KiloGrams: Very Large N-Grams for Malware Classification

2019-08-01 · Edward Raff, William Fleming, Richard Zak, Hyrum Anderson, Bill Finlayson, Charles Nicholas, Mark McLean

N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of $n$ are tested, with $n > 6$ being exceedingly rare. Larger values of $n$ are not tested due to computational burden or the fear of overfitting. In this work, we present a method to find the top-$k$ most frequent $n$-grams that is 60$\times$ faster for small $n$, and can tackle large $n\geq1024$. Despite the unprecedented size of $n$ considered, we show how these features still have predictive ability for malware classification tasks. More important, large $n$-grams provide benefits in producing features that are interpretable by malware analysis, and can be used to create general purpose signatures compatible with industry standard tools like Yara. Furthermore, the counts of common $n$-grams in a file may be added as features to publicly available human-engineered features that rival efficacy of professionally-developed features when used to train gradient-boosted decision tree models on the EMBER dataset.

📄 PDF Abstract BibTeX arXiv:1908.00200

Code (1)

NeuromorphicComputationResearchProgram/KiloGrams

Tasks

Classification General Classification Information RetrievalMalware AnalysisMalware Classification Retrieval

Malware families discovery via Open-Set Recognition on Android manifest permissions

2025-05-19 · Filippo Leveni, Matteo Mistura, Francesco Iubatti, Carmine Giangregorio 외

Malware are malicious programs that are grouped into families based on their penetration technique, source code, and other characteristics. Classifying malware programs into their respective families is essential for bui…

Malware Classification Malware Detection Open Set Learning

Effectiveness of Adversarial Examples and Defenses for Malware Classification

2019-09-10 · Robert Podschwadt, Hassan Takabi

Artificial neural networks have been successfully used for many different classification tasks including malware detection and distinguishing between malicious and non-malicious programs. Although artificial neural netwo…

Classification General Classification Malware Classification Malware Detection

Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes

2026-07-01 · Derek Everett, Edward Raff, James Holt arxiv

Malware poses a critical and ever-evolving threat, and robust and effective systems for detecting and classifying malware are of essential importance. $n$-grams features are among the common static features used in effec…

Malware Classification

A Novel Feature Representation for Malware Classification

2022-10-18 · John Musgrave, Temesguen Messay-Kebede, David Kapp, Anca Ralescu

In this study we have presented a novel feature representation for malicious programs that can be used for malware classification. We have shown how to construct the features in a bottom-up approach, and analyzed the ove…

Classification Descriptive Malware Classification

Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning

2021-01-26 · Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran 외

We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-grams (N=2) of bytes in the Discrete Cosine…

Binary Classification Classification General Classification image-classification+3