Malware Detection Using K-Nearest Neighbor Algorithm and Feature Selection

 Catur Supriyanto (Universitas Dian Nuswantoro, Semarang, Indonesia)
 (*)Fauzi Adi Rafrastara Mail (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Afinzaki Amiral (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Syafira Rosa Amalia (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Muhammad Daffa Al Fahreza (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Mohd. Faizal Abdollah (Universiti Teknikal Malaysia Melaka, Melaka, Malaysia)

(*) Corresponding Author

Submitted: November 6, 2023; Published: January 24, 2024

Abstract

Malware is one of the biggest threats in today’s digital era. Malware detection becomes crucial since it can protect devices or systems from the dangers posed by malware, such as data loss/damage, data theft, account break-ins, and the entry of intruders who can gain full access of system. Considering that malware has also evolved from traditional form (monomorphic) to modern form (polymorphic, metamorphic, and oligomorphic), a malware detection system is needed that is no longer signature-based, but rather machine learning-based. This research will discuss malware detection by classifying the file whether considered as malware or goodware, using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features was reduced using the Information Gain and Principal Component Analysis (PCA) feature selection methods. The performance of kNN with PCA and Information Gain will then be compared to get the best performance. As a result, by using the PCA method where the number of features was reduced until the remaining 32 PCs, the kNN algorithm succeeded in maintaining classification performance with an accuracy of 95.6% and an F1-Score of 95.6%. Using the same number of features as the basis, the Information Gain method is applied by sorting the features from those with the highest Information Gain score and taking the 32 best features. The result, by using this Information Gain method, the classification performance of the kNN algorithm can be increased to 96.9% for both accuracy and F1-Score.

Keywords


Classification; Features Selection; Information Gain; K-Nearest Neighbor; Malware Detection

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