Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares

Ahmed, Tarem; Coates, Mark; Lakhina, Anukool

doi:10.1109/infcom.2007.79

Cited by 115 publications

(69 citation statements)

References 12 publications

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“…They extended this work in [17] where KOAD, an incremental learning approach for real-time diagnosis of volume anomalies. KOAD exploits temporal variations in the structure of multivariate network traffic.…”

Section: A Network Monitoringmentioning

confidence: 99%

Adaptive Anomaly Detection in Performance Metric Streams

Ibidunmoye

Rezaie

Elmroth

2018

IEEE Trans. Netw. Serv. Manage.

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Abstract-Continuous detection of performance anomalies such as service degradations has become critical in cloud and Internet services due to impact on quality of service and end-user experience. However, the volume and fast changing behaviour of metric streams have rendered it a challenging task. Many diagnosis frameworks often rely on thresholding with stationarity or normality assumption, or on complex models requiring extensive offline training. Such techniques are known to be prone to spurious false-alarms in online settings as metric streams undergo rapid contextual changes from known baselines. Hence, we propose two unsupervised incremental techniques following a two-step strategy. First, we estimate an underlying temporal property of the stream via adaptive learning and, then we apply statistically robust control charts to recognize deviations. We evaluated our techniques by replaying over 40 time-series streams from the Yahoo! Webscope S5 datasets as well as 4 other traces of real web service QoS and ISP traffic measurements. Our methods achieve high detection accuracy and few false-alarms, and better performance in general compared to an open-source package for time-series anomaly detection.

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Section: A Network Monitoringmentioning

confidence: 99%

Adaptive Anomaly Detection in Performance Metric Streams

Ibidunmoye

Rezaie

Elmroth

2018

IEEE Trans. Netw. Serv. Manage.

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“…If the points {x t } T t=1 show normal behaviour in the input space, then the corresponding feature vectors {φ(x t )} T t=1 are expected to (also) cluster [3]. Then, it should be possible to explain the region of normality in the feature space using a relatively small dictionary of approximately linearly independent elements {φ(x j )} m j=1 .…”

Section: Kernel-based Online Anomaly Detection Algorithmmentioning

confidence: 99%

Automated Surveillance in Distributed, Visual Networks: An Empirical Comparison of Recent Algorithms

Ahmed¹,

Ahmed²,

Pathan³

2014

IJCA

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A number of algorithms have been recently proposed for automatic intruder detection from CCTV images. Past researchers have typically tested these algorithms on centralized networks where all images are transmitted to a central control room. This paper demonstrates the applicability of a selection of such algorithms to a distributed network of wireless sensors. A distributed network of wireless visual sensors was simulated using a number of high-resolution webcams setup in the hallways of an academic building. The selected algorithms were then applied in a distributed fashion at each node. An empirical comparison of the most popular of the recent algorithms on a simulation of a wireless sensor network was thus obtained. This paper provides corroborating evidence in support of the most effective of such algorithms to the problem of automatic anomaly detection from image streams.

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“…Machine learning methods are usually employed to model what constitutes a nominal behavior and deriving from the representation of the nominal behavior the abnormal behavior. For example, Ahmed et al ([3], [2]) investigate the use of two distinct machine learning approaches, namely the block-based One-Class Neighbor Machine and the recursive Kernel-based Online Anomaly Detection algorithms, to detect network anomaly. Yet, as often happens in machine learning techniques, their models are constrained and cannot be easily adapted to other domains.…”

Section: Related Workmentioning

confidence: 99%

Detecting anomalies in unmanned vehicles using the Mahalanobis distance

Lin

Khalastchi

Kaminka

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-The use of unmanned autonomous vehicles is becoming more and more significant in recent years. The fact that the vehicles are unmanned (whether autonomous or not), can lead to greater difficulties in identifying failure and anomalous states, since the operator cannot rely on its own body perceptions to identify failures. Moreover, as the autonomy of unmanned vehicles increases, it becomes more difficult for operators to monitor them closely, and this further exacerbates the difficulty of identifying anomalous states, in a timely manner. Model-based diagnosis and fault-detection systems have been proposed to recognize failures. However, these rely on the capabilities of the underlying model, which necessarily abstracts away from the physical reality of the robot. In this paper we propose a novel, model-free, approach for detecting anomalies in unmanned autonomous vehicles, based on their sensor readings (internal and external). Experiments conducted on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) demonstrate the efficacy of the approach by detecting the vehicles deviations from nominal behavior.

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Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares

Cited by 115 publications

References 12 publications

Adaptive Anomaly Detection in Performance Metric Streams

Adaptive Anomaly Detection in Performance Metric Streams

Automated Surveillance in Distributed, Visual Networks: An Empirical Comparison of Recent Algorithms

Detecting anomalies in unmanned vehicles using the Mahalanobis distance

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