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Replicant framework learns to evade and harden malware detectors

By PulseAugur Editorial · [1 sources] ·

A new framework called Replicant has been developed to learn how to evade and harden malware detectors. This deep reinforcement learning approach operates under a strict black-box threat model, meaning it doesn't require access to the detector's internal workings. Replicant demonstrated a 78.8% attack success rate across seven Android malware detectors, outperforming existing state-of-the-art methods. The framework's learned policies are reusable across different samples, detectors, and feature spaces, and it also proves effective in adversarial training to create more robust detectors. AI

IMPACT This research could lead to more robust malware detection systems by understanding and simulating sophisticated adversarial attacks.

RANK_REASON Research paper detailing a new framework for malware detection evasion. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Replicant framework learns to evade and harden malware detectors

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Research paper detailing a new framework for malware detection evasion. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia, Alexander Herzog, Myles Foley, Chris Hicks, Lorenzo Cavallaro, Fabio Pierazzi ·

    REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

    arXiv:2608.28499v1 Announce Type: new Abstract: To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-the-art attacks do not effectively model realistic a…

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