Using Side Channel Information and Artificial Intelligence for Malware Detection
This work addresses the problem of malware detection for cybersecurity professionals and system administrators, offering an alternative detection method that does not rely on code analysis.
This paper explores the use of side channel information for malware detection. It demonstrates that side channel data can effectively identify malware on a computing platform without direct access to the executable code.
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 the network packets and download executables to function. Side channel information leaked from hardware has been shown to reveal secret information in systems such as encryption keys. This work demonstrates that side channel information can be used to detect malware running on a computing platform without access to the code involved.