Unsupervised Anomaly Detection with Unlabeled Data Using Clustering by Chimphlee, Witcha, Abdullah, Abdul Hanan, Md. Sap, Mohd. Noor

“…We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. …”
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Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

“…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. …”
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To identify suspicious activity in anomaly detection based on soft computing by Chimphlee, Witcha, Sap, M., Abdullah, Abdul Hanan, Chimphlee, Siriporn, Srinoy, Surat

“…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.…”

Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means by Chimphlee, Witcha, Md. Sap, Mohd. Noor, Abdullah, Abdul Hanan, Chimphlee, Siriporn

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Iterative window size estimation on self-similarity measurement for network traffic anomaly detection by Idris, Mohd. Yazid, Abdullah, Abdul Hanan, Maarof, Mohd. Aizaini

“…Thus, the purpose of this method is to minimize the curve-fitting error on self-similarity measurement and detection loss probability in anomaly detection. This iterative method was applied to network traffic data provided by Lincoln Lab, Massachuset Institute of Technology (MIT). …”

A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique by Dahliyusmanto, Dahliyusmanto, Herawan, Tutut, Syefrida Yulina, Syefrida Yulina, Abdullah, Abdul Hanan

Anomaly intrusion detection model using data mining techniques by Abdullah, Abdul Hanan, Rusli, Rozana

“…Evaluations are done using unsupervised anomaly detection schemes on the DARPA’98 data sets and real network traffic. …”

Fast optimization method: an on-line hurst parameter estimator by Idris, Mohd. Yazid, Abdullah, Abdul Hanan, Maarof, Mohd. Aizaini

“…The on-line Hurst estimator is crucial to characterize self-similar feature on stochastic process and widely applied in various fields such as in network traffic analysis, bandwidth provisioning and anomaly detection. Recent on-line Hurst estimator based on fast wavelet transform known as real-time wavelet estimator (RWM) is proven can estimates faster than other methods in on-line fashion. …”