SCOPUS 2005-2009

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A novel method for unsupervised anomaly detection using unlabelled data
(2008) Abdul Samad Bin Haji Ismail; Abdul Hanan Abdullah; Kamalrulnizam Bin Abu Bak; Md Asri Bin Ngadi; Dahliyusmanto Dahlan; Witcha Chimphlee; A. S. B. H. Ismail; Faculty of Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia; email: abdsamad@utm.my
Most current intrusion detection methods cannot process large amounts of audit data for real-time operation. In this paper, anomaly network intrusion detection method based on Principal Component Analysis (PCA) for data reduction and Fuzzy Adaptive Resonance Theory (Fuzzy ART) for classifier is presented. Moreover, PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Using a set of benchmark data from KDD (Knowledge Discovery and Data Mining) Competition designed by DARPA for demonstrate to detection intrusions. Experimental results show the proposed model can classify the network connections with satisfying performance. © 2008 IEEE.
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.
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 examination of tourists' attitudinal and behavioral loyalty: Comparison between domestic and international tourists
(2009) Panisa Mechinda; Sirivan Serirat; Nak Gulid; P. Mechinda; Marketing Department, Faculty of Business Administration, Rajamangala University of Technology, Thanyaburi, Amphur Thanyaburi, Pathumtani 12110, 39 Moo 1 Rangsit-Nakornnayok Road, Khlong 6, Thailand; email: Lpanisa@yahoo.com
The purpose of this study is to examine the antecedents of tourists' loyalty (both attitudinal and behavioral) towards Chiangmai (a major tourist destination in Thailand). Multiple regression analysis indicated that attitudinal loyalty was mainly driven by attachment, familiarity and perceived value, whereas behavioral loyalty is driven by familiarity. Only one dimension of pull motivation (history, heritage and knowledge) influenced attitudinal loyalty, whereas none of pull motivation's dimensions had an effect on behavioral loyalty. Regarding push motivation, tourists' desire for novelty negatively influenced behavioral loyalty. Finally, male tourists tended to be more attitudinally and behaviorally loyal, while tourists who had children living with them showed less attitudinal loyalty. © SAGE Publications.
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.
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.
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.
Anomaly intrusion detection systems based on evolutionary computing
(2007) Surat Srinoy; Siriporn Chimphlee; Witcha Chimphlee
As malicious intrusions are a growing problem, we need a solution to detect the intrusions accurately. Network administrators are continuously looking for new ways to protect their resources from harm, both internally and externally. Intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic 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. The objective of this paper is to describe a fuzzy c-means and genetic algorithms and discuss its usage to detect intrusion in a computer network. We are using a Genetic Algorithms (GA) to select a subset of input features for clustering with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Experiments were performed with DARPA data sets, which have information on computer networks, during normal behavior and intrusive behavior.
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.
Biological activities and chemistry of saponins from Chenopodium quinoa Willd.
(2009) Tiwatt Kuljanabhagavad; Michael Wink; M. Wink; Department of Biology, Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg 69120, Im Neuenheimer Feld 364, Germany; email: wink@uni-hd.de
Chenopodium quinoa Willd. is a valuable food source which has gained importance in many countries of the world. The plant contains various bitter-tasting saponins which present an important antinutritional factor. Various triterpene saponins have been reported in C. quinoa including both monodesmosidic and bidesmosidic triterpene saponins of oleanolic acid, hederagenin, phytolaccagenic acid, and serjanic acid as the major aglycones and other aglycones as 3_-hydroxy-23-oxo-olean-12-en-28-oic acid, 3_-hydroxy-27-oxo-olean-12-en-28-oic acid, and 3_, 23_, 30_-trihydroxy-olean-12-en-28-oic acid. A tridesmosidic saponin of hederagenin has also been reported. Here we review the occurrence, analysis, chemical structures, and biological activity of triterpene saponins of C. quinoa. In particular, the mode of action of the mono- and bidesmosidic triterpene saponins and aglycones are discussed. © 2009 Springer Science+Business Media B.V.
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.
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.
Feature-based sentence extraction using fuzzy inference rules
(2009) Ladda Suanmali; Naomie Salim; Mohammed Salem Binwahlan; L. Suanmali; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, 10300, Thailand; email: ladda_sua@dusit.ac.th
Automatic text summarization is a wide research area. Automatic text summarization is to compress the original text into a shorter version and help the user to quickly understand large volumes of information. There are several ways in which one can characterize different approaches to text summarization: extractive and abstractive from single document or multi document. This paper focuses on the automatic text summarization by sentence extraction. The first step in summarization by extraction is the identification of important features. Our approach used important features based on fuzzy logic to extract the sentences. In our experiment, we used 30 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 8 important features and calculate their score for each sentence. We propose a method using fuzzy logic for sentence extraction and compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the highest average precision, recall, and F-measure for the summaries are conducted from fuzzy method. © 2009 IEEE.
