Quantum Machine Learning for Adversarially Robust Cybersecurity: Network and Phishing Attack Detection

This project aims to enhance the detection of modern cyberattacks by investigating quantum machine learning (QML) and quantum neural network (QNN) approaches alongside classical machine learning and deep learning models. As cyber threats evolve, attackers increasingly employ stealthy strategies, such as low-and-slow attacks and phishing/spear-phishing, designed to evade traditional detection systems. Moreover, many existing models remain vulnerable to adversarial attacks, where subtle input manipulations can lead to incorrect predictions.

To address these challenges, the project will first establish strong classical baselines using traditional ML algorithms and deep neural networks (DNNs). Subsequently, quantum-based models, including QML and QNNs, will be implemented to assess their potential advantages in feature representation, learning efficiency, and robustness. A comprehensive comparative evaluation will be conducted using standard metrics such as accuracy, precision, recall, F1-score, evasion rate, and robustness under adversarial perturbations. The objective is to determine whether quantum approaches can improve the reliability and resilience of cybersecurity detection systems against advanced and evasive threats.

Faculty Supervisor:

Fadoua Khennou

Student:

Partner:

Cadi Ayyad University

Discipline:

Computer science

Sector:

Education

University:

Université de Moncton

Program: