SCOPUS 2005-2009

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Integrating genetic algorithms and fuzzy c-means for anomaly detection
(2005) Witcha Chimphlee; Abdul Hanan Abdullah; Mohd Moor Md Sap; Siriporn Chimphlee; Surat Srinoy; W. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Road, Thailand; email: witcha_chi@dusit.ac.th
The goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest. © 2005 IEEE.
E-learning in Thailand: An analysis and case study
(International Institute of Informatics and Systemics, IIIS, 2005) Pannee Suanpang; Jarinee Santijanyaporn
This paper presents a discussion of E-Leaming in the context of Thailand using as an example a study carried out in a course in Business Statistics at Suan Dusit Rajabhat University (SDU), Thailand. The online course was a pioneering research project at SDU for studying the efficiency and effectiveness of the online learning system. The research conducted over 16 weeks compared online learning with traditional teaching. Aspects of students' learning outcomes have been analyzed, including quantitative features such as their grades and course evaluations, and this analysis is supported by qualitative features such as results of open-ended questionnaires, interviews and diaries. Results of the analysis show that students' outcomes were more favorable in the online groups than in the traditional groups. The large amount of rich qualitative information obtained highlights a range of reasons for this. The results of this study will be beneficial and useful for further research to develop effective and efficient online learning systems in Thailand, and in other countries with similar educational backgrounds to this country.
An adaptive IDS model based on swarm intelligence and support vector machine
(2006) Surat Srinoy; S. Srinoy; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand; email: surat_sri@dusit.ac.th
Intrusion detection system looks for unusual or suspicious activity, such as patterns of network traffics that are likely indicators of unauthorized activity. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large, human labeling is tedious, time-consuming, and expensive. In this paper we present support vector machine approach to data clustering. Support vector machine is used to initially create raw clusters and then these clusters are refined using Artificial Fuzzy Ants Clustering (AFAC). AFAC that has been developed as swarm intelligence techniques. The Algorithm uses ant colony optimization 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 Artificial Fuzzy Ants Clustering (AFAC) 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.
Anomaly-based intrusion detection using fuzzy rough clustering
(2006) Witcha Chimphlee; Abdul Hanan Abdullah; Mohd Noor Md. Sap; Surat Srinoy; Siriporn Chimphlee; W. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Thailand; email: witcha_chi@dusit.ac.th
It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods © 2006 IEEE.
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.
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.
An approach to solve computer attacks based on hybrid model
(2006) Surat Srinoy; Witcha Chimphlee; Siriporn Chimphlee; Yoothapoom Poopaibool; S. Srinoy; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Ratchasima Road, Thailand; email: surat_sri@dusit.ac.th
It is an important issue for the security of network to detect new intrusion attack. We introduce the idea of the Independent component analysis (ICA) based feature selection heuristics, and present Support Vector Machine (SVM) algorithm for data classification. ICA aims at extracting unknown hidden factors/components from multivariate data using only the assumption that unknown factors are mutually independent. The experimental results on dataset Knowledge Discovery and Data Mining (KDDCup99) show that our method outperforms the existing intrusion detection methods.
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.
Rough fuzzy approach for web usage mining
(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salihin Bin Ngadiman; Witcha Chimphlee; Surat Srinoy; S. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Rd, Thailand; email: siriporn_chi@dusit.ac.th
Web usage mining is a new subfield of data mining research. It aims at discovery of trends and regularities in web users' access patterns. In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. A challenge in web classification is how to deal with the high dimensionality of the feature space. This paper applies the concept of rough fuzzy approach for classification in web usage mining tasks after we present Independent Component Analysis (ICA) for feature. Clustering is an important part of web mining that involves finding natural groupings of web resources or web users.
Using association rules and Markov model for predict next access on Web usage mining
(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salihin Bin Ngadiman; Witcha Chimphlee
Predicting the next request of a user as visits Web pages has gained importance as Web-based activity increases. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts a user's next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site access prediction based on past visitor behavior and compare it association rules technique. In these approaches, sequences of user requests are collected by the session identification technique, which distinguishes the requests for the same web page in different browses. We report experimental studies using real server log for comparison between methods and show that degree of precision. © 2006 Springer.
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.
Meta-scheduler in Grid environment with multiple objectives by using genetic algorithm
(2006) Siriluck Lorpunmanee; Mohd Noor Md Sap; Abdul Hanan Abdullah; Surat Srinoy; S. Lorpunmanee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Rd., Malaysia; email: siriluck_lor@dusit.ac.th
Grid computing is the principle in utilizing and sharing large-scale resources of heterogeneous computing systems to solve the complex scientific problem. Such flexible resource request could offer the opportunity to optimize several parameters, such as coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major opportunity is in optimal job scheduling, which Grid nodes need to allocate the resources for each job. This paper proposes and evaluates a new method for job scheduling in heterogeneous computing Systems. Its objectives are to minimize the average waiting time and make-span time. The minimization is proposed by using a multiple objective genetic algorithm (GA), because the job scheduling problem is NP-hard problem. Our model presents the strategies of allocating jobs to different nodes. In this preliminary tests we show how the solution founded may minimize the average waiting time and the make-span time in Grid environment. The benefits of the usage of multiple objective genetic algorithm is improving the performance of the scheduling is discussed. The simulation has been obtained using historical information to study the job scheduling in Grid environment. The experimental results have shown that the scheduling system using the multiple objective genetic algorithms can allocate jobs efficiently and effectively.
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.
