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Title: Intelligent Adaptive Intrusion Detection Systems Using Neural Networks (Comparitive study )
Author(s): Aida O. Ali, Ahmed I. saleh, Tamer R. Badawy
Pages: 1-8 Paper ID: 101701-6363-IJET-IJENS Published: February, 2010
Abstract: Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS’s responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. Classification algorithms are used to discriminate between normal and different types of attacks. In this paper, a comparative study between the performances of recent nine artificial neural networks (ANNs) based classifiers is evaluated, based on a selected set of features. The results showed that; the Multilayer perceptrons (MLPS) based classifier provides the best results; about 99.63% true positive attacks are detected.
Keywords: Component; Intrusion detection system; artificial neural networks;Multilayer perceptrons.
Full Text (.pdf)  International Journals Of Engineering and Science | 263 KB
Title: A Survey of Botnet Technology and Detection
Author(s): Fatima Naseem, Mariam shafqat, Umbreen Sabir, Asim Shahzad
Pages: 9-12 Paper ID: 103801-5959-IJET-IJENS Published: February, 2010
Abstract: Apart from viruses, worms, Trojan horses, and network intrusions; there is a less familiar and exponentially growing threat that tends to be more disastrous: Botnets. The target of the botnet attacks on the integrity and resources of users might be multifarious; including the teenagers evidencing their hacking skills to organized criminal syndicates, disabling the infrastructure and causing financial damage to organizations and governments. In this context, it is crucial to know in what ways the system could be targeted. The major advantage of this classification is to identify the problem and find the specific ways of defense and recovery. This paper aims to provide a concise overview of major existing types of botnets on the basis of attacking techniques.
Keywords: Botnet Technology, Botnet Life Cycle
Full Text (.pdf)  International Journals Of Engineering and Science | 185 KB
Title: Adaptive Background subtraction in Dynamic Environments Using Fuzzy Logic
Author(s): Sivabalakrishnan. M, D. Manjula
Pages: 13-16 Paper ID: 109701-8282-IJET-IJENS Published: February, 2010
Abstract: Extracting a background from an image is the enabling step for many high-level vision processing tasks, such as object tracking and activity analysis. Although there are a number of object extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the object. In this paper, we develop a fuzzy logic inference system to detach the moving object from the background. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.
Keywords: Median filter, Background subtraction, Fuzzy.
Full Text (.pdf)  International Journals Of Engineering and Science | 228 KB