Browsing by Author "Mohd Noor Md. Sap"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item 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.thIt 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.Item 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.thFinancial 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.Item 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.thTo 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.