# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 4 Blockchain-Based Architecture and System Design
### Collaborative DDoS Mitigation  
The proposed system integrates deep learning, edge computing, and smart contracts into a multi-layered Distributed Denial of Service (DDoS) defense mechanism. This collaborative approach enhances the system's ability to detect, mitigate, and respond to DDoS attacks in real time, ensuring minimal disruption to operations [44; 45; 46]. * Deep Learning for Threat Detection: The system utilizes advanced deep learning models, such as Long Short-Term Memory (LSTM) networks, to analyze real-time network traffic patterns. These models are trained to identify anomalies and flag potential DDoS attacks, enabling early detection and rapid response [45]. By continuously monitoring traffic behavior, the system distinguishes between legitimate and malicious activity, significantly reducing false positives while maintaining high detection accuracy. * Edge Computing for Localized Mitigation: Edge computing plays a crucial role in mitigating DDoS attacks by processing and filtering malicious traffic at the network's edge. By offloading computational tasks to edge nodes, the system reduces the load on central servers, ensuring an uninterrupted flow of legitimate traffic. This localized approach minimizes latency and enhances the system's resilience against large-scale attacks. Edge nodes act as the first line of defense, filtering out malicious requests before they reach critical infrastructure, thereby preventing network congestion and maintaining operational continuity. * Smart Contracts for Automated Response: Smart contracts embedded within the blockchain automate the response to detected threats. When a DDoS attack is identified, smart contracts trigger predefined actions, such as blocking malicious IP addresses or stopping suspicious traffic that causes rate-limiting. These actions are recorded on the blockchain, creating a transparent and tamper-proof audit trail that ensures accountability and traceability [44]. Using smart contracts eliminates manual intervention, enabling faster and more reliable responses to cyber threats. * Integrated Defense Mechanism: By combining deep learning, edge computing, and smart contracts, the system creates a collaborative defense mechanism that is both proactive and adaptive. This integrated approach improves the system's ability to withstand DDoS attacks, ensuring continuous operation and data integrity even under adverse conditions. The decentralized nature of this solution further enhances its robustness, making it well suited for the dynamic and interconnected environments of Industrial Internet of Things (IIoT) systems. Algorithm 2 demonstrates the collaborative role of deep learning, edge computing, smart contracts, and blockchain in achieving effective DDoS mitigation, thereby enhancing security in Industrial Internet of Things (IIoT) networks. Key contributions of collaborative DDoS mitigation:  
* Early Detection: Deep learning models identify anomalies in real-time traffic, enabling proactive threat identification. * Localized Mitigation: Edge computing filters malicious traffic at the edge, reducing latency and server load. * Automated Response: Smart contracts execute predefined actions, ensuring rapid and tamper-proof responses to threats. * Decentralized Resilience: Blockchain integration ensures transparency, accountability, and resistance to single points of failure. This multi-layered approach mitigates DDoS attacks effectively and strengthens the overall security posture of IIoT systems, ensuring uninterrupted operations in industrial environments.