Securing IIoT systems against DDoS attacks with adaptive moving target defense strategies

Area of Science:

• Cybersecurity
• Network Security
• Industrial Internet of Things (IIoT)

Background:

• Distributed Denial of Service (DDoS) attacks pose a significant threat to Industrial Internet of Things (IIoT) security.
• Traditional static defense mechanisms are insufficient against evolving DDoS threats in IIoT environments.

Purpose of the Study:

• To introduce and evaluate a Moving Target Defense (MTD) strategy for enhancing IIoT security against DDoS attacks.
• To propose an adaptive MTD traffic manager (MTDTM) architecture for early detection and mitigation of DDoS attacks in edge clouds.

Main Methods:

• Development of an MTD traffic manager (MTDTM) architecture incorporating a traffic classifier.
• Implementation of dynamic admission rules and relocation of service replicas for adaptive traffic distribution.
• Simulation-based validation of the proposed MTDTM approach.

Main Results:

• The MTDTM architecture demonstrated efficient early detection and mitigation of DDoS attacks in resource-constrained edge clouds.
• Adaptive MTD approach significantly improved service response times by 15% to 20% compared to existing algorithms.
• Substantial enhancements in average resource availability were achieved during DDoS attack scenarios.

Conclusions:

• The proposed adaptive Moving Target Defense strategy effectively fortifies IIoT security against dynamic DDoS threats.
• MTDTM architecture ensures service availability and maintains quality of service for legitimate users in IIoT environments.
• This research offers a robust solution for safeguarding critical industrial processes against sophisticated cyberattacks.