Statistical anomaly detection with sensor networks

Paschalidis, Ioannis Ch.; Yin, Chen

doi:10.1145/1824766.1824773

Cited by 28 publications

(16 citation statements)

References 12 publications

(15 reference statements)

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“…Paschalidis et al [26] use Markov models to characterise the normal behaviour of sensor networks; that is, a Markov model at each sensor node is built to estimate anomaly-free probabilities from its past observation traces, and a tree-indexed Markov model is developed to capture their spatial correlations across the network. Based on derived optimal anomaly detection rules, the approach can assess whether its most recent empirical measure is consistent with the anomaly-free probability model.…”

Section: Learning-based Methodsmentioning

confidence: 99%

Detecting abnormal events on binary sensors in smart home environments

Stevenson

Dobson

2016

Pervasive and Mobile Computing

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With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.

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Section: Learning-based Methodsmentioning

confidence: 99%

Detecting abnormal events on binary sensors in smart home environments

Stevenson

Dobson

2016

Pervasive and Mobile Computing

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“…Paschalidis et al [22] use Markov models to characterise the normal behaviour of sensor networks; that is, a Markov model at each sensor node is built to estimate anomalyfree probabilities from its past observation traces, and a treeindexed Markov model is developed to capture their spatial correlations across the network. Based on derived optimal anomaly detection rules, the approach can assess whether its most recent empirical measure is consistent with the anomalyfree probability model.…”

Section: A Failure Detection In Wireless Sensor Networkmentioning

confidence: 99%

Fault detection for binary sensors in smart home environments

Stevenson

Dobson

2015

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Experiments in assisted living confirm that such systems can provide context-aware services that enable occupants to remain active and independent. They also demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a technique that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets that include multiple individuals, and show consistent rates of anomaly detection across different environments.

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“…Nowadays, statistics, clustering, classification, and nearest-neighbor algorithms are commonly used methods of detecting abnormal [ 5 ]. Method under a certain probability model is based on statistical anomaly detection algorithm, nevertheless, a hypothetical probability model has to be predefined [ 6 ]. SVM-based [ 7 ] algorithms, Bayes networks [ 8 ] and neural networks [ 9 ] are the main several classification algorithms for anomaly detection used to learn a classify model (classifier) from the training data (labeled data instances) and then to predict a test instance using the classifier.…”

Section: Introductionmentioning

confidence: 99%

An incremental anomaly detection model for virtual machines

et al. 2017

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Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

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Statistical anomaly detection with sensor networks

Cited by 28 publications

References 12 publications

Detecting abnormal events on binary sensors in smart home environments

Detecting abnormal events on binary sensors in smart home environments

Fault detection for binary sensors in smart home environments

An incremental anomaly detection model for virtual machines

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