Work

Using KNN Model and Signature-Based Model as Hybrid Model for Malware Detection

Year: 2024

Type: article

Abstract: In this modern generation, identification of malicious software has become a crucial undertaking. This research presents a way of detecting malware by using signature-based detection and K-Nearest Neighbor algorithm. The study evaluates the research outperforms the existing techniques. Significant improvement in accuracy of 96.96% over baseline method. Combination of Signature+KNN demonstrates equal accuracy and speeds up the detection. This research underscores the potential of a proposed approach to enhance cybersecurity effectiveness.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14263998

Authors: Low Choon Keat, Tan Xuan Ying

Language: English

Topic: Advanced Malware Detection Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949331550

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