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Detecting Extreme Rank Anomalous Collections

by Hanbo Dai, Feida Zhu, Ee-peng Lim, Hwee Hwa Pang
"... Anomaly or outlier detection has a wide range of applications, including fraud and spam detection. Most existing studies focus on detecting point anomalies, i.e., individual, isolated entities. However, there is an increasing number of applications in which anomalies do not occur individually, but i ..."
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algorithms for finding top-K extreme rank anomalous collections. We apply the algorithms on real Web spam data to detect spamming sites, and on IMDB data to detect unusual actor groups. Our algorithms achieve higher precisions compared to existing spam and anomaly detection methods. More importantly, our

The pothole patrol: Using a mobile sensor network for road surface monitoring

by Jakob Eriksson, Lewis Girod, Bret Hull, Ryan Newton, Samuel Madden, Hari Balakrishnan - in ACM MobiSys , 2008
"... This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patr ..."
Abstract - Cited by 151 (4 self) - Add to MetaCart
This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole

S.: Video behavior profiling for anomaly detection

by Tao Xiang, Shaogang Gong - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2008
"... Abstract—This paper aims to address the problem of modeling video behavior captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior profiling and online anomaly sampling/detection without a ..."
Abstract - Cited by 57 (10 self) - Add to MetaCart
available based on an online Likelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. The effectiveness and robustness of our approach is demonstrated through experiments using noisy and sparse data sets collected

Revisit Network Anomaly Ranking in Datacenter Network Using Re-ranking

by unknown authors
"... Abstract—With the continuous growth of modern datacenter networks in recent years, network intrusions targeting those datacenters have also been growing rapidly. In this situation, system monitoring and intrusion detection become essential to control the risks of such networks. There are many networ ..."
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network anomaly detection systems being used to identify significant anomalies in datacenter networks. However, they often focus on detecting significant anomalies, while ignoring insignificant anomalies oftentimes. Existing anomaly ranking models are not accurate in detecting insignificant anomalies

Fusing Points and Lines for High Performance Tracking

by Edward Rosten, Tom Drummond - IN INTERNATIONAL CONFERENCE ON COMPUTER VISION , 2005
"... This paper addresses the problem of real-time 3D modelbased tracking by combining point-based and edge-based tracking systems. We present a careful analysis of the properties of these two sensor systems and show that this leads to some non-trivial design choices that collectively yield extremely hig ..."
Abstract - Cited by 151 (5 self) - Add to MetaCart
This paper addresses the problem of real-time 3D modelbased tracking by combining point-based and edge-based tracking systems. We present a careful analysis of the properties of these two sensor systems and show that this leads to some non-trivial design choices that collectively yield extremely

OddBall: Spotting Anomalies in Weighted Graphs

by Leman Akoglu, Mary Mcglohon, Christos Faloutsos
"... Abstract. Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ran ..."
Abstract - Cited by 77 (28 self) - Add to MetaCart
, ranks and eigenvalues that seem to govern the socalled “neighborhood sub-graphs ” and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design OddBall, so that it is scalable and it can work un-supervised (no user-defined constants) and (c) we report experiments

Anomaly detection in large graphs

by Leman Akoglu, Mary Mcglohon, Christos Faloutsos - In CMU-CS-09-173 Technical Report , 2009
"... Discovering anomalies is an important and challenging task for many settings, from network intrusion to fraud detection. However, most work to date has focused on clouds of multi-dimensional points, with little emphasis on graph data; even then, the focus is on un-weighted, node-labeled graphs. Here ..."
Abstract - Cited by 4 (2 self) - Add to MetaCart
. Here we propose OddBall, an algorithm to detect anomalous nodes in weighted graphs. The contributions are the following: (a) we carefully choose features, that easily reveal nodes with strange behavior; (b) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem

Histogram-Based Traffic Anomaly Detection

by Andreas Kind, Marc Ph. Stoecklin, Xenofontas Dimitropoulos
"... Identifying network anomalies is essential in enterprise and provider networks for diagnosing events, like attacks or failures, that severely impact performance, security, and Service Level Agreements (SLAs). Feature-based anomaly detection models (ab)normal network traffic behavior by analyzing dif ..."
Abstract - Cited by 15 (2 self) - Add to MetaCart
models, rather than using coarse entropy-based distribution approximations. We evaluate histogram-based anomaly detection and compare it to previous approaches using collected network traffic traces. Our results demonstrate the effectiveness of our technique in identifying a wide range of anomalies

Trustworthy Anomaly Detection for Smartphones

by Ingo Bente, Gabi Dreo, Bastian Hellmann, Joerg Vieweg
"... Due to the increasing level of utilization, smartphones are commonly used in enterprise environments nowadays. This introduces new threats to such environments like mo-bile malware with different behavior (mostly exfiltration of user information or abuse of premium services [1]). However, there is c ..."
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, there is currently no established way for an enterprise to as-sess the security state of connected smartphones in order to prevent or at least limit the potential amount of damage that compromised devices can cause. To circumvent this problem, we propose a novel network-based, distributed anomaly detection system

Unsupervised Spectral Ranking for Anomaly and Application to Auto Insurance Fraud Detection

by unknown authors
"... For many data mining problems, obtaining labels is costly and time consuming, if not practically infeasible. In addition, unlabeled data often includes categorical or ordinal features which, compared with numerical features, can present additional challenges. By establishing a connection between uns ..."
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unsupervised support vector machine optimiza-tion and spectral Laplacian optimization, we propose a new unsupervised spectral rank-ing method for anomaly (SRA). Using the 1st non-principal eigenvector of the Lapla-cian matrix directly, the proposed SRA can generate anomaly ranking either with re
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