mlsanityData to 14 Oct 2026

LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis

arXiv 2505.18551 ↗First posted 24 May 2025cs.CR cs.LGcited by 6

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Abstract (arXiv)

Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem, adversarial development of new malware families, and the continuous emergence of both benign and malicious applications. Prior studies have shown that such concept drift -- distributional shifts in benign and malicious samples, leads to significant degradation in detection performance over time. Despite the practical importance of this issue, existing datasets are often outdated and limited in temporal scope, diversity of malware families, and sample scale, making them insufficient for the systematic evaluation of concept drift in malware detection. To address this gap, we present LAMDA, the largest and most temporally diverse Android malware benchmark to date, designed specifically for concept drift analysis. LAMDA spans 12 years (2013-2025, excluding 2015), includes over 1 million samples (approximately 37% labeled as malware), and covers 1,380 malware families and 150,000 singleton samples, reflecting the natural distribution and evolution of real-world Android applications. We empirically demonstrate LAMDA's utility by quantifying the performance degradation of standard ML models over time and analyzing feature stability across years. As the most comprehensive Android malware dataset to date, LAMDA enables in-depth research into temporal drift, generalization, explainability, and evolving detection challenges. The dataset and code are available at: https://iqsec-lab.github.io/LAMDA/.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 55 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

20 of 55 references.

References · 20

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8 more
  1. 2022Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Spacecited by 15
  2. 2022On the Limitations of Continual Learning for Malware Classificationcited by 12
  3. 2022AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detectioncited by 11
  4. 2023Combating Representation Learning Disparity with Geometric Harmonizationcited by 11
  5. 2025MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classificationcited by 8
  6. 2023EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysiscited by 6
  7. 2017EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learningcited by 2
  8. 2025Quantifying True Robustness: Synonymity-Weighted Similarity for Trustworthy XAI Evaluationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Regression-aware Continual Learning for Android Malware Detectioncited by 2
  2. 2025Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detectioncited by 1
  3. 2025Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmarkcited by 0
  4. 2025DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detectioncited by 0
  5. 2026FreeMOCA: Memory-Free Continual Learning for Malicious Code Analysiscited by 0
  6. 2026Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detectioncited by 0