Zero-Day Exploit Detection Using Machine Learning

Code injection is an attack technique widely used by threat actors to launch arbitrary code execution on victim machines through vulnerable applications. In 2021, the Open Web Application Security Project (OWASP) ranked it as third in the top 10 web application security risks.

Given the popularity of code injection in exploits, signatures with pattern matches are commonly used to identify the anomalies in network traffic (mostly URI path, header string, etc.). However, injections can happen in numerous forms, and a simple injection can easily evade a signature-based solution by adding extraneous strings. Therefore, signature-based solutions will often fail on the variants of the proof of concept (PoC) of Common Vulnerabilities and Exposures (CVEs). In this blog, Unit 42 researchers explore how deep learning models can help provide more flexible coverage that is more robust to attempts by attackers to avoid traditional signatures.

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Source: Palo Alto Unit 42