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Intrusion-detection systems (IDSs) are essential tools for the security of computer systems. Anomaly detection, which uses knowledge about normal behaviors and attempts to detect intrusions by noting significant deviations, has been paid more and more attention. In this paper, we introduce a HMM-based method for anomaly detection. The proposed method is composed of two important stages: off-line training...
Intrusion-detection systems (IDSs) are essential tools for the security of computer systems. Anomaly detection, which uses knowledge about normal behaviors and attempts to detect intrusions by noting significant deviations, has been paid more and more attention. In this paper, we introduce a novel framework for anomaly detection. In the proposed method, two widely used statistical learning method,...
In this paper, we used Hidden Markov prediction tools to predict the state of the behavior of users in a ubiquitous home network. The state of the user's behavior presents a change of interest in the action of the user. This paper proposes a weight (WEIGHT) for the level of interest in the behavior and the strength of the relation between the behavior and interest, which is the formulation of the...
Traditionally, HMM-based approaches to online Kanji handwriting recognition have relied on a hand-made dictionary, mapping characters to primitives such as strokes or substrokes. We present an unsupervised way to learn a stroke tagger from data, which we eventually use to automatically generate such a dictionary. In addition to not requiring a prior hand-made dictionary, our approach can improve the...
Chord sequences are a compact and useful description of music, representing each beat or measure in terms of a likely distribution over individual notes without specifying the notes exactly. Transcribing music audio into chord sequences is essential for harmonic analysis, and would be an important component in content-based retrieval and indexing, but accuracy rates remain fairly low. In this paper,...
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