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

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.