Publication Date
Spring 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.
Recommended Citation
Trehan, Harshit, "Fake malware opcodes generation using HMM and different GAN algorithms" (2021). Master's Projects. 1001.
DOI: https://doi.org/10.31979/etd.eq6a-twvq
https://scholarworks.sjsu.edu/etd_projects/1001
