本文提出了一种使用自回归因果注意力模型进行轨迹异常检测的新方法，称为LM-TAD。该方法利用了语言陈述与轨迹之间的相似性，两者均由需要通过外部规则和上下文变化来保持连贯性的有序元素组成。通过将轨迹视为token序列，我们的模型学习了轨迹上的概率分布，从而能够高精度地识别异常位置。我们引入了用户特定的token以考虑个体行为模式，增强了针对用户上下文定制的异常检测能力。我们的实验证明了LM-TAD在合成和真实数据集上的有效性。特别是，该模型在Pattern of Life (PoL)数据集上通过检测用户上下文异常而优于现有方法，并在Porto出租车数据集上取得了有竞争力的结果，突显了其适应性和鲁棒性。此外，我们引入了困惑度和惊奇率指标，用于检测异常值并精确定位轨迹内的特定异常位置。LM-TAD框架支持多种轨迹表示，包括GPS坐标、停留点和活动类型，证明了其在处理多样化轨迹数据方面的多功能性。此外，我们的方法非常适合在线轨迹异常检测，通过缓存注意力机制的键值状态来显著减少计算延迟，从而避免重复计算。
英文摘要：
This paper presents a novel approach for trajectory anomaly detection using an autoregressive causal-attention model, termed LM-TAD. This method leverages the similarities between language statements and trajectories, both of which consist of ordered elements requiring coherence through external rules and contextual variations. By treating trajectories as sequences of tokens, our model learns the probability distributions over trajectories, enabling the identification of anomalous locations with high precision. We incorporate user-specific tokens to account for individual behavior patterns, enhancing anomaly detection tailored to user context. Our experiments demonstrate the effectiveness of LM-TAD on both synthetic and real-world datasets. In particular, the model outperforms existing methods on the Pattern of Life (PoL) dataset by detecting user-contextual anomalies and achieves competitive results on the Porto taxi dataset, highlighting its adaptability and robustness. Additionally, we introduce the use of perplexity and surprisal rate metrics for detecting outliers and pinpointing specific anomalous locations within trajectories. The LM-TAD framework supports various trajectory representations, including GPS coordinates, staypoints, and activity types, proving its versatility in handling diverse trajectory data. Moreover, our approach is well-suited for online trajectory anomaly detection, significantly reducing computational latency by caching key-value states of the attention mechanism, thereby avoiding repeated computations.
Trajectory Prediction (TP) is an important research topic in computer vision and robotics fields. Recently, many stochastic TP models have been proposed to deal with this problem and have achieved better performance than the traditional models with deterministic trajectory outputs. However, these stochastic models can generate a number of future trajectories with different qualities. They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results. This hinders them …
CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection
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）；DiDi Global Inc.（滴滴全球公司）
Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. However, the labeled data often does not align with the target distribution and introduces harmful bias to the trained model. In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection. Concretely, we view anomaly detection as a supervised learning task where the objective is to optimize the recall at a given …
Research and application of Transformer based anomaly detection model: A literature review
Huazhong University of Science and Technology（华中科技大学）；School of Cyber Science and Engineering（网络科学与工程学院）；School of Artificial Intelligence and Automation（人工智能与自动化学院）
Utah State University（犹他州立大学）；University of Arkansas（阿肯色大学）
Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection. In the past decade, a variety of anomaly detection models have been developed, which lead to big progress towards accurately detecting various anomalies. Despite the successes, anomaly detection models still face many limitations. The most significant one is whether we can trust the detection results from the models. In recent years, the research community has spent a great effort to design trustworthy machine learning models, such as developing trustworthy classification …
School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；Department of Physics, University of Montreal（蒙特利尔大学物理系）；Mila - Quebec AI Institute（魁北克人工智能研究所）
Northwestern University（西北大学）；University of California, Irvine（加州大学欧文分校）
Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations are introduced to those history trajectories, the resulting anomalous (or adversarial) trajectories can significantly mislead the future trajectory prediction module of the ego vehicle, which may result in unsafe planning and even fatal accidents. Therefore, it is of great importance to detect such anomalous trajectories of the surrounding vehicles for system safety, but few works have addressed this issue. In this wo…
Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods is challenging because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the response variable or the class labels cannot be used to construct an ensemble for unsupervised anomaly detection. We use Item Response Theory (IRT) -- a class of models used in educational psychometrics to assess student and test question characteristics -- to construct an unsupervised anomaly detection ensemble. IRT's latent trait computation lends itself to anomaly detection becau…
Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science. Recently, deep anomaly detection methods were shown to achieve superior results particularly in complex data such as images. Our work focuses on deep one-class classification for anomaly detection which learns a mapping only from the normal samples. However, the non-linear transformation performed by deep learning can potentially find patterns associated with social bias. The challenge with adding fairness to deep anomaly detection is to ensure both making fair and correct anomaly…
This paper presents a novel framework for unsupervised anomaly detection on masked objects called ODDObjects, which stands for Out-of-Distribution Detection on Objects. ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The method utilizes autoencoder-based image reconstruction, where high reconstruction error indicates the possibility of an anomaly. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets. Various model archi…
UIUC（伊利诺伊大学厄巴纳-香槟分校）；KAIST（韩国科学技术院）；University of Vermont（佛蒙特大学）
Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based infrastructures. Unfortunately, existing approaches fall too short for these challenges; online anomaly detection methods bear the burden of handling the complexity while offline deep anomaly detection methods suffer from the evolving data distribution. This paper presents a framework for online deep anomaly detection, ARCUS, which can be instantiated with any autoencoder-based deep ano…
Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of…
Utah State University（犹他州立大学）；University of Arkansas（阿肯色大学）；Clemson University（克莱姆森大学）
