Abstract:
Despite the significant advancement in wireless technologies over the years, IEEE 802.11 still emerges as the de-facto standard to achieve the required short to medium ra...View moreMetadata
Abstract:
Despite the significant advancement in wireless technologies over the years, IEEE 802.11 still emerges as the de-facto standard to achieve the required short to medium range wireless device connectivity in anywhere from offices to homes. With it being ranked the highest among all deployed wireless technologies in terms of market adoption, vulnerability exploitation and attacks targeting it have also been commonly observed. IEEE 802.11 security has thus become a key concern over the years. In this paper, we analysed the threats and attacks targeting the IEEE 802.11 network and also identified the challenges of achieving accurate threat and attack classification, especially in situations where the attacks are novel and have never been encountered by the detection and classification system before. We then proposed a solution based on anomaly detection and classification using a deep learning approach. The deep learning approach self-learns the features necessary to detect network anomalies and is able to perform attack classification accurately. In our experiments, we considered the classification as a multi-class problem (that is, legitimate traffic, flooding type attacks, injection type attacks and impersonation type attacks), and achieved an overall accuracy of 98.6688% in classifying the attacks through the proposed solution.
Date of Conference: 19-22 March 2017
Date Added to IEEE Xplore: 11 May 2017
ISBN Information:
Electronic ISSN: 1558-2612