Data Availability Statement:
Data are contained within the article.
Conflicts of Interest:
The authors declare no conflict of interest.
References
1.
Rimal, B.P.; Kong, C.; Poudel, B.; Wang, Y.; Shahi, P. Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging
Trends, and Open Issues. Energies 2022, 15, 1908.
https://doi.org/10.3390/en15051908
.
2.
Aldaej, A.; Ahanger, T.A.; Ullah, I. Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments. Sensors 2023,
23, 9869.
https://doi.org/10.3390/s23249869
.
3.
Kilichev, D.; Kim, W. Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO.
Mathematics 2023, 11, 3724.
https://doi.org/10.3390/math11173724
.
4.
Rashid, M.M.; Khan, S.U.; Eusufzai, F.; Redwan, M.A.; Sabuj, S.R.; Elsharief, M. A Federated Learning-Based Approach for
Improving Intrusion Detection in Industrial Internet of Things Networks. Network 2023, 3, 158–179.
https://doi.org/10.3390/
network3010008
.
5.
Peyman, M.; Copado, P.J.; Tordecilla, R.D.; Martins, L.D.C.; Xhafa, F.; Juan, A.A. Edge Computing and IoT Analytics for Agile
Optimization in Intelligent Transportation Systems. Energies 2021, 14, 6309.
https://doi.org/10.3390/en14196309
.
6.
Lobato, E.; Prazeres, L.; Medeiros, I.; Araújo, F.; Rosário, D.; Cerqueira, E.; Tostes, M.; Bezerra, U.; Fonseca, W.; Antloga,
A. A Monitoring System for Electric Vehicle Charging Stations: A Prototype in the Amazon. Energies 2023, 16, 152.
https:
//doi.org/10.3390/en16010152
.
7.
Lee, H.C.; Liu, H.Y.; Lin, T.C.; Lee, C.Y. A Customized Energy Management System for Distributed PV, Energy Storage Units, and
Charging Stations on Kinmen Island of Taiwan. Sensors 2023, 23, 5286.
https://doi.org/10.3390/s23115286
.
8.
Al Sawafi, Y.; Touzene, A.; Hedjam, R. Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks. J. Sens.
Actuator Netw. 2023, 12, 21.
https://doi.org/10.3390/jsan12020021
.
9.
Gou, W.; Zhang, H.; Zhang, R. Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the
Internet of Vehicles. Sensors 2023, 23, 8788.
https://doi.org/10.3390/s23218788
.
10.
Awajan, A. A Novel Deep Learning-Based Intrusion Detection System for IoT Networks. Computers 2023, 12, 34.
https:
//doi.org/10.3390/computers12020034
.
Mathematics 2024, 12, 571
25 of 26
11.
Fatani, A.; Dahou, A.; Abd Elaziz, M.; Al-qaness, M.A.A.; Lu, S.; Alfadhli, S.A.; Alresheedi, S.S. Enhancing Intrusion Detection
Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks. Sensors
2023
, 23, 4430.
https://doi.org/10.3390/s23094430
.
12.
Zeinali, M.; Erdogan, N.; Bayram, I.S.; Thompson, J.S. Impact of Communication System Characteristics on Electric Vehicle
Grid Integration: A Large-Scale Practical Assessment of the UK’s Cellular Network for the Internet of Energy. Electricity 2023,
4, 309–319.
https://doi.org/10.3390/electricity4040018
.
13.
Strielkowski, W.; Streimikiene, D.; Fomina, A.; Semenova, E. Internet of Energy (IoE) and High-Renewables Electricity System
Market Design. Energies 2019, 12, 4790.
https://doi.org/10.3390/en12244790
.
14.
Florea, B.C.; Taralunga, D.D. Blockchain IoT for Smart Electric Vehicles Battery Management. Sustainability 2020, 12, 3984.
https://doi.org/10.3390/su12103984
.
15.
Tappeta, V.S.R.; Appasani, B.; Patnaik, S.; Ustun, T.S. A Review on Emerging Communication and Computational Technologies
for Increased Use of Plug-In Electric Vehicles. Energies 2022, 15, 6580.
https://doi.org/10.3390/en15186580
.
