157 citations · 169 across the 10 of their papers we have counts for
10 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…
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…
Transformers for End-to-End InfoSec Tasks: A Feasibility Study
Ethan M. Rudd, Mohammad Saidur Rahman, Philip Tully
In this paper, we assess the viability of transformer models in end-to-end InfoSec settings, in which no intermediate feature representations or processing steps occur outside the…
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…