Extracting discriminative features for identifying abnormal sequences in one-class mode
| Autor: | Cheng Wang, Junfu Yin, Longbing Cao, Yin Song |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Anomaly-based intrusion detection system
Computer science business.industry Feature vector Feature extraction Pattern recognition Intrusion detection system computer.software_genre Fault detection and isolation Generative model Discriminative model Anomaly detection Artificial intelligence Data mining business computer |
| Zdroj: | IJCNN |
| Popis: | This paper presents a novel framework for detecting abnormal sequences in an one-class setting (i.e., only normal data are available), which is applicable to various domains. Examples include intrusion detection, fault detection and speaker verification. Detecting abnormal sequences with only normal data presents several challenges for anomaly detection: the weak discrimination of normal and abnormal sequences; the unavailability of the abnormal data and other issues. Traditional model-based anomaly detection techniques can solve some of the above issues but with limited discrimination power (because of directly modeling the normal data). In order to enhance the discriminative power for anomaly detection, we turn to extracting discriminative features from the generative model based on the principle deducted from the corresponding theoretical analysis. Then a new anomaly detection framework is developed on top of that. The proposed approach firstly projects all the sequential data into a model-based equal length feature space (this is theoretically proven to have better discriminative power than the model itself), and then adopts a classifier learned from the transformed data to detect anomalies. Experimental evaluation on both the synthetic and real-world data shows that our proposed approach outperforms several anomaly detection baseline algorithms for sequential data. © 2013 IEEE. |
| Databáze: | OpenAIRE |
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