Music classification as a new approach for malware detection

Each year, a huge number of malicious programs are released which causes malware detection to become a critical task in computer security. Antiviruses use various methods for detecting malware, such as signature-based and heuristic-based techniques. Polymorphic and metamorphic malwares employ obfuscation techniques to bypass traditional detection methods used by antiviruses. Recently, the number of these malware has increased dramatically. Most of the previously proposed methods to detect malware are based on high-level features such as opcodes, function calls or program’s control flow graph (CFG). Due to new obfuscation techniques, extracting high-level features is tough, fallible and time-consuming; hence approaches using program’s bytes are quicker and more accurate. In this paper, a novel byte-level method for detecting malware by audio signal processing techniques is presented. In our proposed method, program’s bytes are converted to a meaningful audio signal, then Music Information Retrieval (MIR) techniques are employed to construct a machine learning music classification model from audio signals to detect new and unseen instances. Experiments evaluate the influence of different strategies converting bytes to audio signals and the effectiveness of the method.