digital forensics

Artificial intelligence is transforming malware detection by moving away from traditional file signature reliance to analyzing program actions. Antivirus software previously used signatures to identify known threats, but the rapid emergence of over 450,000 new malware samples daily complicates this method. Attackers now employ techniques like repacking and encryption to evade detection, prompting the need for machine-learning and behavioral techniques for better protection. Modern detection systems assess multiple characteristics of executable files, including their behavior, to identify potential threats, even if they lack recognized signatures. Microsoft's Project Ire exemplifies advanced detection capabilities, functioning as an automated malware analyst that evaluates unknown binaries based on their behavior. Additionally, modern endpoint-security products leverage cloud systems to analyze data from multiple devices, enhancing threat detection through shared intelligence. However, AI detection can still produce false positives and is susceptible to manipulation by attackers. Therefore, effective security systems require a combination of traditional measures, behavioral monitoring, machine learning, and human analysis to navigate complex threats.