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.
Fuzzy swarm based text summarization
(2009) Mohammed Salem Binwahlan; Naomie Salim; Ladda Suanmali
Problem statement: The aim of automatic text summarization systems is to select the most relevant information from an abundance of text sources. A daily rapid growth of data on the internet makes the achieve events of such aim a big challenge. Approach: In this study, we incorporated fuzzy logic with swarm intelligence; so that risks, uncertainty, ambiguity and imprecise values of choosing the features weights (scores) could be flexibly tolerated. The weights obtained from the swarm experiment were used to adjust the text features scores and then the features scores were used as inputs for the fuzzy inference system to produce the final sentence score. The sentences were ranked in descending order based on their scores and then the top n sentences were selected as final summary. Results: The experiments showed that the incorporation of fuzzy logic with swarm intelligence could play an important role in the selection process of the most important sentences to be included in the final summary. Also the results showed that the proposed method got a good performance outperforming the swarm model and the benchmark methods. Conclusion: Incorporating more than one technique for dealing with the sentence scoring proved to be an effective mechanism. The PSO was employed for producing the text features weights. The purpose of this process was to emphasize on dealing with the text features fairly based on their importance and to differentiate between more and less important features. The fuzzy inference system was employed to determine the final sentence score, on which the decision was made to include the sentence in the summary or not. © 2009 Science Publications.
Identifying zigzag based perceptually important points for indexing financial time series
(2009) Chaliaw Phetking; Mohd Noor Md. Sap; Ali Selamat; C. Phetking; Faculty of Science and Technology, Rajabhat Suan Dusit Unviersity, Wachira, Dusit, Bangkok, 10170, Thailand; email: chaliaw-phe@dusit.ac.th
Financial time series often exhibit high degrees of fluctuation which are considered as noise in time series analysis. To remove noise, several lower bounding the Euclidean distance based dimensionality reduction methods are applied. But, however, these methods do not meet the constraint of financial time series analysis that wants to retain the important points and remove others. Therefore, although a number of methods can retain the important points in the financial time series reduction, but, however, they loss the nature of financial time series which consist of several uptrends, downtrends and sideway trends in different resolutions and in the zigzag directions. In this paper, we propose the Zigzag based Perceptually Important Point Identification method to collect those zigzag movement important points. Further, we propose Zigzag based Multiway Search Tree to index these important points. We evaluate our methods in time series dimensionality reduction. The results show the significant performance comparing to other original method. © 2009 IEEE.
Improvement properties of recycled polypropylene by reinforcement of coir fiber
(Trans Tech Publications Ltd, 2009) P. Threepopnatkul; C. Kulsetthanchalee; K. Bunmee; N. Kliaklom; W. Roddouyboon; P. Threepopnatkul; Department of Materials Science and Engineering, Faculty of Engineering and Industrial Technology, Silpakom University, Nakom-pathom 73000, Thailand; email: poonsubt@yahoo.com
This research was to study the related mechanical and thermal properties of recycled polypropylene from post consumer containers reinforced with coir fiber. Surface of coir fiber was treated with sodium hydroxide to remove lignin and hemicelluloses and likely to improve the interfacial adhesion in the composites. The composites of treated coir fiber and recycled polypropylene were prepared by varying the coir fiber contents at 5%, 10% and 20% by weight using a twin screw extruder. The thermal properties were investigated by thermal gravimetric analysis (TGA) and differential scanning calorimeter (DSC). The results from TGA showed that thermal stability of the composites was lower than that of recycled polypropylene resin and thermal stability decreased with increasing coir fiber content. From DSC results, it indicated that the crystallinity of treated coir fiber composites increased as a function of fiber content. The mechanical properties of injection-molded samples were studied by universal testing machine. The treated coir fiber composites produced enhanced mechanical properties. The tensile strength, tensile modulus and impact strength of modified coir fiber/recycled polypropylene composites increased as a function of coir fiber content.
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.
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.
Integration Soft Computing Approach to Network Security
(Institute of Electrical and Electronics Engineers Inc., 2007) Surat Srinoy; S. Srinoy; Computer Science Department, Suan Dusit Rajabhat University, Bangkok, 295 Ratchasima Rd, Thailand; email: surat_sri@dusit.ac.th
Computer security is defined as the protection of computing system against threats to confidentiality, integrity, and availability. Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security. 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 this area. Lacking a distinctive boundary definition among normal and abnormal datasets, discriminating the normal and abnormal behaviors seems too much complex. This paper proposes an integrating support vector machine and rough set for recognizing intrusion detection in computer network. Empirical results clearly show that support vector machine and rough set approach could play a major role for intrusion detection systems. © 2007 IEEE.

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