Hardware Performance Counter-Based Fine-Grained Malware Detection

Detection of malicious programs using hardware-based features has gained prominence recently. The tamper-resistant hardware metrics prove to be a better security feature than the high-level software metrics, which can be easily obfuscated. Hardware Performance Counters (HPC), which are inbuilt in most of the recent processors, are often the choice of researchers amongst hardware metrics. However, a lack of determinism in their counts, thereby affecting the malware detection rate, minimizes the advantages of HPCs. To overcome this problem, in our work, we propose a three-step methodology for fine-grained malware detection. In the first step, we extract the HPCs of each system call of an unknown program. Later, we make a dimensionality reduction of the fine-grained data to identify the components that have maximum variance. Finally, we use a machine learning based approach to classify the nature of the unknown program into benign or malicious. Our proposed methodology has obtained a 98.4% detection rate, with a 3.1% false positive. It has improved the detection rate significantly when compared to other recent works in hardware-based anomaly detection.