| Abstract: | WiFi 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
several respects, the current, more robust, security amendments
will inevitably get outperformed in the future too. In any case,
several security vulnerabilities have been spotted in virtually any
version of the protocol rendering the integration of external
protection mechanisms a necessity. In this context, the contribution
of this paper is multi-fold. First, it gathers, categorizes,
thoroughly evaluates the most popular attacks on 802.11, and
analyzes their signatures. Second, it offers a publicly available
dataset containing a rich blend of normal and attack traffic
against 802.11 networks. A quite extensive first-hand evaluation
of this dataset using several machine learning algorithms and
data features is also provided. Given that to the best of our
knowledge the literature lacks such a rich and well-tailored
dataset, it is anticipated that the results of the work at hand
will offer a solid basis for intrusion detection in the current as
well as, next generation wireless networks. |
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Greek