Table 1 Summary of related works.

ElKashlan et al. ¹²

ML-CA classification approach

IoT traffic-based classifier for EV charging station security

Enhances cybersecurity, reduces DDoS risks

Effectiveness in large-scale IoT networks has not explored

ElKashlan et al.¹³

MLCA for DDoS detection

Identifies cyber threats in EVCS

Ensures stable EVCS operation

Dependence on dataset quality

Akbarian et al.¹⁴

Two-stage framework (T-SF) with ML (SVM, RF, MLP)

Attack detection with 98% accuracy

Models’ attacker-EV control centre interactions

Generalization to evolving threats not addressed

Mazhar et al.¹⁵

ML methods for energy forecasting

IoT-integrated smart grids for energy optimization

Enhances energy efficiency and occupant comfort

Requires real-world deployment validation

Mohamed N et al.¹⁶

AI & ML in EV security

Focuses on authentication, intrusion detection, and blockchain integration

Strong security approach using deep learning (70%)

Blockchain integration challenges are not detailed

Sahani N et al.¹⁷

ML-IDS applications

Review intrusion detection in smart grids

Identifies research gaps and future directions

Lacks practical deployment analysis

Tufail S et al.¹⁸

Cybersecurity analysis in smart grids

Examines risks in customer communication & management

Provides mitigation strategies for smart grid threats

No implementation of proposed solutions

Guato Burgos et al.¹⁹

Hybrid AI-based anomaly detection

Identifies seven key anomalies in smart grids

Highlights the importance of AI over statistical models

Lacks validation against real-world cyberattacks

Omitaomu O. A. et al.²⁰

AI in smart grids

Security, defect detection, load forecasting, and grid stability

Enhances resilience & reliability

It does not specify AI deployment hurdles

Sulaiman A et al.²¹

LSTM & RNN models for power data security

Federated learning for safe power-sharing

Real-time threat detection & fast FDIA identification

Model scalability for large networks is unclear

Dixit et al.²²

AI for Autonomous EVs (AEVs)

AI-driven smart transportation and energy sharing

Supports sustainable EV transition

High-security risks in data exchange

Li et al.²³

AI-based anomaly detection for IoV

Evaluate security vulnerabilities in AEVs

Highlights security threats and anomaly classification

Limited discussion on countermeasures

Hussain et al.²⁴

Bi-LSTM for anomaly detection

It uses voltage, current, frequency, and SoC data

Automates dataset generation with RT-LAB

Requires extensive training for high accuracy

CMAs Study²⁵

Deep learning for Charge Manipulation Attack detection

Assesses impact on DA & RT electricity markets

Real-world EV charging dataset validation

Defence mechanisms for evolving CMAs have not been explored

Narasipuram et al.²⁶

iL2C converter with VFPSM hybrid controller

Enhanced battery charging with a wide voltage range

Improves efficiency & dynamic performance

Performance under real-world conditions not tested

AEV Security Study²⁷

AI-based anomaly detection

Identifies & classifies malicious AEVs

Weighted ensemble approach for accuracy

Lacks real-world deployment testing