Lightweight Anomaly Detection for Wireless Sensor Networks

Cheng, Pu; Zhu, Muhua

doi:10.1155/2015/653232

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…Taking into account all four scenarios, the overall performance of the proposed anomaly/attack method for five AMI network features is presented in [36,50,51]. Taking into account Holt's exponential smoothing model, we achieve FP values changing from 6.40 to 10.60%, so we can state that this interval is acceptable for anomaly detection class security systems.…”

Section: Experimental Setup and Resultsmentioning

confidence: 95%

Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

2017

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The article presents solutions to anomaly detection in network traffic for critical smart metering infrastructure, realized with the use of radio sensory network. The structure of the examined smart meter network and the key security aspects which have influence on the correct performance of an advanced metering infrastructure (possibility of passive and active cyberattacks) are described. An effective and quick anomaly detection method is proposed. At its initial stage, Cook's distance was used for detection and elimination of outlier observations. So prepared data was used to estimate standard statistical models based on exponential smoothing, that is, Brown's, Holt's, and Winters' models. To estimate possible fluctuations in forecasts of the implemented models, properly parameterized Bollinger Bands was used. Next, statistical relations between the estimated traffic model and its real variability were examined to detect abnormal behavior, which could indicate a cyberattack attempt. An update procedure of standard models in case there were significant real network traffic fluctuations was also proposed. The choice of optimal parameter values of statistical models was realized as forecast error minimization. The results confirmed efficiency of the presented method and accuracy of choice of the proper statistical model for the analyzed time series.

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Section: Experimental Setup and Resultsmentioning

confidence: 95%

Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

2017

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“…Efficient model training in input space. To reduce the computational time and space, we first introduce Theorem 1, 24 and then propose Theorem 2 to efficiently obtain the radius of the quarter sphere.…”

Section: Online Quarter-sphere One-class Svmmentioning

confidence: 99%

Fast outlier detection for high-dimensional data of wireless sensor networks

Qiao

Cui

Jin

et al. 2020

International Journal of Distributed Sensor Networks

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This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

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“…In [ 22 ], the authors use one-class quarter-sphere Support Vector Machines (QSSVM) in two new anomaly detection algorithms: LADS and LADQS. These algorithms are suitable to run in constrained nodes due to their low computational complexity.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

Garcia-Font

Garrigues

Rifà-Pous

2016

Sensors

107

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In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

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Lightweight Anomaly Detection for Wireless Sensor Networks

Cited by 13 publications

References 15 publications

Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

Fast outlier detection for high-dimensional data of wireless sensor networks

A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

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