Independent component analysis and rough fuzzy based approach to web usage mining
(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salim Bin Ngadiman; Witcha Chimphlee; Surat Srinoy; S. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Rd, Thailand; email: siripom.chi@dusit.ac.th
Web Usage Mining is that area of Web Mining which deals with the extraction of interesting knowledge from logging information produced by Web servers. A challenge in web classification is how to deal with the high dimensionality of the feature space. In this paper we present Independent Component Analysis (ICA) for feature selection and using Rough Fuzzy for clustering web user sessions. It aims at discovery of trends and regularities in web users' access patterns. ICA is a very general-purpose statistical technique in which observed random data are linearly transformed into components that are maximally independent from each other, and simultaneously have "interesting" distributions. Our experiments indicate can improve the predictive performance when the original feature set for representing web log is large and can handling the different groups of uncertainties/impreciseness accuracy.
Mathematical simulation of nonlinear effects in micro ring resonator
(2006) P.P. Yupapin; Chat Teeka; Pakkinee Chitsakul; P.P. Yupapin; Department of Applied Physics, Faculty of Science, King Mongkut' S Institute of Technology Ladkrabang, Bangkok 10520, Thailand; email: kypreech@kmitl.ac.th
In this paper, we demonstrate the mathematical simulation data of light traveling in an optical micro ring resonator. The optical nonlinear properties such as chaos, bifurcation, bistability and instability of the optical outputs are studied. By changing the optical parameters that result the change of the optical output intensities, the nonlinear behaviors such as bifurcation, chaos and bistability effects are occurred. The relationship between the optical parameters and output intensities are derived by varying the interested parameters such as coupling coefficient (_), nonlinear refractive index (n2), and linear phase shift (_0). The results obtained are presented and plotted showing that the optical parameters can be changed i.e. controlled, and then the nonlinear effects characteristics can be predicted and controlled. © 2006 IEEE.
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.
To detect misuse and anomaly attacks through rule induction analysis and fuzzy methods
(2006) Witcha Chimphlee; Abdul Hanan Abdullah; Mohd Noor Md. Sap; Siriporn Chimphlee; Surat Srinoy; W. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Road, Thailand; email: witcha_chi@dusit.ac.th
To protect networks, intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. In this paper we propose an intrusion detection method that combines rule induction analysis for misuse detection and Fuzzy c-means for anomaly detection. Rule induction is used to generate patterns from data and finding a set of rules that satisfy some predefined criteria. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Our method is an accurate model for handle complex attack patterns in large networks. Empirical studies using the network security data set from the DARPA 1998 offline intrusion detection project (KDD 1999 Cup) show the feasibility of misuse and anomaly detection results.
To identify suspicious activity in anomaly detection based on soft computing
(2006) Witcha Chimphlee; Mohd Noor Md Sap; Abdul Hanan Abdullah; Siriporn Chimphlee; Surat Srinoy; W. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Road, Thailand; email: witcha_chi@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. In this paper we propose an intrusion detection method that proposes rough set based feature selection heuristics and using fuzzy c-means for clustering data. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy 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 increase accuracy detection rate for suspicious activity and signature detection. Empirical studies using the network security data set from the DARPA 1998 offline intrusion detection project (KDD 1999 Cup) show the feasibility of misuse and anomaly detection results.
Shelf-life extension of refrigerated soft shell mud crab (Scylla serrata Forskal) by ozone water and storage under air and modified atmosphere packaging
(2007) Nathapol Phapinyo; Tawirat Chaowalit; Aiyakarn Tanpipattanakul; Kijja Sooknet; Aekarat Kumthong; Wanchai Worawattanamateekul; Jim P. Smith; N. Phapinyo; Program of Food Science and Technology, Faculty of Science and Technology, Rajabhat Suan Dusit University, Bangkok 10300, Thailand; email: nphapinyo@yahoo.com
The effects of ozone water combined with modified atmosphere packaging on the refrigerated storage quality of soft shell mud crab at 4±0.5¡C were investigated. Soft shell mud crab stored under modified atmosphere packaging and vacuum had lower microbial counts compared with that of mud crab stored in an aerobic package (p<0.05). The odor and overall acceptability were acceptable for the soft shell mud crabs packaged under 80%CO 2+20%N2 and vacuum throughout the storage period of 11 days. Microbial changes in all samples were generally in agreement with sensory panel evaluations. The shelf life of soft shell mud crab soaked in 1.0 ppm for 20 min and packaged under 80%CO2+20%N2 and vacuum was extended to at least 11 days at 4±0.5¡C. The shelf life of soft shell mud crab soaked in water and aerobically packaged was 3 days.
Firmness properties of mangoes
(2007) Bundit Jarimopas; Udomsak Kitthawee; B. Jarimopas; Department of Agricultural Engineering, Faculty of Engineering, Kamphaengsaen Kasetsart University, Kamphaenagsaen, Nakohn Pathom, Thailand; email: jarimopas@yahoo.com
The purpose of this research was to determine the firmness of mango fruit at different stages of maturity. Immature, mature, and over mature fruit from two Thai mango cultivars (Nam Dokmai and Chok Anan) were subjected to two firmness measuring techniques: a slow compression technique using the Universal Testing Machine and a high-speed impact sensing technique using the low-mass impact tester. The firmness of a mango as determined by the compression test was expressed by the slope of the force-deformation graph, while firmness values derived from the impact test were defined by the ratio between maximum acceleration and the corresponding time (the firmness index). A very good correlation between the two indicators was obtained. Each firmness indicator of the two cultivars remained relatively unchanged from the immature through to the fully mature stage, although firmness rapidly decreased as the fruit ripened. The Chok Anan cultivar was firmer than the Nam Dokmai type throughout the development period. The impact method was able to determine mango firmness rapidly, accurately, and non-destructively.

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