Quantum Computing-based Optimization of Cybersecurity Protocols for Secure Data Protection

Abstract

The escalation of security attacks has led to a growing complexity and posed serious challenges for traditional IDS paradigms to securing and sustaining a resilient network infrastructure. Current solutions are generally inefficient at optimizing detection parameters, cannot detect attacks that have previously not been encountered and do not offer clear decision making processes. In this work, a framework for optimization of secure data protection using quantum computing is proposed which combines machine learning, anomaly detection, explainable AI and quantum computing inspired optimization techniques in a single cybersecurity framework to overcome these limitations. Selected network traffic from CICIDS2017 data set is used in the system to detect various types of attacks such as Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), PortScan and WebAttack. The multi-class intrusion detection uses an XGBoost (Extreme Gradient Boosted) Classifier to learn discriminative patterns from network flow features. The Quantum Approximate Optimization Algorithm (QAOA) is integrated to optimize certain model parameters (learning rate, tree depth and decision thresholds) that can enhance detection performance. Moreover, an Isolation Forest model is run concurrently to detect zero day and unknown anomalies not found in the training set. To enhance the interpretability, SHAP-based explainability is incorporated to measure the network's contribution to each prediction by its features. All the framework is deployed via Flask and is displayed on an interactive dashboard that shows attack classifications, threat risk scores, optimized configurations and explanatory insights. The hybrid architecture proposed shows a novel combination of classical artificial intelligence and quantum optimization techniques to create an adaptive, explainable, and intelligent cybersecurity solution.