Conformal Prediction for Offensive Security
Researchers apply conformal prediction to offensive security, presenting initial findings on privacy-attacking machine learning and network traffic analysis.
The paper observes that conformal prediction (CP), introduced over 25 years ago, has been used mainly defensively in cybersecurity and rarely for offensive purposes. The authors present initial findings applying CP in two offensive areas: attacks on privacy-preserving machine learning and network traffic analysis. The work aims to close a gap in the offensive security literature rather than report an incident.
- Conformal prediction is rarely documented for offensive security despite 25 years of use
- Initial findings cover CP-based attacks in privacy-preserving ML and network traffic analysis
Full article81 words · extracted from arxiv.org · click to collapse
Despite its introduction more than a quarter century ago, Conformal Prediction (CP) has seen surprisingly few applications to the cyber security world thus far. In particular, we observe that, while CP has been employed as a defensive measure in many recent works, its use for carrying out attacks (i.e., for offensive security) is hard to trace in the literature. We explore this gap, by presenting initial findings in two key areas of offensive security: Privacy-Preserving Machine Learning, and network traffic analysis.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.05165