mlsanityData to 14 Oct 2026

BinCtx: Multi-Modal Representation Learning for Robust Android App Behavior Detection

arXiv 2510.14344 ↗First posted 16 Oct 2025cs.CR cs.AIcited by 0

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Abstract (arXiv)

Mobile app markets host millions of apps, yet undesired behaviors (e.g., disruptive ads, illegal redirection, payment deception) remain hard to catch because they often do not rely on permission-protected APIs and can be easily camouflaged via UI or metadata edits. We present BINCTX, a learning approach that builds multi-modal representations of an app from (i) a global bytecode-as-image view that captures code-level semantics and family-style patterns, (ii) a contextual view (manifested actions, components, declared permissions, URL/IP constants) indicating how behaviors are triggered, and (iii) a third-party-library usage view summarizing invocation frequencies along inter-component call paths. The three views are embedded and fused to train a contextual-aware classifier. On real-world malware and benign apps, BINCTX attains a macro F1 of 94.73%, outperforming strong baselines by at least 14.92%. It remains robust under commercial obfuscation (F1 84% post-obfuscation) and is more resistant to adversarial samples than state-of-the-art bytecode-only systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 70 references that are in the map. First posted on arXiv 16 Oct 2025.

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16 of 70 references.

References · 16

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  12. 2021Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Appscited by 19
4 more
  1. 2018Beyond Google Play: A Large-Scale Comparative Study of Chinese Android App Marketscited by 5
  2. 2017Android Malware Detection using Deep Learning on API Method Sequencescited by 4
  3. 2021JuCify: A Step Towards Android Code Unification for Enhanced Static Analysiscited by 2
  4. 2021CHAMP: Characterizing Undesired App Behaviors from User Comments based on Market Policiescited by 1

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