Peter Komisarczuk

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Keywords: Wireless networks Context awareness Intelligence Reinforcement learning Mobile ad hoc networks Wireless sensor networks Cognitive radio networks a b s t r a c t In wireless networks, context awareness and intelligence are capabilities that enable each host to observe, learn, and respond to its complex and dynamic operating environment in an(More)
Client honeypots are security devices designed to find servers that attack clients. High-interaction client honey-pots (HICHPs) classify potentially malicious web pages by driving a dedicated vulnerable web browser to retrieve and classify these pages. Considering the size of the Internet, the ability to identify many malicious web pages is a crucial task.(More)
Client-side attacks have become an increasing problem on the Internet today. Malicious web pages launch so-called drive-by-download attacks that are capable to gain complete control of a user's machine by merely having that user visit a malicious web page. Criminals that are behind the majority of these malicious web pages are highly sensitive to location,(More)
—Malicious web pages that launch client-side attacks on web browsers have become an increasing problem in recent years. High-interaction client honeypots are security devices that can detect these malicious web pages on a network. However, high-interaction client honeypots are both resource-intensive and unable to handle the increasing array of vulnerable(More)
—We present a physical cognitive radio system implementation under the GNU Radio platform with the aim of evaluating a reinforcement learning spectrum management scheme. In our experiments we examine the packet transmission success rate of the cognitive user for a variety of channel utilisation parameters. We derive analytical expressions using Markov chain(More)
We present the design and analysis of a new algorithm for high interaction client honeypots for finding malicious servers on a network. The algorithm uses the divide-and-conquer paradigm and results in a considerable performance gain over the existing sequential algorithm. The performance gain not only allows the client honeypot to inspect more servers with(More)
Minkowski Weighted K-Means is a variant of K-Means set in the Minkowski space, automatically computing weights for features at each cluster. As a variant of K-Means, its accuracy heavily depends on the initial centroids fed to it. In this paper we discuss our experiments comparing six initializations, random and five other initializations in the Minkowski(More)