Computing and Information Systems - Research Publications
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ItemIntrusion detection for routing attacks in sensor networks(SAGE PUBLICATIONS INC, 2006)Security is a critical challenge for creating robust and reliable sensor networks. For example, routing attacks have the ability to disconnect a sensor network from its central base station. In this paper, we present a method for intrusion detection in wireless sensor networks. Our intrusion detection scheme uses a clustering algorithm to build a model of normal traffic behavior, and then uses this model of normal traffic to detect abnormal traffic patterns. A key advantage of our approach is that it is able to detect attacks that have not previously been seen. Moreover, our detection scheme is based on a set of traffic features that can potentially be applied to a wide range of routing attacks. In order to evaluate our intrusion detection scheme, we have extended a sensor network simulator to generate routing attacks in wireless sensor networks. We demonstrate that our intrusion detection scheme is able to achieve high detection accuracy with a low false positive rate for a variety of simulated routing attacks.
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ItemProtecting SIP Server from CPU-Based DoS Attacks using History-Based IP Filtering(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009-10)
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ItemSurvey of network-based defense mechanisms countering the DoS and DDoS problems(ASSOC COMPUTING MACHINERY, 2007-04)This article presents a survey of denial of service attacks and the methods that have been proposed for defense against these attacks. In this survey, we analyze the design decisions in the Internet that have created the potential for denial of service attacks. We review the state-of-art mechanisms for defending against denial of service attacks, compare the strengths and weaknesses of each proposal, and discuss potential countermeasures against each defense mechanism. We conclude by highlighting opportunities for an integrated solution to solve the problem of distributed denial of service attacks.
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ItemInformation sharing for distributed intrusion detection systems(ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2007-08)
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ItemDiscovering correlated spatio-temporal changes in evolving graphs(SPRINGER LONDON LTD, 2008-07)
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ItemApproximate clustering in very large relational data(WILEY, 2006-08)
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ItemAutomatically Determining the Number of Clusters in Unlabeled Data Sets(Institute of Electrical and Electronics Engineers, 2009-03-01)One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper, we investigate a new method called Dark Block Extraction (DBE) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for Visual Assessment of Cluster Tendency (VAT) of a data set, using several common image and signal processing techniques. Its basic steps include 1) generating a VAT image of an input dissimilarity matrix, 2) performing image segmentation on the VAT image to obtain a binary image, followed by directional morphological filtering, 3) applying a distance transform to the filtered binary image and projecting the pixel values onto the main diagonal axis of the image to form a projection signal, and 4) smoothing the projection signal, computing its first-order derivative, and then detecting major peaks and valleys in the resulting signal to decide the number of clusters. Our DBE method is nearly “automatic,” depending on just one easy-to-set parameter. Several numerical and real-world examples are presented to illustrate the effectiveness of DBE.
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ItemAn efficient clustering scheme to exploit hierarchical data in network traffic analysis(IEEE COMPUTER SOC, 2008-06)