8 citations · 19 across the 13 of their papers we have counts for
17 papers
An Introduction to Compression-Based Machine Learning
John Hurwitz, Edward Raff, Charles K. Nicholas
Any lossless compression algorithm (like gzip) may be converted into a machine learning method, via either Normalized Compression Distance or the Minimum Description Length princip…
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Siddhant Gupta, Fred Lu, Andrew Barlow +5
A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious acto…
Neural Normalized Compression Distance and the Disconnect Between Compression and Classification
John Hurwitz, Charles Nicholas, Edward Raff
It is generally well understood that predictive classification and compression are intrinsically related concepts in information theory. Indeed, many deep learning methods are expl…
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…
SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection
Maksim E. Eren, Nick Solovyev, Manish Bhattarai +3
As the amount of text data continues to grow, topic modeling is serving an important role in understanding the content hidden by the overwhelming quantity of documents. One popular…
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation
Maksim E. Eren, Luke E. Richards, Manish Bhattarai +3
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendation…
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
Andre T. Nguyen, Fred Lu, Gary Lopez Munoz +3
We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how pre…
Rank-1 Similarity Matrix Decomposition For Modeling Changes in Antivirus Consensus Through Time
Robert J. Joyce, Edward Raff, Charles Nicholas
Although groups of strongly correlated antivirus engines are known to exist, at present there is limited understanding of how or why these correlations came to be. Using a corpus o…
MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels
Robert J. Joyce, Dev Amlani, Charles Nicholas +1
Malware family classification is a significant issue with public safety and research implications that has been hindered by the high cost of expert labels. The vast majority of cor…
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
John Boutsikas, Maksim E. Eren, Charles Varga +3
The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machi…
A Quantum Algorithm To Locate Unknown Hashgrams
Nicholas R. Allgood, Charles K. Nicholas
Quantum computing has evolved quickly in recent years and is showing significant benefits in a variety of fields, especially in the realm of cybersecurity. The combination of softw…