Description of field of research:

Understanding and coping with malware is getting increasingly daunting, given their relentless growth in complexity and volume. Artificial Intelligence (AI) is considered to be the most effective approach to learn patterns and models behind such complexity, and to develop techniques that can control the malware spread and further evolution. This research project aims to provide an overview on AI techniques that have been used to detect and analyze (e.g., classify) malware in various environments (such as Desktop and Mobile). We will first perform a comprehensive survey on the state-of-the-art machine learning (ML) techniques to detect and analyze malware(s) and ransomware. These techniques range from Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), to other forms of Deep Neural Networks (DNNs). Furthermore, we will investigate the effectiveness of ML techniques in preventing the malware attacks on the networks (e.g., ML-based intrusion detection methods). The main objectives of the project are:

  1. Perform a comprehensive survey on the AI methods (e.g., CNNs, RNNs, GANs, and DNNs) to detect and analyse malware and ransomware and write a technical report on the findings. 
  2. Perform a comparative analysis on the AI-based defensive mechanisms to prevent malware and ransomware on the systems and networks. 
Research Area

Network security |
Malware detection and analysis |
Machine learning

In this project, student is expected to communicate with CSE supervisor for the continuous guidance and supervision. There will be weekly meetings on the project progress with a supervisor. This project will provide an opportunity to a student to directly involve in state-of-the-art and emerging field of cybersecurity i.e., malware detection using AI, and learn ways to conduct research for real applications. We also prefer student to have strong technical writing skills to write a survey report. 

From this project, student with the help of supervisor is expected to:

  1. Perform a comprehensive survey on state-of-the-art AI/ML-based malware detection and analysis techniques. 
  2. Perform a comparative analysis on the anti-malware approaches such as ML-based intrusion detection systems. 
  3. Produce critical findings and document in the form of a chapter/technical report.