A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles

Zhang, Yiying; Shang, Jing; Chen, Xi; Liang, Kai Ming

doi:10.3390/en13061382

Cited by 15 publications

(3 citation statements)

References 20 publications

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“…The methods we used in our comparison were as follows: SVM [36], which uses support vector machines, a traditional machine learning method, to classify packets as trusted or malicious; CNN [37], which uses only convolutional neural network performs spatial feature extraction and uses the loss function and redundant error items designed by the spatial characteristics of the link load to achieve intrusion detection; LSTM [38], which only uses long short-term Memory network training to obtain the characteristics of traffic data changes in the time dimension In order to achieve intrusion detection; and CNN-BiSRU [39], which uses two deep learning models for serial feature extraction, with the first being convolutional neural network to extract the spatial features of the original data and the other being bidirectional simple recurrent unit to extract the temporal features based on it. Finally, the classification results were output through softmax to achieve the purpose of intrusion detection.…”

Section: Performance Comparisonmentioning

confidence: 99%

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

Xing

Wang

et al. 2023

Sensors

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The problems with network security that the Internet of Vehicles (IoV) faces are becoming more noticeable as it continues to evolve. Deep learning-based intrusion detection techniques can assist the IoV in preventing network threats. However, previous methods usually employ a single deep learning model to extract temporal or spatial features, or extract spatial features first and then temporal features in a serial manner. These methods usually have the problem of insufficient extraction of spatio-temporal features of the IoV, which affects the performance of intrusion detection and leads to a high false-positive rate. To solve the above problems, this paper proposes an intrusion detection method for IoV based on parallel analysis of spatio-temporal features (PA-STF). First, we built an optimal subset of features based on feature correlations of IoV traffic. Then, we used the temporal convolutional network (TCN) and long short-term memory (LSTM) to extract spatio-temporal features in the IoV traffic in a parallel manner. Finally, we fused the spatio-temporal features extracted in parallel based on the self-attention mechanism and used a multilayer perceptron to detect attacks in the Internet of Vehicles. The experimental results show that the PA-STF method reduces the false-positive rate by 1.95% and 1.57% on the NSL-KDD and UNSW-NB15 datasets, respectively, with the accuracy and F1 score also being superior.

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Section: Performance Comparisonmentioning

confidence: 99%

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

Xing

Wang

et al. 2023

Sensors

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“…a Markov model-based attack mitigation method is proposed to evaluate the security state of relay protection system using multi-source data of relay protection device [26]. As to the detection schemes against cyber attacks on the charging pile of power systems, a self-learning detection method based on long short-term memory (LSTM) is proposed to extract the unknown malicious behavior characteristics [27]. In [28], an artificial neural network with nonlinear autoregressive exogenous is used to identify the compromised signals in power system state estimation (PSSE).…”

Section: Related Workmentioning

confidence: 99%

Cyber attacks targeting electronic devices of power systems and countermeasures

Wang

Zhang

2021

IEICE Electron. Express

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Enhancing the security and reliability of power systems is of great significance to maintaining social stability. The power electronic devices of power systems can be utilized by attackers to launch indirect attacks, which could cause devastating impacts on the power grids. In this paper, load side attacks (LSAs) on relay protection devices is proposed as the first step to analyze the features of the attack. To achieve detection, a relation-based detection network is introduced to distinguish the LSAs from the normal faults.

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“…In [32], a DC microgrid network attack detection method and a distributed generator set were proposed. In [33], based on broadcasting many false information messages to the communication network, a self-learn-based attack detection method was proposed, which turned training and prediction into a closed-loop system. In [34], the economic efficiency of the whole system was considered, and the elastic distributed coordination of PEV charging against network attacks was studied, and the detection, isolation, update, and recovery steps were designed comprehensively.…”

Section: Introductionmentioning

confidence: 99%

Demand Response Management via Real-Time Pricing for Microgrid with Electric Vehicles under Cyber-Attack

Zhu

Yin²,

Xue

et al. 2023

Electronics

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The initiative of users to participate in power grid operation is a key factor in realizing the optimal allocation of power. Demand response (DR) management mechanisms based on real-time pricing (RTP) can effectively promote the enthusiasm of users, stimulate the efficiency of microgrids for power dispatch, and achieve the goasl of power peak shifting and valley filling. In this paper, we consider a microgrid composed of several energy providers (EPs) and multiple users, and each user is equipped with several electric vehicles (EVs). It should be noted that EVs may be attacked by networks in the process of data exchange when EVs connect to the MG. In this environment, we establish a multi-time slots social welfare maximization model that reflects the common interests of EPs and users. To simplify the problem, we decompose this multi-time slots model into a set of single-time slot optimization problems by the relaxation method. Furthermore, the mechanisms of identification and processing (MIP) for EVs under cyber-attack are proposed. The problem is decoupled to EPs and users by duality decomposition. Then, through integration with MIP, a distributed RTP algorithm based on the dual subgradient algorithm is designed to obtain the optimal electricity price. Finally, the simulation results verify the feasibility of the model and the effectiveness of the proposed algorithm. Through comparative analysis, the necessity of identifying EVs under cyber-attack is fully embodied.

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A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles

Cited by 15 publications

References 20 publications

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

Cyber attacks targeting electronic devices of power systems and countermeasures

Demand Response Management via Real-Time Pricing for Microgrid with Electric Vehicles under Cyber-Attack

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