Blockchain Malware Detection Tool Based on Signature Technique


Abdul Rahman, Siti Husna and Nevin Gabriel, Chastan and Haw, Su Cheng and Zainuddin, Ahmad Anwar (2023) Blockchain Malware Detection Tool Based on Signature Technique. Advances in Artificial Intelligence and Machine Learning, 03 (04). pp. 1654-1670. ISSN 2582-9793

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Downloading software or files from the internet can be risky as it is hard to know if they are safe and do not contain viruses. Traditional anti-virus software uses a centralized database to identify malware, but this method has drawbacks due to its centralized design, which creates a single point of failure. Blockchain technology has become a solution to many problems faced in the tech industry, including the need for a decentralized and secure way for users to verify and confirm the presence of malware in a file. The decentralized database, permanence, immutability, anonymity, and auditability of blockchain technology make it an ideal solution for malware detection. In fact, malware data has been compiled in databases that antivirus manufacturers use to identify malware. However, blockchain technology provides a more secure and decentralized way to store this data, which can be shared between users and allow them to rapidly update whether a file is safe or not. This paper presents a blockchain-based malware detection tool designed to enhance security and prevent the spread of malware in digital networks. The tool based on Java programming language incorporates signaturebased methods to effectively identify and detect malicious codes in malware. The proposed tool contributes to the field of cybersecurity by leveraging blockchain technology to enhance malware detection process

Item Type: Article
Uncontrolled Keywords: Blockchain
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 22 Feb 2024 06:18
Last Modified: 22 Feb 2024 06:18


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