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A Malware Attack Enabled an Online Energy Strategy for Dynamic Wireless EVs within Transportation Systems

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  • Fahad Alsokhiry

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Andres Annuk

    (Institute of Forestry and Engineering, Estonian University of Life Sciences, 51006 Tartu, Estonia)

  • Toivo Kabanen

    (Institute of Forestry and Engineering, Estonian University of Life Sciences, 51006 Tartu, Estonia)

  • Mohamed A. Mohamed

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt)

Abstract

Developing transportation systems (TSs) under the structure of a wireless sensor network (WSN) along with great preponderance can be an Achilles’ heel from the standpoint of cyber-attacks, which is worthy of attention. Hence, a crucial security concern facing WSNs embedded in electrical vehicles (EVs) is malware attacks. With this in mind, this paper addressed a cyber-detection method based on the offense–defense game model to ward off malware attacks on smart EVs developed by a wireless sensor for receiving data in order to control the traffic flow within TSs. This method is inspired by the integrated Nash equilibrium result in the game and can detect the probability of launching malware into the WSN-based EV technology. For effective realization, modeling the malware attacks in conformity with EVs was discussed. This type of attack can inflict untraceable detriments on TSs by moving EVs out of their optimal paths for which the EVs’ power consumption tends toward ascending thanks to the increasing traffic flow density. In view of this, the present paper proposed an effective traffic-flow density-based dynamic model for EVs within transportation systems. Additionally, on account of the uncertain power consumption of EVs, an uncertainty-based UT function was presented to model its effects on the traffic flow. It was inferred from the results that there is a relationship between the power consumption and traffic flow for the existence of malware attacks. Additionally, the results revealed the importance of repressing malware attacks on TSs.

Suggested Citation

  • Fahad Alsokhiry & Andres Annuk & Toivo Kabanen & Mohamed A. Mohamed, 2022. "A Malware Attack Enabled an Online Energy Strategy for Dynamic Wireless EVs within Transportation Systems," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4691-:d:999787
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    References listed on IDEAS

    as
    1. Mohamed, Mohamed A., 2022. "A relaxed consensus plus innovation based effective negotiation approach for energy cooperation between smart grid and microgrid," Energy, Elsevier, vol. 252(C).
    2. Qiong Zhang & Wenzheng Zhang, 2019. "Accurate detection of selective forwarding attack in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    3. Abdulaziz Almalaq & Saleh Albadran & Mohamed A. Mohamed, 2022. "Deep Machine Learning Model-Based Cyber-Attacks Detection in Smart Power Systems," Mathematics, MDPI, vol. 10(15), pages 1-16, July.
    4. Norouzi, Mohammadali & Aghaei, Jamshid & Pirouzi, Sasan & Niknam, Taher & Fotuhi-Firuzabad, Mahmud & Shafie-khah, Miadreza, 2021. "Hybrid stochastic/robust flexible and reliable scheduling of secure networked microgrids with electric springs and electric vehicles," Applied Energy, Elsevier, vol. 300(C).
    5. Tan, Hong & Yan, Wei & Ren, Zhouyang & Wang, Qiujie & Mohamed, Mohamed A., 2022. "A robust dispatch model for integrated electricity and heat networks considering price-based integrated demand response," Energy, Elsevier, vol. 239(PA).
    6. Roustaei, M. & Niknam, T. & Salari, S. & Chabok, H. & Sheikh, M. & Kavousi-Fard, A. & Aghaei, J., 2020. "A scenario-based approach for the design of Smart Energy and Water Hub," Energy, Elsevier, vol. 195(C).
    7. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.
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    Cited by:

    1. Luiz Fernando Ribas Monteiro & Yuri R. Rodrigues & A. C. Zambroni de Souza, 2023. "Cybersecurity in Cyber–Physical Power Systems," Energies, MDPI, vol. 16(12), pages 1-34, June.
    2. Abdulaziz Almalaq & Saleh Albadran & Mohamed A. Mohamed, 2023. "An Adoptive Miner-Misuse Based Online Anomaly Detection Approach in the Power System: An Optimum Reinforcement Learning Method," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    3. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.

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