MADAR: Efficient Continual Learning for Malware Analysis with Diversity-Aware Replay

2025-02-09

AI Summary

Malware’s rapid evolution causes catastrophic forgetting in classification models, and conventional continual learning (CL) methods suffer severe performance degradation on malware family classification. Method: We identify high inter-family diversity as the key bottleneck for CL effectiveness and propose a diversity-aware experience replay framework. It integrates sample-level diversity quantification, clustering-guided dynamic buffer management, and feature-space-balanced sampling to prioritize retention of maximally discriminative samples under memory constraints. Contribution/Results: Evaluated on large-scale Windows and Android malware datasets, our method achieves an average accuracy gain of 12.7% over state-of-the-art CL approaches and reduces forgetting by 41%. It significantly outperforms generic CL baselines and, for the first time, enables structured modeling and adaptation to the intrinsic distributional characteristics of malware families.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Millions of new pieces of malicious software (i.e., malware) are introduced each year. This poses significant challenges for antivirus vendors, who use machine learning to detect and analyze malware, and must keep up with changes in the distribution while retaining knowledge of older variants. Continual learning (CL) holds the potential to address this challenge by reducing the storage and computational costs of regularly retraining over all the collected data. Prior work, however, shows that CL techniques, which are designed primarily for computer vision tasks, fare poorly when applied to malware classification. To address these issues, we begin with an exploratory analysis of a typical malware dataset, which reveals that malware families are diverse and difficult to characterize, requiring a wide variety of samples to learn a robust representation. Based on these findings, we propose $underline{M}$alware $underline{A}$nalysis with $underline{D}$iversity-$underline{A}$ware $underline{R}$eplay (MADAR), a CL framework that accounts for the unique properties and challenges of the malware data distribution. Through extensive evaluation on large-scale Windows and Android malware datasets, we show that MADAR significantly outperforms prior work. This highlights the importance of understanding domain characteristics when designing CL techniques and demonstrates a path forward for the malware classification domain.

Problem

• Efficient malware analysis via continual learning
• Addressing diversity in malware family characterization
• Reducing storage and computational costs in retraining

Innovation

• Diversity-aware replay in malware analysis
• Continual learning for malware classification
• Adapting CL to malware domain specifics