FL-MGVN: Federated learning for anomaly detection using mixed gaussian variational self-encoding network |
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| Institution: | 1. Academic Affairs Office, Chongqing Normal University, Chongqing, PR China;2. School of Computer and Information Science, Chongqing Normal University, Chongqing, PR China;1. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan;2. Department of Medical Research, China Medical University Hospital, China Medical University Taichung, Taiwan;3. Department of Information Technology, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia;4. Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia;5. Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan;6. Graduate Institute of Acupuncture Science, China Medical University, Taichung, Taiwan;7. Chinese Medicine Research Center, China Medical University, Taichung, Taiwan;8. School of Post-Baccalaureate Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan;9. Department of Chinese Medicine, Asia University Hospital, Taichung, Taiwan;10. Department of Food Nutrition and Health Biotechnology, Taichung, Asia University, Taiwan;1. AGH University of Science and Technology, 30 Mickiewicza Ave, Kraków 30-059, Poland;2. VSB Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic;1. School of Economics and Management, Chang''an University, Xi''an 710064, China;2. Computer & Information Sciences Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia;3. Institute of IR4.0, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia;4. College of Engineering, Al Ain University, Al Ain, United Arab Emirates;5. Department of Mathematics, College of Science, Tafila Technical University, Tafila, Jordan;1. School of Information and Communication Engineering, Hunan Institute of Science and Technology, Hunan, China;2. Machine Vision & Artificial Intelligence Research Center, Hunan Institute of Science and Technology, Hunan, China |
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| Abstract: | Anomalous data are such data that deviate from a large number of normal data points, which often have negative impacts on various systems. Current anomaly detection technology suffers from low detection accuracy, high false alarm rate and lack of labeled data. Anomaly detection is of great practical importance as an effective means to detect anomalies in the data and provide important support for the normal operation of various systems. In this paper, we propose an anomaly detection classification model that incorporates federated learning and mixed Gaussian variational self-encoding networks, namely MGVN. The proposed MGVN network model first constructs a variational self-encoder using a mixed Gaussian prior to extracting features from the input data, and then constructs a deep support vector network with the mixed Gaussian variational self-encoder to compress the feature space. The MGVN finds the minimum hypersphere to separate the normal and abnormal data and measures the abnormal fraction by calculating the Euclidean distance between the data features and the hypersphere center. Federated learning is finally incorporated with MGVN (FL-MGVN) to effectively address the problems that multiple participants collaboratively train a global model without sharing private data. The experiments are conducted on the benchmark datasets such as NSL-KDD, MNIST and Fashion-MNIST, which demonstrate that the proposed FL-MGVN has higher recognition performance and classification accuracy than other methods. The average AUC on MNIST and Fashion-MNIST reached 0.954 and 0.937, respectively. |
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