volume 9, issue 7, P904953 2013
DOI: 10.1155/2013/904953
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Abstract: We present a lightweight and scalable method for classifying network and program traces to detect system intrusion attempts. By employing interelement dependency models to overcome the independence violation problem inherent in the Naive Bayes learners, our method yields intrusion detectors with better accuracy. For efficient and lightweight counting of n-gram features without losing accuracy, we use a k-truncated generalized suffix tree ( k-TGST) for storing n-gram features. The k-TGST storage mechanism enab…

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