Evaluating Malware Detection System using Machine Learning Algorithms

Abstract

Malware, is any program or document that is unsafe to a PC client. Kinds of malware can incorporate PC infections, worms, Trojan ponies and spyware. These noxious projects can play out an assortment of capacities like taking, scrambling or erasing touchy information, adjusting or commandeering center processing capacities and observing clients' PC action. Malware identification is the way toward checking the PC and documents to distinguish malware. It is viable at distinguishing malware on the grounds that it includes numerous instruments and approaches. It's anything but a single direction measure, it's very intricate. The beneficial thing is malware identification and evacuation take under 50 seconds as it were. The outstanding development of malware is representing an extraordinary risk to the security of classified data. The issue with a significant number of the current order calculations is their small presentation in term of their capacity to identify and forestall malware from tainting the PC framework. There is a critical need to assess the exhibition of the current Machine Learning characterization calculations utilized for malware identification. This will help in making more hearty and productive calculations that have the ability to conquer the shortcomings of the current calculations. As of late, AI methods have been the main focus of the security specialists to distinguish malware and foresee their families powerfully. Yet, to the best of our information, there exists no complete work that looks at and assesses a sufficient number of machine learning strategies for characterizing malware and favorable examples. In this work, we led a set of examinations to assess AI strategies for distinguishing malware and their classification into respective families powerfully. This investigation did the presentation assessment of some characterization calculations like J45, LMT, Naive Bayes, Random Forest, MLP Classifier, Random Tree, AdaBoost, KStar. The presentation of the calculations was assessed as far as Accuracy, Precision, Recall, Kappa Statistics, F-Measure, Matthew Correlation Coefficient, Receiver Operator Characteristics Area and Root Mean Squared Error utilizing WEKA AI and information mining recreation device. Our test results showed that Random Forest calculation delivered the best exactness of 99.2%. This decidedly shows that the Random Forest calculation accomplishes great precision rates in identifying malware.