Table 7 Attack impact analysis.

DDoS Attack

Overloading the charging network with excessive connection requests.

Causes charging station downtime, increased response delays, and service disruptions.

Traffic filtering and anomaly detection reduce attack impact by 95%.

Man-in-the-Middle

Intercepting and modifying data between EV and charging stations.

Leads to billing fraud, session hijacking, and user data leaks.

Encrypted communication and behavioural anomaly detection prevent data tampering.

Spoofing Attack

A malicious entity impersonates a legitimate EV user or charging station.

Unauthorized access, fraudulent charging, and incorrect billing data.

Authentication validation using federated learning ensures secure access control.

Data Manipulation

Altering transaction logs and energy consumption records.

False billing, incorrect charging history, and operational inefficiencies.

Blockchain-based logging and data integrity checks prevent tampering.

False Data Injection

Injecting incorrect sensor readings into smart grid infrastructure.

Grid instability, energy theft, and financial losses.

Machine learning-based anomaly detection identifies irregular energy patterns.