8 citations · 19 across the 13 of their papers we have counts for
7 papers · 1 filter
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!
Tirth Patel, Fred Lu, Edward Raff +3
Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0.1\% change can…
MalDICT: Benchmark Datasets on Malware Behaviors, Platforms, Exploitation, and Packers
Robert J. Joyce, Edward Raff, Charles Nicholas +1
Existing research on malware classification focuses almost exclusively on two tasks: distinguishing between malicious and benign files and classifying malware by family. However, m…
Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection
Maksim E. Eren, Manish Bhattarai, Robert J. Joyce +3
Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed…
A Feature Set of Small Size for the PDF Malware Detection
Ran Liu, Charles Nicholas
Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vector…
AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora
Robert J. Joyce, Tirth Patel, Charles Nicholas +1
When investigating a malicious file, searching for related files is a common task that malware analysts must perform. Given that production malware corpora may contain over a billi…
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?
Ran Liu, Maksim Eren, Charles Nicholas
With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML mod…
IMCDCF: An Incremental Malware Detection Approach Using Hidden Markov Models
Ran Liu, Charles Nicholas
The popularity of dynamic malware analysis has grown significantly, as it enables analysts to observe the behavior of executing samples, thereby enhancing malware detection and cla…