EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection
Institute of Innovation, Science and Sustainability, Federation University Australia（创新、科学与可持续性研究所，联邦大学澳大利亚）；School of Software, Dalian University of Technology（软件学院，大连理工大学）；Computing Center, Anshan Normal University（计算机中心，鞍山师范学院）
图异常检测是各类真实场景中一项受欢迎且至关重要的任务，相关研究已持续数十年。近年来，许多扩展基于深度学习方法的研究在图异常检测上展现出较好的性能。然而，现有方法缺乏效率，而效率对于嵌入式设备无疑是必需的。为此，我们提出了一种通过对比学习实现的异质图高效异常检测模型（Efficient Anomaly detection model on heterogeneous Graphs via contrastive LEarning，EAGLE），该模型根据异常节点与正常节点到局部上下文的距离对它们进行对比。所提方法首先在元路径层级采样实例对，以用于对比学习。随后，采用基于图自编码器的模型以无监督方式学习信息丰富的节点嵌入，并进一步将其与判别器结合，以预测节点的异常分数。实验结果表明，EAGLE在三个异质网络数据集上优于最先进方法。
英文摘要：
Graph anomaly detection is a popular and vital task in various real-world scenarios, which has been studied for several decades. Recently, many studies extending deep learning-based methods have shown preferable performance on graph anomaly detection. However, existing methods are lack of efficiency that is definitely necessary for embedded devices. Towards this end, we propose an Efficient Anomaly detection model on heterogeneous Graphs via contrastive LEarning (EAGLE) by contrasting abnormal nodes with normal ones in terms of their distances to the local context. The proposed method first samples instance pairs on meta path-level for contrastive learning. Then, a graph autoencoder-based model is applied to learn informative node embeddings in an unsupervised way, which will be further combined with the discriminator to predict the anomaly scores of nodes. Experimental results show that EAGLE outperforms the state-of-the-art methods on three heterogeneous network datasets.
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
Department of Data Science and AI, Monash University（数据科学与人工智能系，墨尔本大学）；Department of Computer Science, University of Oxford（计算机科学系，牛津大学）；IBM T. J. Watson Research Center（IBM T. J. Watson研究中心）；Squirrel Ai Learning（Squirrel Ai Learning公司）；School of ICT, Griffith University（信息科技学院，格里菲斯大学）
Deep Contrastive One-Class Time Series Anomaly Detection
Beihang University（北京航空航天大学）；University of New South Wales（新南威尔士大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；China University of Geosciences in Beijing（中国地质大学（北京））
ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling
Munich Center for Machine Learning（慕尼黑机器学习中心）；LMU München（慕尼黑大学）；German Research Center for Environmental Health, Helmholtz Zentrum München（德国环境健康研究中心，亥姆霍兹慕尼黑中心）
Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dataset sizes limit its effectiveness. It was previously shown that utilizing external, generic datasets (e.g. ImageNet classification) can significantly improve anomaly detection performance. One approach is outlier exposure, which fails when the external datasets do not resemble the anomalies. We take the approach of transferring representations pre-trained on external datasets for anomaly detection. Anomaly detection…
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
University of Chinese Academy of Science（中国科学院大学）；Tsinghua University（清华大学）；Shanghai University of International Business and Economics（上海国际商务经济学院）；Northeast Normal University（东北师范大学）；Fuzhou University（福州市大学）；Hubei University（湖北大学）；Hainan University（海南大学）；Northeastern University（东北大学）
Detecting anomalies is one fundamental aspect of a safety-critical software system, however, it remains a long-standing problem. Numerous branches of works have been proposed to alleviate the complication and have demonstrated their efficiencies. In particular, self-supervised learning based methods are spurring interest due to their capability of learning diverse representations without additional labels. Among self-supervised learning tactics, contrastive learning is one specific framework validating their superiority in various fields, including anomaly detection. However, the primary objec…
La Trobe University（乐卓博大学）；Monash University（蒙纳士大学）
Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Existing data mining and machine learning methods are either shallow methods that could not effectively capture the complex interdependency of graph data or graph autoencoder methods that could not fully exploit the contextual information as supervision signals for effective anomaly detection. To overcome these challenges, in this paper, we propose a novel method, Self-Supervised Learning for Graph Anomaly Detection (S…
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Zhongguancun Laboratory（中关村实验室）；School of Software, Beihang University（北京航空航天大学软件学院）
Monash University（莫纳什大学）；Alibaba Group（阿里巴巴集团）；Nanjing University of Science and Technology（南京理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Chinese Academy of Sciences（中国科学院）；University of Minnesota（明尼苏达大学）
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomal…
Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；Mininglamp Technology（明略科技）；Bosch Center for Artificial Intelligence（博世人工智能中心）；University of Oslo（奥斯陆大学）
