Computing and Information Systems - Research Publications

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Type: Journal Article × Author: Kotagiri, Ramamohanarao × Author: Leckie, Christopher × Start date: 2006 × End date: 2009 ×

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Now showing 1 - 6 of 6
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    Protecting SIP Server from CPU-Based DoS Attacks using History-Based IP Filtering
    Zhou, CV ; Leckie, C ; Ramamohanarao, K (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009-10)
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    Survey of network-based defense mechanisms countering the DoS and DDoS problems
    Peng, T ; Leckie, C ; Ramamohanarao, K (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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    Information sharing for distributed intrusion detection systems
    Peng, T ; Leckie, C ; Ramamohanarao, K (ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2007-08)
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    Selective Sampling for Approximate Clustering of Very Large Data Sets
    WANG, L. ; BEZDEK, J. ; LECKIE, C. ; KOTAGIRI, R. ( 2008)
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    Approximate clustering in very large relational data
    Bezdek, JC ; Hathaway, RJ ; Huband, JM ; Leckie, C ; Kotagiri, R (WILEY, 2006-08)
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    Automatically Determining the Number of Clusters in Unlabeled Data Sets
    Wang, L ; Leckie, C ; Ramamohanarao, K ; Bezdek, J (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.