Publication Date

Spring 5-25-2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Fabio Di Troia

Second Advisor

Katerina Potika

Third Advisor

Nada Attar

Keywords

fake op code generation, HMM, GANs

Abstract

Malware, or malicious software, is a program that is intended to harm systems. In the past decade, the number of malware attacks have grown and, more importantly, evolved. Many researchers have successfully integrated cutting edge Machine Learning techniques to combat this ever present and growing threat to cyber and information security. One big challenge faced by many researchers is the lack of enough data to train machine learning models and specifically deep neural networks properly. Generative modelling has proven to be very efficient at generating synthesized data that can match the actual data distribution.

In this project, we aim to generate malware samples as opcode sequences and attempt to differentiate between the fake and real samples. We use different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate fake samples.

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