PROJECT TITLE :

Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset

ABSTRACT:

Wi-Fi has become the de facto wireless technology for achieving short- to medium-range device connectivity. While early attempts to secure this technology have been proved inadequate in many respects, the current additional robust security amendments can inevitably get outperformed in the longer term, too. In any case, many security vulnerabilities have been noticed in nearly any version of the protocol rendering the integration of external protection mechanisms a necessity. During this context, the contribution of this paper is multifold. First, it gathers, categorizes, totally evaluates the foremost in style attacks on 802.eleven and analyzes their signatures. Second, it offers a publicly accessible dataset containing a wealthy blend of normal and attack traffic against 802.eleven networks. A quite extensive initial-hand analysis of this dataset using several machine learning algorithms and knowledge features is additionally provided. Given that to the best of our data the literature lacks such a made and well-tailored dataset, it's anticipated that the results of the work at hand can provide a solid basis for intrusion detection in this plus next-generation wireless networks.