional, and Long Short-Term Memory (LSTM) network, for classifying CAAs are evaluated under our CAA classification framework in five CAA scenarios and the normal scenario without CAA. Experimental results show that LSTM outperforms the other two neural network architectures, and its CAA classification accuracy is higher than 95%. We successfully transferred the learned LSTM model to classify CAAs on other nodes in the same network and the nodes in other networks, which verifies the generality of our proposed framework.