The integration of machine learning algorithms into the cybersecurity arsenal represents a paradigm shift in the battle against malware. The discussed algorithms—Random Forest, Support Vector Machines ...

The scope of this paper is to present a malware detection approach using machine learning. In this paper we will focus on windows executable files. Because of the abnormal growth of these malicious ...

This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen ...

The concept of employing machine learning and deep learning to malware detection isn't really new, but it's been only over the past few years that it's become more realistic to deploy, thanks to ...

This project focuses on multiple ML algorithms for identifying websites that are phished, are compared and analysed. Ada-Boost, XGBoost, Logistic Regression, Random Forest, Support Vector Machine, ...

Abstract: Now a days, more existing techniques are available to found tumours on brain from MR images. But still the researchers are concentrated on to detect the brain tumour accurately with less ...

SVM and kNN exemplify several important trade-offs in machine learning (ML). SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set ...