16.
Arif, M.; Kim, W.; Qureshi, S. Interference Characterization in Cellular-Assisted Vehicular Communications With Jamming. IEEE
Access 2022, 10, 42469–42480.
https://doi.org/10.1109/ACCESS.2022.3168709
.
17.
Hamdare, S.; Kaiwartya, O.; Aljaidi, M.; Jugran, M.; Cao, Y.; Kumar, S.; Mahmud, M.; Brown, D.; Lloret, J. Cybersecurity Risk
Analysis of Electric Vehicles Charging Stations. Sensors 2023, 23, 6716.
https://doi.org/10.3390/s23156716
.
18.
Sarieddine, K.; Sayed, M.A.; Assi, C.; Atallah, R.; Torabi, S.; Khoury, J.; Pour, M.S.; Bou-Harb, E. EV Charging Infrastructure
Discovery to Contextualize its Deployment Security. IEEE Trans. Netw. Serv. Manag. 2023, 21, 1287–1301.
https://doi.org/10.110
9/TNSM.2023.3318406
.
19.
Girdhar, M.; Hong, J.; You, Y.; Song, T.J.; Govindarasu, M. Cyber-Attack Event Analysis for EV Charging Stations. In Proceedings
of the 2023 IEEE Power & Energy Society General Meeting (PESGM), Orlando, FL, USA, 16–20 July 2023; pp. 1–5.
https:
//doi.org/10.1109/PESGM52003.2023.10252504
.
20.
ElKashlan, M.; Aslan, H.; Said Elsayed, M.; Jurcut, A.D.; Azer, M.A. Intrusion Detection for Electric Vehicle Charging Systems
(EVCS). Algorithms 2023, 16, 75.
https://doi.org/10.3390/a16020075
.
21.
ElKashlan, M.; Elsayed, M.S.; Jurcut, A.D.; Azer, M. A Machine Learning-Based Intrusion Detection System for IoT Electric
Vehicle Charging Stations (EVCSs). Electronics 2023, 12, 1044.
https://doi.org/10.3390/electronics12041044
.
22.
Basnet, M.; Hasan Ali, M. Deep Learning-based Intrusion Detection System for Electric Vehicle Charging Station. In Proceedings
of the 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), Bangkok, Thailand, 15–18 September
2020; pp. 408–413.
https://doi.org/10.1109/SPIES48661.2020.9243152
.
23.
Basnet, M.; Hasan Ali, M. Exploring cybersecurity issues in 5G enabled electric vehicle charging station with deep learning. IET
Gener. Transm. Distrib. 2021, 15, 3435–3449.
https://doi.org/10.1049/gtd2.12275
.
24.
Basnet, M.; M. Hasan Ali. WCGAN-Based Cyber-Attacks Detection System in the EV Charging Infrastructure. In Proceedings of
the 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES), Beijing, China, 9–12 December 2022;
pp. 1761–1766.
https://doi.org/10.1109/SPIES55999.2022.10082342
.
25.
Basnet, M.; Hasan Ali, M. Deep-Learning-Powered Cyber-Attacks Mitigation Strategy in the EV Charging Infrastructure.
In Proceedings of the 2023 IEEE Power & Energy Society General Meeting (PESGM), Orlando, FL, USA, 16–20 July 2023; pp. 1–5.
https://doi.org/10.1109/PESGM52003.2023.10252470
.
26.
Islam, S.; Badsha, S.; Sengupta, S.; Khalil, I.; Atiquzzaman, M. An Intelligent Privacy Preservation Scheme for EV Charging
Infrastructure. IEEE Trans. Ind. Inform. 2023, 19, 1238–1247.
https://doi.org/10.1109/TII.2022.3203707
.
27.
Lilhore, U.K.; Manoharan, P.; Simaiya, S.; Alroobaea, R.; Alsafyani, M.; Baqasah, A.M.; Dalal, S.; Sharma, A.; Raahemifar, K.
HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning.
Sensors 2023, 23, 7856.
https://doi.org/10.3390/s23187856
.
