Malware Detection Using a Machine-Learning Based Approach | Zendy

Safa Rkhouya | Zendy; Khalid Chougdali | Zendy

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Malware Detection Using a Machine-Learning Based Approach

Author(s) -

Safa Rkhouya,

Khalid Chougdali

Publication year - 2021

Publication title -

international journal of information technology and applied sciences (ijitas)

Language(s) - English

Resource type - Journals

ISSN - 2709-2208

DOI - 10.52502/ijitas.v3i4.172

Subject(s) - false positive paradox , malware , computer science , gradient boosting , decision tree , machine learning , artificial intelligence , random forest , support vector machine , boosting (machine learning) , false positive rate , true positive rate , false positives and false negatives , data mining , computer security

The purpose of this research work is to study the usage of machine learning in detecting malware. This paper presents a versatile framework, in which a dataset of more than 130000 files has been analyzed, to train and test four machine learning algorithms: Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting; The performance of each algorithm in malware classification, has been studied based on the: Accuracy, execution time, rate of false positives and false negatives, and area under the Receiver Operating Characteristic curve.

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