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

2026-07-01 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes introduces hamm-grams, a novel feature extraction method designed to enhance malware detection and classification systems. This approach addresses the brittleness of traditional n-gram features by proposing a special class of regular expressions characterized by a fixed length and the inclusion of single-character wildcards. The authors devise an efficient algorithm to identify common hamm-grams. This algorithm leverages a new locality-sensitive hash (LSH) specifically engineered to generate collisions among byte sequences exhibiting small Hamming distances. Following the hashing, a clustering process is applied within the hash buckets to strategically place wildcards, thereby creating more robust features. The paper demonstrates the practical advantages of these hamm-gram features in improving the performance of machine learning systems for both malware classification and detection tasks, offering a significant advancement over existing static analysis techniques.

Key takeaway

For AI Security Engineers developing robust malware detection systems, you should consider integrating hamm-grams to overcome the brittleness of traditional n-gram features. This novel approach, using fixed-length regular expressions with single-character wildcards, offers more resilient static analysis. Implementing the proposed locality-sensitive hash and clustering algorithm can significantly enhance your machine learning models' ability to classify and detect evolving malware threats effectively.

Key insights

Hamm-grams, fixed-length regular expressions with wildcards, offer robust features for malware detection by overcoming n-gram brittleness.

Principles

• Regular expressions with wildcards enhance feature robustness.
• Locality-sensitive hashing can group similar byte sequences.
• Clustering within hash buckets refines wildcard placement.

Method

An efficient algorithm finds common hamm-grams using a new locality-sensitive hash for small Hamming distance collisions, followed by clustering within hash buckets to place wildcards.

In practice

• Apply hamm-grams for improved static malware analysis.
• Integrate hamm-grams into machine learning malware classifiers.

Topics

• Malware Detection
• Feature Engineering
• Regular Expressions
• Locality-Sensitive Hashing
• Machine Learning Security
• Static Analysis

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.