28.
Sayegh, H.R.; Dong, W.; Al-madani, A.M. Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE
for Imbalanced Data. Appl. Sci. 2024, 14, 479.
https://doi.org/10.3390/app14020479
.
29.
Ahmad, I.; Imran, M.; Qayyum, A.; Ramzan, M.S.; Alassafi, M.O. An Optimized Hybrid Deep Intrusion Detection Model
(HD-IDM) for Enhancing Network Security. Mathematics 2023, 11, 4501.
https://doi.org/10.3390/math11214501
.
30.
Meliboev, A.; Alikhanov, J.; Kim, W. Performance Evaluation of Deep Learning Based Network Intrusion Detection System across
Multiple Balanced and Imbalanced Datasets. Electronics 2022, 11, 515.
https://doi.org/10.3390/electronics11040515
.
31.
Adefemi Alimi, K.O.; Ouahada, K.; Abu-Mahfouz, A.M.; Rimer, S.; Alimi, O.A. Refined LSTM Based Intrusion Detection for
Denial-of-Service Attack in Internet of Things. J. Sens. Actuator Netw. 2022, 11, 32.
https://doi.org/10.3390/jsan11030032
.
32.
Kethineni, K.; Gera, P. Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture. Systems 2023, 11, 304.
https://doi.org/10.3390/systems11060304
.
33.
Ferrag, M.A.; Friha, O.; Hamouda, D.; Maglaras, L.; Janicke, H. Edge-IIoTset: A New Comprehensive Realistic Cyber Security
Dataset of IoT and IIoT Applications for Centralized and Federated Learning. IEEE Access 2022, 10, 40281–40306.
https:
//doi.org/10.1109/ACCESS.2022.3165809
.
34.
Tareq, I.; Elbagoury, B.M.; El-Regaily, S.; El-Horbaty, E.S.M. Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL
in Cybersecurity for IoT. Appl. Sci. 2022, 12, 9572.
https://doi.org/10.3390/app12199572
.
Mathematics 2024, 12, 571
26 of 26
35.
Khacha, A.; Saadouni, R.; Harbi, Y.; Aliouat, Z. Hybrid Deep Learning-based Intrusion Detection System for Industrial Internet
of Things. In Proceedings of the 2022 5th International Symposium on Informatics and its Applications (ISIA), M’sila, Algeria,
29–30 November 2022; pp. 1–6.
https://doi.org/10.1109/ISIA55826.2022.9993487
.
36.
Tomar, K.; Bisht, K.; Joshi, K.; Katarya, R. Cyber Attack Detection in IoT using Deep Learning Techniques. In Proceedings of the
2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 3–4 March 2023;
pp. 1–6.
https://doi.org/10.1109/ISCON57294.2023.10111990
.
37.
Ding, W.; Abdel-Basset, M.; Mohamed, R. DeepAK-IoT: An effective deep learning model for cyberattack detection in IoT
networks. Inf. Sci. 2023, 634, 157–171.
https://doi.org/10.1016/j.ins.2023.03.052
.
38.
Koppula, M.; I, L.J.L.M. LNKDSEA: Machine Learning Based IoT/IIoT Attack Detection Method. In Proceedings of the
2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems
(ICAECIS), Bengaluru, India, 19–21 April 2023; pp. 655–662.
https://doi.org/10.1109/ICAECIS58353.2023.10170095
.
39.
Salih, K.M.M.; Ibrahim, N.B. Enhancing IoT Forensics through Deep Learning: Investigating Cyber-Attacks and Analyzing
Big Data for Improved Security Measures. In Proceedings of the 2023 4th International Conference on Big Data Analytics and
Practices (IBDAP), Bangkok, Thailand, 25–27 August 2023; pp. 1–8.
https://doi.org/10.1109/IBDAP58581.2023.10271950
.
40.
Ullah, S.; Boulila, W.; Koubâa, A.; Ahmad, J. MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion
Detection in IIoT Networks. IEEE Access 2023, 11, 114590–114601.
https://doi.org/10.1109/ACCESS.2023.3324657
.
|
