Detection of malware in downloaded files using various machine learning models

Kamboj, Akshit; Kumar, Priyanshu; Bairwa, Amit Kumar; Joshi, Sandeep

doi:10.1016/j.eij.2022.12.002

Cited by 19 publications

(2 citation statements)

References 15 publications

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“…Selama dekade terakhir, telah terjadi peningkatan 87% dalam infeksi malware dan program yang mungkin tidak diinginkan. Kontribusi signifikan datang dari file yang diunduh dari internet [1]. Perangkat lunak apa pun yang melakukan tindakan jahat, termasuk pencurian informasi, spionase, dll.…”

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“…Malware dapat membahayakan komputer korban dengan berbagai cara, termasuk memanipulasi data komputer, mengenkripsi data sensitif, atau bahkan memantau aktivitas korban tanpa persetujuan pemilik komputer. Risiko komputer yang menyimpan sebagian besar data penting kita terinfeksi oleh malware telah meningkat secara eksponensial [1]. Penyebab infeksi ini terjadi karena file yang diunduh dari dunia internet yang luas.…”

Section: Latar Belakangunclassified

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Optimasi Metode Supervised Learning Dengan Menggunakan Particle Swarm Optimization Untuk Deteksi Malware

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et al. 2023

JUTIKOMP

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The purpose of this research is for malware detection to solve problems that arise when users access the internet and download files that have been infiltrated by malware. One of the popular solutions today is to use machine learning techniques to train many malware models by considering special features that allow prediction of whether particular software is malware or harmless using machine learning algorithms. The dataset used is a malware detection dataset from Kaggle, which will then be classified using the ensemble classifier algorithm which belongs to the supervised learning category algorithm. Improve classification with feature optimization using Particle Swarm Optimization (PSO). This study resulted in an accuracy value generated by the Ensemble algorithm of 92%, AUC 0.94%. Then, the classification was optimized with PSO, resulting in an accuracy value increased by 7.32% to 100% accuracy while AUC increased by 0.059 to AUC of 1. From the results of the research produced, feature selection is recommended before building a classification model for malware detection.

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Section: Latar Belakangunclassified