Using Side Channel Information and Artificial Intelligence for Malware Detection

Abstract

Cybersecurity continues to be a difficult issue for society especially as the number of networked systems grows. Techniques to protect these systems range from rules-based to artificial intelligence-based intrusion detection systems and anti-virus tools. These systems rely upon the information contained in network packets and downloaded executables to function. Side channel information leaked from hardware has been shown to reveal secret information in systems such as encryption keys. Computers provide many side channels such as temperature, access rates, operational frequencies, and voltages that can provide insight into what is running on a system. This work demonstrates that this side channel information can be used to detect malware running on a computing platform without access to the code involved.