MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES

Abstract

Malware is malicious software disseminated to infiltrate the secrecy, integrity, and functionality of a system, such as viruses, worms, Trojans, backdoors, and spyware. To defend against an increasing number of sophisticated malware attacks, deep-learning based Malware Detection Systems (MDSs) have become a vital component of my economic and national security. The dataset, malware dataset is implemented as input. The input dataset is taken from dataset repository. Based on the characteristics of the observations, the dataset was created in a UNIX / Lunix-based virtual machine for classification purposes, which are harmless with malware software for Android devices. The data set consists of 100,000 observation data and 35 features. precision, recall, f1-score.