Machine Learning Techniques for Malware Detection- a Research Review

Jain, Prati; Rajvaidya, Ishita; Sah, Keshav Kumar; Kannan, Jayanthi

doi:10.1109/sceecs54111.2022.9740918

Cited by 2 publications

(1 citation statement)

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“…Malware stands for malicious software and is intended to harm systems and users by stealing information, destroying data, or simply irritating them. Malware is widely distributed and computer security issues are on the rise, according to reports [1]. Malicious programs or attacks, such as malware and ransomware families, continue to pose serious cybersecurity concerns, with potentially devastating effects [2].…”

Section: Introductionmentioning

confidence: 99%

An Exploratory Analysis of Feature Selection for Malware Detection with Simple Machine Learning Algorithms

Rahman,

Islam,

Nugroho

et al. 2023

JCOMSS

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Computers have become increasingly vulnerable to malicious attacks with an increase in popularity and the proliferation of open system architectures. There are numerous malware detection technologies available to protect the computer operating system from such attacks. This type of malware detector targets programs based on patterns detected in the properties of computer applications. As the amount of analytical data increases, the computer defense system is adversely affected. The performance of the detection mechanism has been hindered due to the presence of numerous irrelevant characteristics. The goal of this study is to provide a feature selection approach that will help malware detection systems be more accurate by detecting pertinent and significant traits. Furthermore, by selecting the most important features, it is possible to maintain an acceptable level of accuracy in the detection of malware while significantly lowering the computational cost. The proposed method displays the most important features (MIFs) obtained from each machine learning method, including data cleaning and feature selection. Furthermore, the method applies six machine learning classification techniques to the selected feature set. Several classifiers were evaluated based on several characteristics for malware detection, including Support Vector Machines (SVM), Logistic Regression (LR), K-nearest neighbor (K-NN), Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF). Our suggested model was tested on two malware datasets to determine its effectiveness. In terms of accuracy, precision, F1 scores, and recall, the experimental findings show that RF and DT classifiers beat other techniques.

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Section: Introductionmentioning

confidence: 99%