Browsing by Author "Werasak Kurutach"

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    An improving fuzzy ant clustering using artificial immune recognition system
    (2006) Werasak Kurutach; Surat Srinoy; Witcha Chimphlee; Siriporn Chimphlee
    We present a swarm intelligence approach to data clustering. Ant based clustering is used to initially create raw clusters and then these clusters are refined using Artificial Immune Recognition System (AIRS). AIRS that has been developed as an immune-inspired supervise learning algorithm. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose artificial immune recognition systems (AIRS) in the second stage for refinement mean of overcoming these complexities are proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset.
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    An integrated fuzzy ants and artificial immune recognition system for anomaly detection
    (2006) Surat Srinoy; Werasak Kurutach; S. Srinoy; Department of Computer Science, Suan Dusit Rajabhat University, Bangkok, Thailand; email: surat_sri@dusit.ac.th
    A computer system intrusion is seen as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. The introduction to networks and the internet caused great concern about the protection of sensitive information and have resulted in many computer security research efforts during the past few years. This paper highlights a novel approach for detecting intrusion based on bio-inspired algorithm. The intrusion detection model combines the fuzzy ants clustering algorithm and artificial immune recognition algorithm to maximize detection accuracy and minimize computational complexity. The implemented system has been tested on the training data set from DARPA DATA SET by MIT Lincoln Laboratory on intrusion. The applicability of the proposed method and the enhanced security it provides are discussed. © 2006 ICASE.
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    Anomaly detection model based on bio-inspired algorithm and independent component analysis
    (Institute of Electrical and Electronics Engineers Inc., 2006) Surat Srinoy; Werasak Kurutach; S. Srinoy; Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Ratchasima Road, Thailand; email: surat_sri@dusit.ac.th
    With the advent and explosive growth of the global Internet and electronic commerce environments, adaptive/automatic network/service intrusion and anomaly detection in wide area data networks and e-commerce infrastructures is fast gaining critical research and practical importance. In this paper we present independent component analysis (ICA) based feature selection heuristics approach to data clustering. Independent Component Analysis is used to initially create raw clusters and then these clusters are refined using parallel Artificial Immune Recognition System(AIRS). AIRS that has been developed as an immune system techniques. The Algorithm uses artificial immune system(AIS) principle to find good partitions of the data. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose parallel Artificial Immune Recognition System (AIRS)) in the second stage for refinement mean of overcoming these complexities are proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999). © 2006 IEEE.
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    Combination artificial ant clustering and K-PSO clustering approach to network security model
    (2006) Surat Srinoy; Werasak Kurutach; S. Srinoy; Department of Computer Science, Suan Dusit Rajabhat University, Thailand; email: surat_sri@dusit.ac.th
    A Computer system now operate in an environment of near ubiquitous connectivity, whether tethered to an Ethernet cable or connected via wireless technology. While the availability of always on communication has created countless new opportunities for web based businesses, information sharing, and coordination, it has also created new opportunities for those that seek to illegally disrupt, subvert, or attack these activities. We present natural based data mining algorithm approach to data clustering. Artificial ant clustering algorithm is used to initially create raw clusters and then these clusters are refined using k-mean particle swarm optimization (KPSO). KPSO that has been developed as evolutionary-based clustering technique. The algorithm uses hybridization the k-means algorithm and PSO principle to find good partitions of the data. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose k-means particle swarm optimization clustering algorithm in the second stage for refinement mean of overcoming these complexities is proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999. © 2006 IEEE.
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    Intrusion detection via independent component analysis based on rough fuzzy
    (2006) Surat Srinoy; Werasak Kurutach; Witcha Chimphlee; Siriporn Chimphlee; Santi Sounsri; S. Srinoy; Department of Computer Engineering, Mahanakorn University of Technology, Nongchok, Bangkok, 51 Chuemsumphun Road, Thailand; email: surat_sri@dusit.ac.th
    Independent component analysis (ICA) aims at extracting unknown hidden factors/components from multivariate data using only the assumption that unknown factors are mutually independent. In this paper we discuss an intrusion detection method that proposes independent component analysis based feature selection heuristics and using rough fuzzy for clustering data. Rough set has to decrease the amount of data and get rid of redundancy and Fuzzy methods allow objects to belong to several clusters simultaneously, with different degrees of membership. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset.
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    Unsupervised learning: A fusion of rough sets and fuzzy ants clustering for anomaly detection system
    (Institute of Electrical and Electronics Engineers Inc., 2006) Surat Srinoy; Werasak Kurutach; S. Srinoy; Department Computer Science, Suan Dusit Rajabhat University, Thailand; email: surat_sri@dusit.ac.th
    The Traditional intrusion detection systems (IDS) look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper we propose an intrusion detection method that proposes rough set based feature selection heuristics and using fuzzy ants for clustering data. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy ants clustering methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset. ©2006 IEEE.