A New False Data Injection Detection Protocol based Machine Learning for P2P Energy Transaction between CEVs

Dhaou Said, Mayssa Elloumi

202223 citationsDOI

Abstract

Without security, any network system loses its efficiency, reliability, and resilience. With the huge integration of the ICT capabilities, the Electric Vehicle (EV) as a transportation form in cities is becoming more and more affordable and able to reply to citizen and environmental expectations. However, the EV vulnerability to cyber-attacks is increasing which intensifies its negative impact on societies. This paper targets the cybersecurity issues for Connected Electric Vehicles (CEVs) in parking lots where a peer-to-peer(P2P) energy transaction system is launched. A False Data Injection Attack (FDIA) on the electricity price signal is considered and a Machine Learning/SVM classification protocol is used to detect and extract the right values. Simulation results are conducted to prove the effectiveness of this proposed model.

Topics & Concepts

Reliability (semiconductor)Computer scienceDatabase transactionVulnerability (computing)Computer securityResilience (materials science)Protocol (science)ElectricityPeer-to-peerElectric vehicleWarning systemComputer networkEngineeringTelecommunicationsDatabaseElectrical engineeringPower (physics)Alternative medicineMedicineThermodynamicsPathologyQuantum mechanicsPhysicsSmart Grid Security and ResilienceBlockchain Technology Applications and SecurityVehicular Ad Hoc Networks (VANETs)