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings. However, existing fair anomaly detection approaches mainly focus on association-based fairness notions. In this work, we target counterfactual fairness, which is a prevalent causation-based fairness notion. The goal of counterfactually fair anomaly detection is to ensure that the detection outcome of an individual in the factual world is the same as that in the counterfactual world where the individual had belonged to a different group. To this end, we…
We proposed a multivariate time series anomaly detection frame-work Ymir, which leverages ensemble learning and supervisedlearning technology to efficiently learn and adapt to anomaliesin real-world system applications. Ymir integrates several currentlywidely used unsupervised anomaly detection models through anensemble learning method, and thus can provide robust frontalanomaly detection results in unsupervised scenarios. In a super-vised setting, domain experts and system users discuss and providelabels (anomalous or not) for the training data, which reflects theiranomaly detection criteria …
University of Jinan（济南大学）；Shandong Jiaotong University（山东交通学院）
Unsupervised representation learning has been extensively employed in anomaly detection, achieving impressive performance. Extracting valuable feature vectors that can remarkably improve the performance of anomaly detection are essential in unsupervised representation learning. To this end, we propose a novel discriminative feature learning framework with gradient preference for anomaly detection. Specifically, we firstly design a gradient preference based selector to store powerful feature points in space and then construct a feature repository, which alleviate the interference of redundant f…
University of Granada（格拉纳达大学）；ArcelorMittal Global R&D（安赛乐米塔尔全球研发中心）
The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a special case, where the anomaly represents a failure that must be prevented. Related time-series research as outlier and novelty detection or time-series classification does not apply to the concept of an anomaly in this field, because they are not single points which have not been seen previously and may not be precisely annotated. Moreover, due to…
Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT) and Simple Feedforward Neural Network. The effectiveness of each algorithm was evaluated and compared…
Dive into Time-Series Anomaly Detection: A Decade Review
Inria（法国国家信息与自动化研究所）；DI ENS（巴黎高等师范学院数字研究所）；PSL（巴黎文理研究大学）；CNRS（法国国家科学研究中心）；The Ohio State University（俄亥俄州立大学）；Université Paris Cité（巴黎西岱大学）；IUF（法国大学研究院）
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
Department of Electrical and Computer Engineering, McGill University, Canada（麦吉尔大学电气与计算机工程系，加拿大）；Mila - Quebec Artificial Intelligence Institute, Montreal, Canada（魁北克人工智能研究所，蒙特利尔，加拿大）
NTT Communication Science Laboratories（NTT通信科学实验室）；NTT Software Innovation Center（NTT软件创新中心）
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measure the performance with inexact anomaly labels, we define the inexact AUC, which is our extension of the area under the ROC curve (AUC) for inexact labels. The proposed method trains an anomaly score function so that the smooth approximation of the inexact AUC increases while anom…
With the high requirements of automation in the era of Industry 4.0, anomaly detection plays an increasingly important role in higher safety and reliability in the production and manufacturing industry. Recently, autoencoders have been widely used as a backend algorithm for anomaly detection. Different techniques have been developed to improve the anomaly detection performance of autoencoders. Nonetheless, little attention has been paid to the latent representations learned by autoencoders. In this paper, we propose a novel selection-and-weighting-based anomaly detection framework called SWAD.…
We develop a supervised machine learning model that detects anomalies in systems in real time. Our model processes unbounded streams of data into time series which then form the basis of a low-latency anomaly detection model. Moreover, we extend our preliminary goal of just anomaly detection to simultaneous anomaly prediction. We approach this very challenging problem by developing a Bayesian Network framework that captures the information about the parameters of the lagged regressors calibrated in the first part of our approach and use this structure to learn local conditional probability dis…
Anomaly detection is concerned with identifying examples in a dataset that do not conform to the expected behaviour. While a vast amount of anomaly detection algorithms exist, little attention has been paid to explaining why these algorithms flag certain examples as anomalies. However, such an explanation could be extremely useful to anyone interpreting the algorithms' output. This paper develops a method to explain the anomaly predictions of the state-of-the-art Isolation Forest anomaly detection algorithm. The method outputs an explanation vector that captures how important each attribute of…
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during training. Recent advances in self-supervised representation learning have directly driven improvements in anomaly detection. In this position paper, we first explain how self-supervised representations can be easily used to achieve state-of-the-art performance in commonly reported anomaly detection benchmarks. We then argue that tackling the next generation of anomaly detection tasks requires new technical and conceptu…
Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on u…
NTT Communication Science Laboratories（NTT通信科学实验室）；NTT Secure Platform Laboratories（NTT安全平台实验室）
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been widely used for unsupervised anomaly detection. By the recent advance of deep learning, the density estimation performance has been greatly improved. However, the neural density estimators cannot exploit anomaly label information, which would be valuable for improving the anomaly detection performance. The proposed method effectively utilizes the anomaly label information by training the neural density estimator so th…
Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we propose the spatio-temporal graph auto-encoder for learning normal driving behaviours. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents. To perform anomaly detection, we first estimate a density function of the learned trajectory feature representation and then detect anomalies in low-density regions. Due to the lack of multi-agent trajectory datasets for anomaly detec…
General Electric Global Research Center（通用电气全球研究中心）；General Electric Power & Water Engineering（通用电气电力与水务工程部门）
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection pe…
