A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Chen, Yunhong; Li, Shu-Ming

doi:10.1007/s11277-019-06143-1

Cited by 23 publications

(18 citation statements)

References 36 publications

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“…However, we are forced to use this measure because other researchers widely use the CPU time to prove the lightweightness of systems, for example [43], [58]- [61]. Moreover, the authors of [60] have also utilized MATLAB for performing the experiments to prove efficiency of the model. We also do not investigate a particular solution and we consider more general situation.…”

Section: ) Experimentsmentioning

confidence: 99%

“…We also do not investigate a particular solution and we consider more general situation. Thus, similar to the method followed in [60], using MATLAB and performing simulation-based experiments is the only way that we can follow to perform these experiments and prove the efficiency of proposed algorithm.…”

Section: ) Experimentsmentioning

confidence: 99%

“…However, a large difference in time (seconds versus hours or even days) cannot be characterized by the implementation method exclusively. Moreover, Chen and Li [60] presented a comparison of energy consumption and CPU time of their model. The given results generate our claim.…”

Section: ) Experimentsmentioning

confidence: 99%

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Toward a Lightweight Intrusion Detection System for the Internet of Things

et al. 2019

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Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called the Internet of Things (IoT). However, the ubiquitous and wide-range IoT networks make them prone to cyberattacks. One of the main types of attack is a denial of service (DoS), where the attacker floods the network with a large volume of data to prevent nodes from using the services. An intrusion detection mechanism is considered a chief source of protection for information and communications technology. However, conventional intrusion detection methods need to be modified and improved for application to the IoT owing to certain limitations, such as resource-constrained devices, the limited memory and battery capacity of nodes, and specific protocol stacks. In this paper, we develop a lightweight attack detection strategy utilizing a supervised machine learning-based support vector machine (SVM) to detect an adversary attempting to inject unnecessary data into the IoT network. The simulation results show that the proposed SVM-based classifier, aided by a combination of two or three incomplex features, can perform satisfactorily in terms of classification accuracy and detection time. INDEX TERMS Intrusion detection system, anomaly detection, Internet of Things, support vector machine. VLADIMIR SHAKHOV (M'09) received the B.S. degree in mechanics and applied mathematics, the M.S. degree in mathematics, and the Ph.D.

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Section: ) Experimentsmentioning

confidence: 99%

Section: ) Experimentsmentioning

confidence: 99%

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Toward a Lightweight Intrusion Detection System for the Internet of Things

et al. 2019

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“…More specifically, anomaly detection methods for packet sequences based on the Markov chain [17], cluster [18], decision tree [19], Bayesian Markov chain [20] and based on the Support Vector Machine (SVM) [21] can be mentioned in this regards. Moreover, researches developed different detection schemes based on spatio-temporal flow features, including the anomaly detection based on K-Nearest Neighbor (KNN) [22] and the anomaly detection based on the SVM [23], to investigate the sequential characteristics of the traffic.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Multi-Types of Flow Features Based on Hybrid Neural Network for Improving Network Anomaly Detection

Cao

2019

IEEE Access

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Security issues of large-scale local area network are becoming more prominent and the anomaly detection for the network traffic is the key means to solve this problem. On the other hand, it is a challenge to extract effective and accurate traffic features for anomaly detection. In order to resolve this challenge, multi-types of network flow features are designed and analyzed in the present study. These features include sequence packet features, general statistical features and environmental features, which can profile the characteristics of network flows accurately. Moreover, a method based on the hybrid neural network is proposed to detect anomaly by analyzing these features. One-dimensional convolutional network is implemented to analyze the sequence features in the hybrid neural network, while deep neural networks are utilized to learn the characteristics of high-dimension feature vectors including general statistical features and environmental features. The method can make comprehensive analysis for network anomaly detection. Two datasets of ISCX-IDS-2012 and CIC-IDS-2017 are carried out to evaluate the performance of the proposed method and other similar algorithms. The present study shows that the comprehensive performances of the proposed method are better than that for others algorithms. It is concluded that the proposed method can be applied for the anomaly detection applications with reasonable performance. INDEX TERMS Anomaly detection, hybrid neural network, network flow feature.

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“…Sindagi et al [9] developed the adaptive SVDD for the surface defect detection of organic light emitting diode. Chen et al [10] proposed the SVDD approach based on spatiotemporal and attribute correlations to detect the anomaly nodes in the wireless sensor networks. In all these anomaly detection tasks, SVDD supposes that most of the training samples are normal and creates a minimized hypersphere to surround these normal samples.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Deng¹,

Zhang²

2020

Sensors

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As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

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A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Cited by 23 publications

References 36 publications

Toward a Lightweight Intrusion Detection System for the Internet of Things

Toward a Lightweight Intrusion Detection System for the Internet of Things

Analysis of Multi-Types of Flow Features Based on Hybrid Neural Network for Improving Network Anomaly Detection

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

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