Analyzing Hardware Based Malware Detectors

Detection of malicious software at the hardware level is emerging as an effective solution to increasing security threats. Hardware based detectors rely on Machine Learning(ML) classifiers to detect malware-like execution pattern based on Hardware Performance Counters(HPC) information at runtime. The effectiveness of these learning methods mainly relies on the information provided by expensive-to-implement limited number of HPC. This paper is the first attempt to thoroughly analyze various robust machine learning methods to classify benign and malware applications. Given the limited availability of HPC the analysis results help guiding architectural decision on what hardware performance counters are needed most to effectively improve ML classification accuracy. For software implementation we fully implemented these classifier at OS Kernel to understand various software overheads. The software implementation of these classifiers are found to be relatively slow with the execution time in the range of milliseconds, order of magnitude higher than the latency needed to capture malware at runtime. This is calling for hardware accelerated implementation of these algorithms. For hardware implementation, we have synthesized the studied classifier models on FPGA to compare various design parameters including logic area, power, and latency. The results show that while complex ML classifier such as MultiLayerPerceptron and logistics are achieving close to 90% accuracy, after taking into consideration their implementation overheads, they perform worst in terms of PDP, accuracy/area and latency compared to simpler but slightly less accurate rule based and tree based classifiers. Our results further show OneR to be the most cost-effective classifier with more than 80% accuracy and fast execution time of less than 10ns, achieving highest accuracy per logic area, while mainly relying on only a single branch-instruction HPC information.