Adaptive AI Techniques for Mitigating Rowhammer Attacks in Cloud Computing Environments

Rowhammer attacks pose a significant security threat to cloud computing environments, exploiting hardware vulnerabilities in DRAM to manipulate memory contents and compromise system integrity. Traditional mitigation techniques, such as increased refresh rates and error correction codes, often fail to provide adaptive and efficient defenses against evolving Rowhammer variants. This research proposes an AI-driven approach that leverages machine learning for attack detection and reinforcement learning for dynamic mitigation, enhancing the security and reliability of cloud infrastructures. The study evaluates multiple AI models on a cloud-based testbed, demonstrating superior detection accuracy of 97.8%, reduced false positive rates, and improved attack response times compared to conventional methods. The results indicate a significant decrease in system overhead while maintaining a high mitigation success rate of 95.6%. Additionally, the proposed framework showcases adaptability to emerging Rowhammer techniques, ensuring long-term resilience against sophisticated memory-based exploits. The research also explores potential challenges, including computational resource constraints and adversarial AI risks, and proposes solutions such as federated learning for distributed detection and explainable AI for improved transparency. By integrating AI-driven Rowhammer defenses with existing cloud security mechanisms, this study provides a proactive and scalable solution to protect cloud infrastructures against hardware-based attacks, reinforcing the confidentiality, integrity, and availability of cloud services.