Computers Internet Programming Books RSS Reupload Requests 270

Mathematics can be formalized to allow a computer to execute proof steps and recognize whether a statement has been proven. Computer systems that automate mathematics are called automated theorem provers (ATP). The system has inference rules, which prescribe how the axioms and conjecture may be handled during a search for a proof. The number of possible choices at each point in the process can become very large. It would be useful to have a system that can predict which choices are better than other ones, so that a proof may be reached earlier.

This dissertation investigates the adversarial robustness of machine learning (ML)-based malware detection systems, focusing on practical limitations. While ML has advanced malware detection, it remains vulnerable to adversarial manipulations that enable malicious software to evade malware classifiers. This work rethinks both attack and defense strategies from a practical perspective, aiming to bridge the gap between theoretical approaches and real-world applicability. On the offensive side, it introduces a black-box evasion attack that generates query-efficient adversarial malware through realistic code injections, preserving malicious functionality while evading detection.