Cyber Attack Detection Process in Sensor of DC Micro-Grids Under Electric Vehicle Based on Hilbert–Huang Transform and Deep Learning

Cui, Hao; Dong, Xiaohu; Deng, Hongyan; Dehghani, Moslem; Alsubhi, Khalid; Aljahdali, Hani Moaiteq

doi:10.1109/jsen.2020.3027778

Cited by 42 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of presented detection plan is investigated by using the FDIA scheme and the evaluation outcomes are presented in Table IV. Furthermore, to demonstrate the sufficiency of suggested cyber-attack detection model, it compares with HHT and deep neural network (DNN) presented in the reference [15]. Table IV is able to remark that the offered method can detect the FDIA less than 5ms from the cyber-attack inception in different scenarios with more than 96 % accuracy from 1259 samples.…”

Section: Simulation Resultsmentioning

confidence: 95%

“…So in this totality destruction can be obtained in false data causing by FDI, that its generic procedures include FDIA, replay attack, and man-in-the-middle attack. The confidentiality attack can be obtained in data leakage and illegal use that its general procedures include brute force password cracking, utilization of malware, and internal employee attack [15,16]. In the role of a typical state with the aim of destroying information integrity, the analysis outcomes of state estimation can be disrupted by the FDA, so result in misleading the control center decision.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform

et al. 2021

Self Cite

View full text Add to dashboard Cite

Due to the simultaneous development of DC-microgrids (DC-MGs) and the use of intelligent control, monitoring and operation methods, as well as their structure, these networks can be threatened by various cyber-attacks. Overall, a typical smart DC-MG includes battery, supercapacitors and power electronic devices, fuel cell, solar Photovoltaic (PV) systems, and loads such as smart homes, plug-in hybrid electrical vehicle (PHEV), smart sensors and network communication like fiber cable or wireless to send and receive data. Given these issues, cyber-attack detection and securing data exchanged in smart DC-MGs like CPS has been considered by experts as a significant subject in recent years. In this study, in order to detect false data injection attacks (FDIAs) in a MG system, Hilbert-Huang transform methodology along with blockchain-based ledger technology is used for enhancing the security in the smart DC-MGs with analyzing the voltage and current signals in smart sensors and controllers by extracting the signal details. Results of simulation on the different cases are considered with the objective of verifying the efficacy of the proposed model. The results offer that the suggested model can provide a more precise and robust detection mechanism against FDIA and improve the security of data exchanging in a smart DC-MG.

show abstract

Section: Simulation Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The FDIA type has analyzed the efficiency/performance of offered detection method, and the appraisal results are depicted in Table 4. Besides, to indicate the performance of the proposed cyber-attack detection approach, it compares the Shallow model and deep neural network (DNN) with Hilbert-Huang Transform (HHT) presented in the [48] in Table 4. As shown in Table 4, it can be argued that the proposed method could diagnose the FDIAs by detection precision over 95% that indicates the performance of the offered diagnosing approach on detecting the FDIAs.…”

Section: Discussionmentioning

confidence: 99%

Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision.

show abstract

“…Operating of these units in the AC paradigm is a practical choice to improve its efficiency. Compared to the centralized communication scheme, to avoid a single point of failure, applying distributed controllers' approach is a technical way to enhance MGs' robustness and stability [5], [6]. The distributed control method requires transmitting a lesser amount of data and less traffic in communication links than the integrated communication approach [7].…”

Section: State Of System W Imentioning

confidence: 99%

Cyber Attack Detection Based on Wavelet Singular Entropy in AC Smart Islands: False Data Injection Attack

et al. 2021

Self Cite

View full text Add to dashboard Cite

Since Smart-Islands (SIs) with advanced cyber-infrastructure are incredibly vulnerable to cyber-attacks, increasing attention needs to be applied to their cyber-security. False data injection attacks (FDIAs) by manipulating measurements may cause wrong state estimation (SE) solutions or interfere with the central control system performance. There is a possibility that conventional attack detection methods do not detect many cyber-attacks; hence, system operation can interfere. Research works are more focused on detecting cyber-attacks that target DC-SE; however, due to more widely uses of AC SIs, investigation on cyber-attack detection in AC systems is more crucial. In these regards, a new mechanism to detect injection of any false data in AC-SE based on signal processing technique is proposed in this paper. Malicious data injection in the state vectors may cause deviation of their temporal and spatial data correlations from their ordinary operation. The suggested detection method is based on analyzing temporally consecutive system states via wavelet singular entropy (WSE). In this method, to adjust singular value matrices and wavelet transforms' detailed coefficients, switching surface based on sliding mode controller are decomposed; then, by applying the stochastic process, expected entropy values are calculated. Indices are characterized based on the WSE in switching level of current and voltage for cyber-attack detection. The proposed detection method is applied to different case studies to detect cyber-attacks with various types of false data injection, such as amplitude, and vector deviation signals. The simulation results confirm the high-performance capability of the proposed FDIA detection method. This detection method's significant characteristic is its ability in fast detection (10 ms from the attack initiation); besides, this technique can achieve an accuracy rate of over 96.5%.INDEX TERMS Cyber-attack detection, wavelet transform, wavelet singular entropy, smart-island, false data injection attack, AC state.

show abstract

Cyber Attack Detection Process in Sensor of DC Micro-Grids Under Electric Vehicle Based on Hilbert–Huang Transform and Deep Learning

Cited by 42 publications

References 21 publications

Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform

Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform

Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach

Cyber Attack Detection Based on Wavelet Singular Entropy in AC Smart Islands: False Data Injection Attack

Contact Info

Product

Resources

About