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 ...

The Naive Bayes Classifier, the Decision Tree Classifier, and the K Nearest neighbours method are all examples of classification algorithms discussed in this section. For the purpose of comparing the ...

Matt Wolff, chief data scientist at Cylance, says his team is applying deep learning--a more granular subset of machine learning--to malware detection by training the software via legitimate files ...

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, ...

The problem of network security has arisen as a key source of worry in today’s linked society. Sabotage and information extortion are among the most significant risks toan organization’s security.