Graph-based anomaly detection has been widely used for detecting malicious activities in real-world applications. Existing attempts to address this problem have thus far focused on structural feature engineering or learning in the binary classification regime. In this work, we propose to leverage graph contrastive coding and present the supervised GraphCAD model for contrasting abnormal nodes with normal ones in terms of their distances to the global context (e.g., the average of all nodes). To handle scenarios with scarce labels, we further enable GraphCAD as a self-supervised framework by de…
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning
Northeastern University（东北大学）；Illinois Institute of Technology（伊利诺伊理工学院）；Rochester Institute of Technology（罗切斯特理工学院）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）；University of Southern California（南加利福尼亚大学）；University of Michigan（密歇根大学）
Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal…
SenseTime Research（商汤科技研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加州大学洛杉矶分校）
The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing. Meanwhile, the anomalies in real-world are usually subtle and fine-grained in a high-resolution image especially for industrial applications. Towards this end, we propose a novel framework for unsupervised anomaly detection and localization. Our method aims at learning dense and compact distribution from normal images with a coarse-to-fine alignment process. The coarse alignment stage standardizes the pixel-wise position of objects in both image and …
Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from normal driving) contains the utmost importance to reduce driver-related accidents. However, there are unbounded many anomalous actions that a driver can do while driving, which leads to an 'open set recognition' problem. Accordingly, instead of recognizing a set of anomalous actions that are commonly defined by previous dataset providers, in this work, we propose a contrastive learning approach to learn a metric to …
Predictive Reasoning with Augmented Anomaly Contrastive Learning for Compositional Visual Relations
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；Institute of Biomedical Engineering, Ningbo institute of materials technology and engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所生物医学工程研究所）；School of Electrical & Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning
School of Computer Science and Technology, Xi'an Jiaotong University（西安交通大学计算机科学与技术学院）；Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University（陕西省大数据知识工程重点实验室）；School of Distance Education, Xi’an Jiaotong University（西安交通大学继续教育学院）；University of Virginia（弗吉尼亚大学）
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究 institute）；Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada（格拉纳达大学计算机科学与人工智能系）
Hamburg University of Technology（汉堡工业大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University Heart and Vascular Center（大学心血管中心）
Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume, shape and other parameters like local contrast. However due to limited training data, traditional supervised learning methods often fail to generalize. Existing deep learning methods in paranasal anomaly classification have been used to diagnose at most one anomaly. In our work, we consider three anomalies. Specifically, we employ a 3D CNN to separate maxillary sinus volumes without anomalies from maxillary sinus v…
Huazhong University of Science and Technology（华中科技大学）
The rapid development of Industry 4.0 has amplified the scope and destructiveness of industrial Cyber-Physical System (CPS) by network attacks. Anomaly detection techniques are employed to identify these attacks and guarantee the normal operation of industrial CPS. However, it is still a challenging problem to cope with scenarios with few labeled samples. In this paper, we propose a few-shot anomaly detection model (FSL-PN) based on prototypical network and contrastive learning for identifying anomalies with limited labeled data from industrial CPS. Specifically, we design a contrastive loss t…
Virginia Tech（弗吉尼亚理工大学）；Stevens Institute of Technology（史蒂文斯理工学院）；DeepSig Inc（DeepSig公司）
Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. To this end, we propose a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs contrastive learning in an adversarial setup to learn effective representations of normal and anomalous behavior in wireless networks. We conduct rigorous performance comparisons of CAAD with several st…
TU Kaiserslautern（凯泽斯劳滕工业大学）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）
In this paper, we introduce the Vehicle Claims dataset, consisting of fraudulent insurance claims for automotive repairs. The data belongs to the more broad category of Auditing data, which includes also Journals and Network Intrusion data. Insurance claim data are distinctively different from other auditing data (such as network intrusion data) in their high number of categorical attributes. We tackle the common problem of missing benchmark datasets for anomaly detection: datasets are mostly confidential, and the public tabular datasets do not contain relevant and sufficient categorical attri…
