Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS
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Abstract
This paper investigates Genetic Algorithm (GA)–driven hyperparameter optimization for one-dimensional Convolutional Neural Networks (1D-CNNs) in network intrusion detection systems (NIDS). Building on prior evidence that evolutionary search markedly improves deep models for NIDS, we optimize nine key hyperparameters (filters, kernel size, pooling size, dense depth/width, dropout, learning rate, batch size, and epochs) and train/evaluate on UNSW-NB15, CIC-IDS2017, and NSL-KDD using standard metrics: accuracy, loss, precision, recall, and F1-score. Our GA framework encodes hyperparameters as chromosomes and evolves candidates via selection, crossover, and mutation to maximize validation performance. Experiments show that GA-tuned 1D-CNNs consistently outperform non-optimized baselines across datasets; on UNSW-NB15, for example, GA attains ~99.31% accuracy, aligning with the best reported performance for GA-optimized 1D-CNNs. Results highlight that GA delivers robust gains with practical compute budgets while preserving strong precision-recall trade-offs, and that effectiveness can vary by dataset characteristics. Overall, GA-based hyperparameter optimization offers a simple, reproducible path to higher accuracy and reliability in deep learning–based NIDS, advancing the development of adaptable intrusion-detection solutions for evolving cyber threats.
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[1] Kilichev, D.; Kim, W. Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO. Mathematics 2023, 11, 3724. https://doi.org/10.3390/math11173724
[2] Kilichev, D.; Turimov, D.; Kim, W. Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models. Mathematics 2024, 12, 571. https://doi.org/10.3390/math12040571
[3] Makhmudov, F.; Kilichev, D.; Giyosov, U.; Akhmedov, F. Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems. Mathematics 2025, 13, 712. https://doi.org/10.3390/math13050712
[4] A. Abdusalomov, D. Kilichev, R. Nasimov, I. Rakhmatullayev and Y. I. Cho, "Optimizing Smart Home Intrusion Detection with Harmony-Enhanced Extra Trees," in IEEE Access, doi: 10.1109/ACCESS.2024.3422999
[5] Selvarajan, P.; Salman, R.; Ahamed, S.; Jayasuriya, P. Networks Intrusion Detection Using Optimized Hybrid Network. In Proceedings of the 2023 International Conference on Smart Computing and Application (ICSCA), Hail, Saudi Arabia, 5–6 February 2023; pp. 1–6.
[6] Kanna, R.P.; Santhi, P. Unified Deep Learning approach for Efficient Intrusion Detection System using Integrated Spatial–Temporal Features. Knowl.-Based Syst. 2021, 226, 107132.
[7] Zhao, X.; Su, H.; Sun, Z. An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks. Mathematics 2022, 10, 3941.
[8] Yang, L.; Shami, A. A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles. In Proceedings of the ICC 2022—IEEE International Conference on Communications, Foshan, China, 11–13 August 2022; pp. 2774–2779.
[9] Chen, Y.; Lin, Q.; Wei, W.; Ji, J.; Wong, K.C.; Coello, C.A. Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing. Knowl.-Based Syst. 2022, 244, 108505.
[10] Ragab, M.; Sabir, F. Outlier detection with optimal hybrid deep learning enabled intrusion detection system for ubiquitous and smart environment. Sustain. Energy Technol. Assess. 2022, 52, 102311.
[11] Yan, F.; Zhang, G.; Zhang, D.; Sun, X.; Hou, B.; Yu, N. TL-CNN-IDS: Transfer learning-based intrusion detection system using convolutional neural network. J. Supercomput. 2023, 242
[12] Okey, O.D.; Melgarejo, D.C.; Saadi, M.; Rosa, R.L.; Kleinschmidt, J.H.; Rodríguez, D.Z. Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN. IEEE Access 2023, 11, 1023–1038.
[13] El-Ghamry, A.; Darwish, A.; Hassanien, A.E. An optimized CNN-based intrusion detection system for reducing risks in smart farming. Internet Things 2023, 22, 100709.
[14] Rosay, A.; Riou, K.; Carlier, F.; Leroux, P. Multi-layer perceptron for network intrusion detection: From a study on two recent data sets to deployment on automotive processor. