preprints.org > computer science and mathematics > computer networks and communications > doi: 10.20944/preprints202408.0263.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 4 August 2024 / Approved: 4 August 2024 / Online: 6 August 2024 (06:24:08 CEST)

The rapid proliferation of digital devices and connectivity has seen an unprecedented rise in the threats of cyber-attacks, making cybersecurity a high research priority worldwide. Among the various forms of cyberattacks, malicious software or malware, significantly disrupts the privacy and integrity of data on millions of computing devices globally. It is already a well-established notion that robust malware analysis underpins successful protection systems in the rapidly changing threat landscape. Traditional malware detection techniques, although effective against known malware types, are often found wanting against new and sophisticated varieties. The situation necessitates the exploration of advanced techniques like cryptographic hashing and fuzzy hashing that have proven instrumental in handling malware's dynamic nature. Fuzzy hashing, by allowing a score-based match rather than an exact digital signature, enables the identification of unknown malware that are derivatives of known malwares. However, the efficiency of the fuzzy hashing algorithms comes to the forefront concerning the encryption used. The research paper titled, "Using ASCON-based Fuzzy Hashing for Efficient Malware Analysis," seeks to explore leveraging the ASCON encryption, a lightweight but secure encryption standard, in the application of fuzzy hashing for malware detection.

Cryptography; Malware Analysis; ASCON

Computer Science and Mathematics, Computer Networks and Communications

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