Advanced quantum neural network-link net based intrusion detection framework and enhanced blowfish cryptography for data security

As cybersecurity threats evolve, it has become increasingly important to ensure data protection while successfully discovering intrusions. This paper introduces a novel Quantum Computation with Neural Networks for Intrusion Detection and Data Security (QCNN-IDDS) framework, which integrates advanced quantum computing and neural network techniques for intrusion detection and encryption. The framework uses a Quadratic Neural Network (QNN) to model complex, nonlinear relationships in data, improving intrusion detection performance. Data preprocessing is performed using the Double Normalization Technique (DNT), followed by feature extraction that incorporates statistical measures (e.g., mean, variance, skewness) to assess feature relevance. The detection process uses an Entropy Threshold Weighted Quantum Neural Network (ETW-QNN) and LinkNet to classify data as normal or abnormal. Data classified as normal is then encrypted using the Modulus-assisted Blowfish (MAB) algorithm, providing robust data security. Evaluation on UNSW-NB15 dataset demonstrates that the ETW-QNN model achieves a peak accuracy of 0.917, outperforming models like CNN + LSTM + GRU (0.747), LinkNet (0.742), EfficientNet (0.743), and ResNet (0.757), while DNN achieves the lowest accuracy at 0.730. The proposed framework offers significant improvements in both detection accuracy and data security compared to traditional methods. With its potential for high accuracy and low false positive rates, the QCNN-IDDS framework is expected to enhance the efficiency and reliability of real-world cybersecurity systems, paving the way for more robust, adaptive, and scalable solutions in dynamic and high-traffic environments.