Critical Memory Data Access Monitor Based on Dynamic Strategy Learning

VMM-based approaches have been widely adopted to monitor fine-grained memory accessing behavior through intercepting safety-critical memory accessing and critical instructions executing. However, intercepting memory accessing operations lead to significant performance overhead as CPU control travels to VMM frequently. Some existing approaches have been proposed to resolve the performance problem by centralizing safety critical data to given memory regions. However, these approaches need to modify the source code or binary file of the monitored system, and cannot change monitoring strategies during runtime. As a result, the application scenarios are limited. To reduce the performance overhead of monitoring memory access in this paper, we propose an approach, named DynMon, which controls safety-critical data access monitoring dynamically according to system runtime states. It does not dependent on source code and need not to modify binary file of the monitored systems. DynMon obtains dynamic monitor strategies by learning from historical data automatically. With system runtime status and monitor strategies, DynMon decides memory access monitoring region dynamically at runtime. As a result, DynMon can alleviate system performance burden by reducing safety irrelevant region monitoring. The evaluations prove that it can alleviate 22.23% performance cost compared with no dynamic monitor strategy. Besides, the performance overhead will not increase significantly with large numbers of monitored data.