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Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems.

Sani Umar¹, Muhamad Felemban¹

¹Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This summary is machine-generated.

This study analyzes False Data Injection Attacks (FDIA) on power systems, showing they can significantly increase generation costs. A new attack strategy and a detection mechanism are proposed to address these cybersecurity vulnerabilities.

Keywords:

cyber-security intrusion detection system smart grid

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Area of Science:

Electrical Engineering
Computer Science
Cybersecurity

Background:

Modern power systems face increasing cybersecurity threats.
Vulnerabilities in cyber infrastructure enable adversaries to launch attacks like False Data Injection Attacks (FDIA).

Purpose of the Study:

Analyze the impact of FDIA on power generation costs and physical components.
Introduce a novel FDIA strategy to maximize generation costs.
Propose a detection and prevention mechanism for FDIA.

Main Methods:

Simulations on standard IEEE bus systems using MATPOWER.
Utilized Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), and Particle Swarm Optimization (PSO) for attack execution.
Developed a rule-based FDIA detection and prevention mechanism.

Main Results:

FDIA increased generation costs by up to 15.6% (6-bus), 45.1% (9-bus), 60.12% (30-bus), and 74.02% (118-bus).
The proposed FDIA strategy effectively maximized generation costs in simulations.
The rule-based mechanism demonstrated potential for mitigating FDIA.

Conclusions:

FDIA pose a significant threat to power system economics and stability.
Advanced optimization algorithms can be leveraged for sophisticated cyber attacks.
Effective detection and prevention strategies are crucial for securing power systems.