Cluster-based Deep One-Class Classification Model for Anomaly Detection,ERICDATA高等教育知識庫
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篇名
Cluster-based Deep One-Class Classification Model for Anomaly Detection
並列篇名
Cluster-based Deep One-Class Classification Model for Anomaly Detection
作者 Younghwan KimHuy Kang Kim
英文摘要
As cyber-attacks on Cyber-Physical System (CPS) become more diverse and sophisticated, it is important to quickly detect malicious behaviors occurring in CPS. Since CPS can collect sensor data in near real time throughout the process, there have been many attempts to detect anomaly behavior through normal behavior learning from the perspective of data-driven security. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) model using only a normal dataset for anomaly detection. We use auto-encoder to reduce the dimensions of the dataset and the K-means clustering algorithm to classify the normal data into the optimal cluster size. The DL model trains to predict clusters of normal data, and we can obtain logit values as outputs. The derived logit values are datasets that can better represent normal data in terms of knowledge distillation and are used as inputs to the OCC model. As a result of the experiment, the F1 score of the proposed model shows 0.93 and 0.83 in the SWaT and HAI dataset, respectively, and shows a significant performance improvement over other recent detectors such as Com-AE and SVM-RBF.
起訖頁 903-911
關鍵詞 Anomaly detectionKnowledge distillationClusteringDeep learning
刊名 網際網路技術學刊  
期數 202107 (22:4期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642021072204017  複製DOI
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