5 papers
FreeMOCA: Memory-Free Continual Learning for Malicious Code Analysis
Zahra Asadi, Haeseung Jeon, Sohyun Han +3
As over 200 million new malware samples are identified each year, antivirus systems must continuously adapt to the evolving threat landscape. However, retraining solely on new samp…
CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift
Md Ahsanul Haque, Md Mahmuduzzaman Kamol, Suresh Kumar Amalapuram +2
Android malware detection systems suffer severe performance degradation over time due to concept drift caused by evolving malicious and benign app behaviors. Although recent method…
MADAR: Efficient Continual Learning for Malware Analysis with Distribution-Aware Replay
Mohammad Saidur Rahman, Scott Coull, Qi Yu +1
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 a…
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
Md Ahsanul Haque, Ismail Hossain, Md Mahmuduzzaman Kamol +4
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 distributi…
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification
Jimin Park, AHyun Ji, Minji Park +2
Continual Learning (CL) for malware classification tackles the rapidly evolving nature of malware threats and the frequent emergence of new types. Generative Replay (GR)-based CL s…