学术报告-Multivariate Anomaly Detection with Self-Learning Graph Convolutional Networks
报告题目：MAD-SGCN: Multivariate Anomaly Detection with Self-Learning Graph Convolutional Networks
报告专家：See-Kiong Ng（黄思强）新加坡国立大学计算学院教授
报告时间：2021年12月24日 上午10:00
报告地点：腾讯会议(ID: 977116485)
主办单位：中国矿业大学 计算机科学与技术学院
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Abstract:
Today’s Cyber Physical Systems (CPSs) are large, complex data-intensive systems requiring constant monitoring and analysis of data generated by a multitude of interconnected sensors and actuators to detect anomalies due to possible intrusions or faults with high accuracy and timeliness. Recently, unsupervised anomaly detection techniques based on deep learning for multivariate time series have been proposed for detecting CPSs attacks and showed promising performance. However, current deep learning-based unsupervised anomaly detection methods are either limited by their representation learning methods in encoding the temporal and spatial information simultaneously and effectively, or they cannot easily scale to other tasks without having explicit knowledge of the internal relationships between the different variables or sensors, both of which are important for characterising CPS data. In this paper, we propose a novel unsupervised anomaly detection method MAD-SGCN for multivariate time series, in which the temporal and spatial correlations of each input sequence are captured by Long Short-Term Memory (LSTM) and spectral-based Graph convolutional network (GCN). We design a self-supervised graph structure learning mechanism to minimize the usage of the prior knowledge about the network structures of the CPSs. Experiments on four CPS datasets demonstrate the superiority of the proposed method.
Speaker bio:
See-Kiong Ng (Ph.D. Carnegie Mellon University) is a Professor of Practice at the Department of Computer Science of the School of Computing at National University of Singapore (NUS), and the Deputy Director of the university’s Institute of Data Science. See-Kiong obtained his Bachelor (1989), Masters (1993), and Ph.D. (1998) degrees in Computer Science from Carnegie Mellon University and a Masters (1991) degree in Artificial Intelligence from University of Pennsylvania. Prior to joining NUS in 2016, See-Kiong was a Programme Director of the Urban Systems Initiative by the Science and Engineering Research Council of the Agency of Science, Technology and Research (A*STAR), and the founding head and principle scientist of its Data Mining Department. Currently, See-Kiong’s mission at NUS is to leverage his data science and AI expertise for transdisciplinary and translational research into important real-life problems and education of the next generation of data scientists. From using the computation of data to better understand the biology of the human body, See-Kiong is using machine learning and artificial intelligence to understand the “biology” of complex human cities and societies and creating real-world impact with the science of data.
讲题：基于自学习图卷积网络的多元异常检测
讲座大纲：
当今的信息物理系统 (CPS) 是大型、复杂的数据密集型系统，需要对大量互相关联传感器和执行器生成的数据进行持续监控和分析，以高精度和及时地检测由于可能的入侵或故障引起的异常情况。最近，已经提出了基于多元时间序列深度学习的无监督异常检测技术被提出用来检测 CPS 攻击，并表现出良好的性能。然而，目前基于深度学习的无监督异常检测方法，要么受到表征学习方法的限制，无法同时有效地对时空信息进行编码；要么在没有明确了解不同变量之间的内部关系的情况下，无法轻松扩展到其他任务或传感器，而这两者对于表征 CPS 数据都很重要。在本次报告中，我们提出了一种用于多元时间序列的新型无监督异常检测方法 MAD-SGCN，其中每个输入序列的时间和空间相关性由长短期记忆 (LSTM) 和基于频谱的图卷积网络捕获(GCN) 。设计了一种自监督的图结构学习机制，以最大限度地减少对 CPS 网络结构的先验知识的使用。在四个 CPS 数据集上的实验证明了所提出方法的优越性。
主讲人简介：
黄思强博士为新加坡国立大学计算学院计算机科学系教授，新加坡国立大学数据科学研究所副所长。1986年，他获得新加坡国家电脑局海外奖学金赴美国学习，先后获得卡内基梅隆大学的计算机科学学士（1989年）、硕士（1993年）和博士（1998年）学位，以及宾夕法尼亚大学的人工智能硕士学位（1991年）。黄博士曾担任新加坡科技研究局（A*STAR）组织的城市体系计划主任，并担任了新科研信息通信研究所数据分析部门的创始负责人和首席科学家。目前，黄博士于国大新设立的数据科学研究所，着力为新加坡培养新一代杰出的数据科学家，并与多个行业与公共机构进行成功的研究合作，以数据科学开发实用的人工智能技术。
作为一名经验丰富的数据科学家，从使用数据挖掘和机器学习来揭示人体的生物学（生物信息学），到使用大数据和人工智能来理解复杂的人类城市（智能城市），黄博士以数据的科学与应用证明了数据中的巨大价值。
