Abstract
The expansion of the cyberspace world has paved the way for numerous attacks and misuses of online resources and networks. For instance, malware is the prime cause of data loss and data theft, which is the source of capital loss for many corporations and institutes. Therefore, it becomes imperative to identify whether the data or a file is malicious or not. The methods for malware and benign detection in this study are different machine learning algorithms like Random Forest, XG Boost, Gradient Boost, Decision Tree, Logistic Regression, K-nearest neighbours (KNN), and Naive Bayes (NB). The main objective of this study is to find the best algorithm which gives the best accuracy in the detection of malware. A comparison was made among seven machine learning algorithms, with the accuracy (99%), precision (0.99), recall (1) and F1 score (0.99) of Random Forest being the best.
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Mohi ud din, S., Rizvi, F., Sharma, N., Sharma, D.K. (2023). Comparative Analysis of Various Machine Learning Algorithms for Detection of Malware and Benign. In: Garg, D., Narayana, V.A., Suganthan, P.N., Anguera, J., Koppula, V.K., Gupta, S.K. (eds) Advanced Computing. IACC 2022. Communications in Computer and Information Science, vol 1781. Springer, Cham. https://doi.org/10.1007/978-3-031-35641-4_17
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