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Full-Text Articles in Engineering
A Machine Learning Approach To Anomaly Detection, Philip K. Chan, Matthew V. Mahoney, Muhammad H. Arshad
A Machine Learning Approach To Anomaly Detection, Philip K. Chan, Matthew V. Mahoney, Muhammad H. Arshad
Electrical Engineering and Computer Science Faculty Publications
Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can construct anomaly detection models from past behavior. The first method is a rule learning algorithm that characterizes normal behavior in the absence of labeled attack data. The second method uses a clustering algorithm to identify outliers.
Efficient Decomposition Of Large Fuzzy Functions And Relations, Paul Burkey, Marek Perkowski
Efficient Decomposition Of Large Fuzzy Functions And Relations, Paul Burkey, Marek Perkowski
Electrical and Computer Engineering Faculty Publications and Presentations
This paper presents a new approach to decomposition of fuzzy functions. A tutorial background on fuzzy logic representations is first given to emphasize next the simplicity and generality of this new approach. Ashenhurst-like decomposition of fuzzy functions was discussed in [3] but it was not suitable for programming and was not programmed. In our approach, fuzzy functions are converted to multiple-valued functions and decomposed using an mv decomposer. Then the decomposed multiple-valued functions are converted back to fuzzy functions. This approach allows for Curtis-like decompositions with arbitrary number of intermediate fuzzy variables, that have been not presented for fuzzy functions …