Macos malware used runonly to detection software#

The results from this research show that the Decision Tree algorithm has a 92.78% accuracy with a 3.62% false alarm rate whiles the Stochastic Gradient Descent has an accuracy rate of 91.77% with a 7.09% false-positive rate. They are the Decision Tree (DT), Support Vector Machine (SVM), Gaussian Naïve Bayes (Naïve Bayes), Stochastic Gradient Descent (SGD) and Logistic Regression (LR) algorithms.Īll experiments were tested with a cross-validation technique. We conducted our experiments using five different machine learning algorithms. This research will, therefore, test different machine learning algorithms on the macOS to determine which of the algorithms will produce the best result in detecting malware on the macOS operating system.

Macos malware used runonly to detection software#

Machine learning (ML) has been proven to be an excellent method for detecting malicious software on other operating system platforms. As a result of this growth, the number of malware targeting the macOS has also gone up significantly, which has given rise to the need to find a way to protect macOS users from such malicious software. These Apple workstations run on the macOS operating system. Moreover, this trend is set to continue as Apple makes inroads into the personal computer market previously dominated by the Windows operating system. The number of users choosing Apple desktops and laptops has increased significantly over the past recent years.