Blog by Rapid 7: https://www.rapid7.com/blog/post/2022/11/09/new-research-optimizing-dast-vulnerability-triage-with-deep-learning/

On November 11th 2022, Rapid7 for the first time published and presented state-of-the-art machine learning (ML) research at AISec, the leading venue for AI/ML cybersecurity innovations. Led by Dr. Stuart Millar, Senior Data Scientist, Rapid7's multi-disciplinary ML group has designed a novel deep learning model to automatically prioritize application security vulnerabilities and reduce false positive friction. Partnering with The Centre for Secure Information Technologies (CSIT) at Queen's University Belfast, this is the first deep learning system to optimize DAST vulnerability triage in application security. CSIT is the UK's Innovation and Knowledge Centre for cybersecurity, recognised by GCHQ and EPSRC as a Centre of Excellence for cybersecurity research.

Security teams struggle tremendously with prioritizing risk and managing a high level of false positive alerts, while the rise of the cloud post-Covid means web application security is more crucial than ever. Web attacks continue to be the most common type of compromise; however, high levels of false positives generated by vulnerability scanners have become an industry-wide challenge. To combat this, Rapid7's innovative ML architecture optimizes vulnerability triage by utilizing the structure of traffic exchanges between a DAST scanner and a given web application. Leveraging convolutional neural networks and natural language processing, we designed a deep learning system that encapsulates internal representations of request and response HTTP traffic before fusing them together to make a prediction of a verified vulnerability or a false positive. This system learns from historical triage carried out by our industry-leading SMEs in Rapid7's Managed Services division.

Given the skillset, time, and cognitive effort required to review high volumes of DAST results by hand, the addition of this deep learning capability to a scanner creates a hybrid system that enables application security analysts to rank scan results, deprioritise false positives, and concentrate on likely real vulnerabilities. With the system able to make hundreds of predictions per second, productivity is improved and remediation time reduced, resulting in stronger customer security postures. A rigorous evaluation of this machine learning architecture across multiple customers shows that 96% of false positives on average can automatically be detected and filtered out.

Rapid7's deep learning model uses convolutional neural networks and natural language processing to represent the structure of client-server web traffic. Neither the model nor the scanner require source code access — with this hybrid approach first finding potential vulnerabilities using a scan engine, followed by the model predicting those findings as real vulnerabilities or false positives. The resultant solution enables the augmentation of triage decisions by deprioritizing false positives. These time savings are essential to reduce exposure and harden security postures — considering the average time to detect a web breach can be several months, the sooner a vulnerability can be discovered, verified and remediated, the smaller the window of opportunity for an attacker.

Now recognized as state-of-the-art research after expert peer review, download a copy of the pre-print publication here.