Purdue University Graduate School
Browse

File(s) under embargo

1

year(s)

11

month(s)

12

day(s)

until file(s) become available

An Image-based ML Approach for Wi-Fi Intrusion Detection System and Education Modules for Security and Privacy in ML

thesis
posted on 2024-05-02, 20:15 authored by Rayed Suhail AhmadRayed Suhail Ahmad

The research work presented in this thesis focuses on two highly important topics in the modern age. The first topic of research is the development of various image-based Network Intrusion Detection Systems (NIDSs) and performing a comprehensive analysis of their performance. Wi-Fi networks have become ubiquitous in enterprise and home networks which creates opportunities for attackers to target the networks. These attackers exploit various vulnerabilities in Wi-Fi networks to gain unauthorized access to a network or extract data from end users' devices. The deployment of an NIDS helps detect these attacks before they can cause any significant damages to the network's functionalities or security. Within the scope of our research, we provide a comparative analysis of various deep learning (DL)-based NIDSs that utilize various imaging techniques to detect anomalous traffic in a Wi-Fi network. The second topic in this thesis is the development of learning modules for security and privacy in Machine Learning (ML). The increasing integration of ML in various domains raises concerns about its security and privacy. In order to effectively address such concerns, students learning about the basics of ML need to be made aware of the steps that are taken to develop robust and secure ML-based systems. As part of this, we introduce a set of hands-on learning modules designed to educate students on the importance of security and privacy in ML. The modules provide a theoretical learning experience through presentations and practical experience using Python Notebooks. The modules are developed in a manner that allows students to easily absorb the concepts regarding privacy and security of ML models and implement it in real-life scenarios. The efficacy of this process will be obtained from the results of the surveys conducted before and after providing the learning modules. Positive results from the survey will demonstrate the learning modules were effective in imparting knowledge to the students and the need to incorporate security and privacy concepts in introductory ML courses.

Funding

Cybersecurity Modules Aligned with Undergraduate Computer Science and Engineering Curricula

Directorate for Education & Human Resources

Find out more...

History

Degree Type

  • Master of Science

Department

  • Electrical and Computer Engineering

Campus location

  • Hammond

Advisor/Supervisor/Committee Chair

Quamar Niyaz

Additional Committee Member 2

Khair A. Al Shamaileh

Additional Committee Member 3

Li-Zhe Tan