Source: Rapid7 Cybersecurity Blog Author: Tom Caiazza URL: https://www.rapid7.com/blog/post/2025/01/08/new-research-enhancing-botnet-detection-with-ai-using-llms-and-similarity-search/

ONE SENTENCE SUMMARY:

Rapid7’s research reveals AI’s potential in detecting botnet activity through TLS certificate analysis, significantly enhancing cybersecurity measures.

MAIN POINTS:

1. Botnets use TLS encryption, complicating detection for traditional security tools.
2. Unique TLS certificate characteristics provide avenues for advanced botnet detection.
3. Dr. Stuart Millar’s study utilized AI large language models for detection.
4. C-BERT LLM achieved a 0.994 accuracy rate in distinguishing certificates.
5. The model identifies potential botnets using vector representations of TLS certificates.
6. Testing involved 150,000 certificates, finding one confirmed malicious certificate.
7. The research can detect zero-day botnets not previously documented.
8. AI solutions can reduce false positives and lessen manual inspection efforts.
9. Future research will increase certificate attributes and improve processing capabilities.
10. The study was presented at AISec 2024 and earned a best paper award.

TAKEAWAYS:

1. TLS encryption complicates botnet detection, requiring innovative research solutions.
2. AI models demonstrate efficiency and accuracy in identifying malicious certificates.
3. Real-world applications of the research offer operational benefits for cybersecurity teams.
4. Zero-day detection capabilities highlight the robustness of the AI approach.
5. Continued research will refine the detection process and expand its applicability.