Feature Selection for Imbalanced Network Intrusion Detection Systems Dataset Using Quantum Annealer

Network Intrusion Detection Systems (NIDS) play a critical role in cybersecurity but face challenges such as high-dimensional data, feature redundancy, and class imbalance. Traditional feature selection methods often struggle with efficiency and accuracy in these complex scenarios. This paper proposes a Quantum Annealing (QA) approach to address the challenges of the feature engineering in NIDS by maximizing relevance, minimizing redundancy, and reducing computational overhead. Using the UNSW-NB15 dataset, which reflects real-world network traffic, we formulate the feature selection problem as a Quadratic Unconstrained Binary Optimization (QUBO) and solve it using D-Wave's Quantum Annealer. The selected features are evaluated with Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. Simulation results show that QA reduces feature selection time by 60% compared to wrapper methods, achieving 99.14% accuracy with RF and 98.87% with MLP, while significantly improving detection rates for minority attack classes. Unlike conventional methods, QA effectively addresses imbalanced datasets without requiring artificial balancing, making it well-suited for real-world NIDS applications. This work demonstrates the potential of quantum-assisted cybersecurity, paving the way for scalable and high-performance intrusion detection systems.