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Android malware detection vulnerable to temporal concept drift

By PulseAugur Editorial · [4 sources] ·

Researchers have conducted a longitudinal study on the adversarial robustness of Android malware detection systems over a decade. Their findings indicate that temporal concept drift, where data distributions change over time, significantly reduces the adversarial robustness of these systems. While retraining models with cumulative historical data can mitigate some of this loss, it does not entirely eliminate the problem, highlighting the need for drift-aware assessment frameworks. AI

IMPACT Highlights the need for drift-aware robustness assessment in long-lived adversarial AI systems, impacting developers of security AI.

RANK_REASON The cluster contains an academic paper detailing a study on adversarial vulnerability and temporal concept drift in Android malware detection.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Ahmed Sabbah, Mohammad Kharma, Mohammad Alkhanafseh, Radi Jarrar, Samer Zein, David Mohaisen ·

    Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection

    arXiv:2605.24294v1 Announce Type: cross Abstract: Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propose a chronological adaptive maintenance framework that models deployment-time mai…

  2. arXiv cs.LG TIER_1 English(EN) · Suresh Kumar Amalapuram, Bikraj Shresta, Siva Ram murthy Chebiyam, Bheemarjuna Reddy Tamma, Sumohana S Channappayya ·

    SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget

    arXiv:2605.24903v1 Announce Type: cross Abstract: Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with active learning…

  3. arXiv cs.AI TIER_1 English(EN) · Ahmed Sabbah, Mohammed Kharma, Radi Jarrar, Samer Zein, David Mohaisen ·

    Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

    arXiv:2605.23623v1 Announce Type: cross Abstract: We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The…

  4. arXiv cs.AI TIER_1 English(EN) · David Mohaisen ·

    Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

    We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The dataset is organized into yearly slices and evalu…

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