One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

Shahid, Nauman; Naqvi, Ijaz Haider; Qaisar, Saad

doi:10.1007/s10462-013-9395-x

Cited by 62 publications

(36 citation statements)

References 104 publications

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“…Shahid et al [87]: in this work, various formulations of One-Class SVM such as hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal were analyzed. A number of anomaly detection models for WSN that were designed based on these formulations were analyzed with regards to some characteristics, including the type of input data, the exploitation of spatial, temporal and feature correlations, the setup of SVM thresholds, outlier types, the determination of anomaly type, outlier degree, the effects of dynamic topology change, the dynamic data distribution, and the network heterogeneity.…”

Section: Detection Method-based Classification Of Anomaly Detection Mmentioning

confidence: 99%

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Rassam

Zainal

Maarof

2013

Sensors

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Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.

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Section: Detection Method-based Classification Of Anomaly Detection Mmentioning

confidence: 99%

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Rassam

Zainal

Maarof

2013

Sensors

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“…This is a local detection algorithm, which means that the degree depends on the isolation of the object relative to its neighborhood. (4) Support Vector Machine (SVM) based method [28,29]. In this method, the support vector regression is used to establish the regression model of the historical time series.…”

Section: Outlier Detectionmentioning

confidence: 99%

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Wang

2017

Information

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Abstract:In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction. Firstly, the time series are segmented based on the sliding window. Then, the prediction model is established based on the history data to predict the future value. If the difference between a predicted value and a measured value is larger than the preset threshold value, the sequence point will be judged to be an outlier and then corrected. In this paper, the sliding window and parameter settings of the algorithm are discussed and the algorithm is verified on actual data. This method does not need to pre classify the abnormal points and perform fast, and can handle large scale data. The experimental results show that the proposed algorithm can not only effectively detect outliers in the time series of meteorological data but also improves the correction efficiency notoriously.

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“…Given a new sample, it will then be classified as Trojan-free or Trojan-infested depending on which side of the boundary it lies in. [15] proposes different decision boundaries like hyperplane, hypersphere, and hyperellipsoid. In our Trojan detection context, we will use hyperplane because it has a shorter run time and its performance is comparable to the other decision boundaries according to our experimental results.…”

Section: B Svm Implementationmentioning

confidence: 99%

On application of one-class SVM to reverse engineering-based hardware Trojan detection

Bao

Forte

Srivastava

2014

Fifteenth International Symposium on Quality Electronic Design

114

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Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits known as hardware Trojans has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different approaches to detect them. Among these approaches, test-time detection approaches have drawn the greatest attention and most approaches assume the existence of a "golden model". Prior works suggest using reverse-engineering to identify such Trojan-free ICs for the golden model but they did not state how to do this efficiently. In this paper, we propose an innovative and robust reverseengineering approach to identify the Trojan-free ICs. We adapt a well-studied machine learning method, one-class support vector machine, to solve our problem. Simulation results using state-ofthe-art tools on several publicly available circuits show that our approach can detect hardware Trojans with high accuracy rate across different modeling and algorithm parameters.

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One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

Cited by 62 publications

References 104 publications

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

On application of one-class SVM to reverse engineering-based hardware Trojan detection

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