Preliminary Investigation into Uncertainty-Aware Attack Stage Classification

2025-08-01

AI Summary

This work addresses two key challenges in advanced persistent threat (APT) detection: inaccurate inference of multi-stage attack phases and poor robustness to out-of-distribution (OOD) inputs. To this end, we propose an evidence deep learning (EDL)-based stage classification method—the first to introduce EDL into APT phase identification. By explicitly modeling prediction uncertainty via Dirichlet distribution parameters, our approach simultaneously achieves fine-grained attack-stage classification, OOD detection, and confidence calibration. Evaluated on simulated APT scenarios, the method significantly improves both phase recognition accuracy and uncertainty quantification fidelity. Moreover, it effectively distinguishes previously unseen attack patterns, thereby enhancing model interpretability and deployment robustness in dynamic adversarial environments. The integration of evidential reasoning enables principled uncertainty-aware decision-making, advancing the reliability of APT analytics under real-world operational constraints.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Data transparency and provenance
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity due to their prolonged, multi-stage nature and the sophistication of their operators. Traditional detection systems typically focus on identifying malicious activity in binary terms (benign or malicious) without accounting for the progression of an attack. However, effective response strategies depend on accurate inference of the attack's current stage, as countermeasures must be tailored to whether an adversary is in the early reconnaissance phase or actively conducting exploitation or exfiltration. This work addresses the problem of attack stage inference under uncertainty, with a focus on robustness to out-of-distribution (OOD) inputs. We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages. This allows the system not only to predict the most likely stage of an attack but also to indicate when it is uncertain or the input lies outside the training distribution. Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with calibrated confidence while effectively detecting OOD inputs, which may indicate changes in the attackers' tactics. These results support the feasibility of deploying uncertainty-aware models for staged threat detection in dynamic and adversarial environments.

Problem

• Classifying APT attack stages under uncertainty
• Detecting out-of-distribution inputs in attack progression
• Improving robustness in staged threat detection

Innovation

• Uses Evidential Deep Learning for uncertainty modeling
• Classifies attack stages with Dirichlet distribution outputs
• Detects out-of-distribution inputs for robust inference