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The paper presents a botnet detection approach for the distributed systems. It is based on the developed three level model, which includes botnet’s components: command and control center, control centers, basic elements of the botnet (bots). The novel framework provides the ability to detect known and unknown botnets, and consists of the host and the network levels. At the host level, the detection procedure is based on the implementation of the Bayes classification. The network level extends the results obtained at the host level to the rest of the local area network. Proposed approach provides the exchange of the results obtained by the Bayes classification for further use by other program units of the distributed system. The results of the developed classifier show that representation of the botnets’ samples for different classes and subclasses is sufficient for efficient botnet detection. Proposed technique demonstrates promising results concerning botnet detection in the distributed systems.

The Ternopil Educational Communication Center was developed with NATO funding to improve computing for the universities of Ternopil, Ukraine. It provides high speed access for all of Ternopil’s universities to the Internet and the World Wide Web. It enables high speed communication within Ternopil, and between institutions in Ternopil using the UarNet and URAN networks. It provides an integration of on-line library services, and it supports such activities as video conferencing, distance education, and class to class participation. The universities of Ternopil and the University of Maine are developing programs and projects to involve students and faculty both in Ukraine and the US.

This paper describes the Smart Vehicle Screening System, which can be installed into a tollbooth for automated recognition of vehicle license plate information using a photograph of a vehicle. An automated system could then be implemented to control the payment of fees, parking areas, highways, bridges or tunnels, etc. There are considered an approach to identify vehicle through recognizing of it license plate using image fusion, neural networks and threshold techniques as well as some experimental results to recognize the license plate successfully.