Hybrid Anomaly Detection by Using Clustering for Wireless Sensor Network

Borjali, Ahmad; Wang, Jian; Ali, Zain Anwar; Tanvir, Sania; Khan, M. Sadiq Ali

doi:10.1007/s11277-018-5721-6

Cited by 60 publications

(27 citation statements)

References 9 publications

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“…It is important to detect the outliers efficiently and accurately to improve the reliability of WSN data. The general outlier detection methods can be classified into four classes: statistical-based methods, [4][5][6] nearest neighbor-based methods, 7,9 clustering-based methods, [10][11][12] and classification-based methods. [13][14][15][16][17] Statistical-based methods capture the distribution of the data and evaluate how well the data instance matches the model.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, collected datasets have high dimensionality and large scalability for certain cases, presenting issues for data processing. In the past several years, numerous methods have been proposed to perform outlier detection for WSNs [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] (reviewed in section ''Related work''). However, the majority of these can only address the first two challenges, and most of them cannot be directly applied to high-dimensional and large-scalability data because of the following issues: 15 (1) time-consuming-as the dimension of the input data vector increases, the number of feature subspaces increases exponentially, which results in an exponential search space; (2) low detection rate-the high proportion of irrelevant features in highdimensional datasets unavoidably include noises, which makes the true outliers inconspicuous; and (3) high false alarm rate-in high-dimensional space, we can always determine at least one feature subspace for each point of a dataset that defines such a point as an outlier, that is, every data instance can be considered as an outlier under a particular circumstance.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Again referring the matrix, a new symbol will be yield. (4). Combined the symbols obtained in 2 nd and 3 rd step to get a new Composite symbol.…”

Section: Figure 7 Data Query Processingmentioning

confidence: 99%

“…Then convert the value into its decimal form. (4). Then convert the decimal value into its binary form.…”

Section: Figure 8 Encryptionmentioning

confidence: 99%

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A Unique Data Identification System for Wireless Sensor Networks Based on Enhanced Arithmetic Coding

Balaji¹,

Robinson²,

Eanoch³

2020

ISI

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In wireless sensor networks, the data transmission provides the source of the data and the details of the forwarding data for the data packet through the based station, to save the energy and maximize the bandwidth while data transmission, the data packet is compressed at every node using the arithmetic coding technique. The encoding and decoding techniques are utilized for providing the secured data transmission. In this paper, the proposed Unique Data Identification System (UDIS) is used to generate the data communication model for user based validation technique. The proposed approach is capable of proxy interpretation for enhancing the server to get the solution for user authentication issues and processing the requests from the user. The performance evaluation is conducted using the network simulator to increase the performance in terms of user load and response time.

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Analysis of abnormal data in sensor networks based on improved LSTM in the Internet of Things environment

Wang,

Zhou,

et al. 2023

Int J Communication

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SummaryThe proposed method addresses the challenge of online detection of high‐dimensional data in the IoT environment by introducing an anomaly data analysis technique based on improved LSTM. The method involves normalizing both normal and abnormal data using the correlation between multidimensional data and transforming them into gray image representations for input. Additionally, an enhanced abnormal data detection approach is presented through the construction of two parallel network models: a “two‐layer model” and a “single‐layer model.” This approach aims to improve stability in modeling normal data and enhance the detection capability for abnormal data. The proposed method was evaluated on the Human Activity Recognition (HAR) dataset, which consists of 561 dimensions. The experimental results showcased the effectiveness of this method, achieving a detection rate of 94.12% and a recall rate of 95.21%. These rates surpassed the performance of existing techniques in the field of abnormal data detection. Consequently, this method has demonstrated significant advancements and offers improved system performance when compared to current methods.

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Hybrid Anomaly Detection by Using Clustering for Wireless Sensor Network

Cited by 60 publications

References 9 publications

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

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

A Unique Data Identification System for Wireless Sensor Networks Based on Enhanced Arithmetic Coding

Analysis of abnormal data in sensor networks based on improved LSTM in the Internet of Things environment

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