ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems

Area of Science:

• Computer Science
• Cybersecurity
• Distributed Systems

Background:

• Internet of Things (IoT) systems manage critical operations and sensitive data, making security a paramount concern.
• Dynamic and uncertain IoT environments pose challenges like device unavailability, requiring robust engineering approaches.
• Existing methods for goal-driven, self-adaptive IoT systems lack autonomous security threat detection and mitigation capabilities.

Purpose of the Study:

• To propose a distributed architectural approach, ASSERT, for engineering goal-driven IoT systems capable of autonomous security adaptation.
• To address the gap in current IoT security by enabling systems to automatically detect and mitigate threats.
• To enhance the trustworthiness and security of IoT systems through adaptive measures.

Main Methods:

• The ASSERT approach utilizes agents, federated learning, feedback loops, and blockchain technology.
• It employs a distributed architecture for enhanced resilience and scalability.
• The system is designed for autonomous detection of security threats and self-adaptation to mitigate them.

Main Results:

• Experimental validation demonstrates ASSERT's feasibility and performance in detecting security threats.
• The approach effectively performs autonomous security adaptations to mitigate identified threats.
• ASSERT enables collaborative learning among system constituents regarding security threats.

Conclusions:

• The proposed ASSERT approach successfully enables goal-driven IoT systems to autonomously adapt to security threats.
• It enhances system security, trustworthiness, and collaborative threat intelligence.
• ASSERT provides a scalable and effective solution for securing dynamic IoT environments.