While the in-DRAM swapping approach of DNN-Defender offers significant advantages in protecting against RowHammer attacks, it does have potential limitations and drawbacks:
Latency Overhead: The swapping operations, although optimized, may introduce latency that could affect the overall performance of the system, especially in real-time applications. Future work could focus on optimizing the timing of swap operations further, perhaps by implementing more efficient algorithms or hardware accelerators that can perform these operations with minimal delay.
Limited Scalability: As the size of the model increases, the number of rows that need protection may also increase, potentially leading to scalability issues. To address this, future iterations of DNN-Defender could incorporate adaptive mechanisms that dynamically allocate resources based on the current workload and threat level, ensuring that the most critical rows are prioritized without overwhelming the system.
Resource Contention: The in-DRAM swapping mechanism may lead to contention for memory resources, particularly in multi-core or multi-threaded environments. Future work could explore the integration of a more sophisticated memory management system that can handle concurrent access and swapping operations more efficiently.
Potential for New Attack Vectors: While DNN-Defender protects against specific RowHammer attacks, it may inadvertently create new vulnerabilities if attackers adapt their strategies to exploit the swapping mechanism itself. Ongoing research should focus on identifying and mitigating these potential new attack vectors, possibly by incorporating machine learning techniques to predict and counteract adaptive attacks.