arXiv:2607.01445v1 Announce Type: cross Abstract: 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 effective machine learning systems for malware, but these features are inherently brittle. We propose an algorithm for constructing more robust features, hamm-grams, which are a special class of regular expressions having a fixed length and single-character wildcards. We devise an efficient algorithm for finding common hamm-grams usi
The continuous evolution of malware and cyber threats necessitates constant innovation in defense mechanisms, making advancements in detection algorithms critical.
A strategic reader should care because improved malware detection algorithms directly enhance cybersecurity posture, protecting critical infrastructure and intellectual property, which is vital for national security and economic stability.
The development of 'hamm-grams' offers a more robust and efficient method for identifying malware patterns, potentially improving the reliability of automated threat detection systems.
- · Cybersecurity companies
- · Organizations with digital assets
- · National security agencies
- · Malware developers
- · Cyber adversaries
More effective and resilient malware detection systems are deployed across various platforms.
Reduced incidence of successful malware attacks due to enhanced detection capabilities, leading to fewer data breaches and system compromises.
Malware developers may be forced to develop more sophisticated and evasive techniques, escalating the cybersecurity arms race.
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