Hybrid Quantum-inspired Evolutionary Neural Networks for Intrusion Detection System

Quantum-inspired evolutionary algorithms harness quantum properties to optimize the search process within classical computers, efficiently addressing complex and challenging problems. This study first proposes an intrusion detection system (IDS) based on a hybrid model using quantum-inspired evolutionary neural networks. The model integrates a deep neural network (DNN) and a global best-guided quantum-inspired tabu search algorithm (GQTS). To safeguard against potential threats, an IDS is deployed to monitor network or system traffic and detect malicious attacks. Anomaly detection, a pivotal aspect of IDS, aims to establish a normal model to respond effectively to unknown abnormal attacks. The experiment utilizes the latest dataset, CICIDS2017, which is generated based on realistic background traffic. During the training phase, GQTS selects valid features from the dataset and optimizes the hyperparameters of the DNN setting automatically, significantly contributing to improving accuracy and reducing the false negative rate. The results highlight that the proposed hybrid model decreases computational complexity through feature selection and enhances model accuracy via suitable hyperparameter optimization compared to other state-of-the-art methods. The proposed model demonstrates great potential over alternative structures.