An Optimized KNN Model for Signature-Based Malware Detection
Main Article Content
Abstract
Malware is a computer program developed with the intent of disrupting, stealing, and compromising a computer system. In recent advances in technology and internet use, malware has become the major problem in computer society. In this research, an optimal K-nearest Neighbor (KNN) based malware detection and classification model is proposed. The proposed malware detection model is based on application programming interface (API) call sequence analysis and classification. The dataset is collected from an online Kaggle data repository which consists of 42,797 malicious application programming interface (API) call sequences and 1,079 non-malicious application programming interface (API) call sequences. The Nearest Neighbor (KNN) algorithm is applied to the dataset to create a model that detects malware. Finally, the accuracy of the proposed KNN based malware detection model is evaluated, and the result shows that the accuracy of 98.17% is achieved in the detection of malware using the model. The proposed model is significantly essential for detecting real-time intrusion on computer systems.
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