Anti-ghost the detection of file-less malware

Abstract

The field of information technology has observed considerable development over the last century. Slowly and steadily, computer technology has worked its way into every field imaginable. Now, the use of computer technology can be observed everywhere; from personal use, to use on an enterprise level, rather, all companies rely on a computer in one form or another. In that regard, the computer is considered the most important invention of the 20th century. The integration of computer technology into work, specifically, made life easier. Work became more efficient, as people could easily accomplish things that often took days in a matter of minutes. All in all, the computer proved to be a safe, secure, and efficient tool for work and entertainment alike. This cyber cloud, however, emerged with a darker lining. Everything went into the cyber world, and thus, people began to think of ways to compromise that safety, and thus, the first malware emerged on the scene in 1986. Over the years, new ways to breach systems are being developed every day, the most recent being the use of file-less attack vectors. In response to the increasing amount of file-less malware all over the cyber world, demand for anti-malware systems has observed a considerable increase. Our project relates to said demand, providing a solution to the detection of file-less malware using behavioral analysis, and Machine Learning. Classification between malicious and benign files is conducted using API data, using SVM, Decision Trees, Naïve Bayes and KNN Algorithms.