Toward an Applied Cyber Security Solution in IoT-Based Smart Grids: An Intrusion Detection System Approach

Area of Science:

• Cybersecurity
• Industrial Control Systems Security
• Network Protocols

Background:

• Modern grids increasingly rely on Information and Communication Technology (ICT), connecting Supervisory Control and Data Acquisition (SCADA) systems to the internet via IoT.
• This connectivity exposes critical infrastructure to cyber-attacks, risking operational continuity and safety due to inherent vulnerabilities in industrial protocols like Distributed Network Protocol (DNP3).
• Conventional ICT security measures are often insufficient for securing SCADA communications due to the unique characteristics of process systems and real-time protocols.

Purpose of the Study:

• To develop an innovative cybersecurity solution for SCADA systems.
• To enhance the detection of malicious activities targeting the DNP3 protocol.
• To improve the security of IoT-based smart grid environments.

Main Methods:

• A novel approach integrating an Intrusion Detection System (IDS) with DNP3 vulnerability assessment and attack modeling.
• Utilizing Machine Learning for feature selection from parsed DNP3 data and malware samples.
• Developing a cyber-attack algorithm incorporating classification and visualization.

Main Results:

• The proposed IDS-based cybersecurity solution demonstrated real-time detection capabilities.
• The method effectively identified attacks within an IoT-based smart grid communication setting.
• The DNP3 vulnerability assessment and attack model provided a robust framework for security analysis.

Conclusions:

• The developed IDS-based cybersecurity solution is effective for detecting DNP3-targeted attacks in SCADA systems.
• The approach enhances the security posture of IoT-enabled smart grids.
• Real-time detection of cyber threats is achievable through specialized DNP3 analysis and machine learning.