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Adversarial Learning for Cyber Threat Intelligence: An Attention on Malware
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Dissertation
Adversarial Learning for Cyber Threat Intelligence: An Attention on Malware
2024
Overview
Cyber Threat Intelligence is the knowledge required to protect personal computers, corporations, and critical infrastructure from cyber threat actors. With the modern world's reliance on internet-connected devices, Cyber Threat Intelligence is a necessity. A prominent component of Cyber Threat Intelligence is malware analysis, research conducted to protect computer networks from malware attacks. Malware analysis has seen an application of Artificial Intelligence (AI) in recent years. To combat the increasingly versatile and mutable modern malware, Machine Learning (ML) is now a popular and effective complement to the existing signature-based techniques for malware triage and identification. However, ML is also a readily available tool for adversaries. Through adversarial learning on malware, adversaries have developed techniques for bypassing ML-based models by making their malware appear benign. Two challenges that have arisen in this area of study are modified malware detection and malware family classification. In this thesis, we aim to provide Deep Learning (DL) based solutions to these complex challenges. First, we propose H4rm0ny, the first Reinforcement Learning (RL) two-player game for malware generation and detection. Then, we propose Ch4os, a method for creating adversarial bytes with a generative framework. We also propose a practical and efficient solution for zero-day malware variant matching with reconstruction. Finally, we propose Mecha, a neuro-symbolic approach to open-set malware family classification. We have conducted multiple experiments to observe the efficacy of our solutions against datasets of thousands of software samples. All solutions we discuss in this thesis have improved against the state-of-the-art in empirical testing.
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9798342136228
