157 citations · 169 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
On the Limitations of Continual Learning for Malware Classification
Mohammad Saidur Rahman, Scott E. Coull, Matthew Wright
Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evoluti…
Detecting Adversarial Patches with Class Conditional Reconstruction Networks
Perry Deng, Mohammad Saidur Rahman, Matthew Wright
Defending against physical adversarial attacks is a rapidly growing topic in deep learning and computer vision. Prominent forms of physical adversarial attacks, such as overlaid ad…
Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces
Mohammad Saidur Rahman, Mohsen Imani, Nate Mathews +1
Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a V…
Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks
Mohammad Saidur Rahman, Payap Sirinam, Nate Mathews +2
A passive local eavesdropper can leverage Website Fingerprinting (WF) to deanonymize the web browsing activity of Tor users. The value of timing information to WF has often been di…