LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis

TL;DR

LAMDA is a comprehensive, long-term Android malware dataset designed to analyze concept drift, enabling researchers to evaluate how malware detection models' performance degrades over time due to evolving malware and benign applications.

Contribution

This paper introduces LAMDA, the largest and most temporally diverse Android malware benchmark specifically created for concept drift analysis in malware detection.

Findings

Standard ML models' performance degrades over time on LAMDA

Feature stability varies across different malware families

LAMDA enables detailed study of malware evolution and detection challenges

Abstract

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 Despite the practical importance of this issue, existing datasets are often outdated and limited in temporal scope, diversity of malware families, and sample scale, making them insufficient for the systematic evaluation of concept drift in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/.