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

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

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

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

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

, 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

. 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

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

, 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 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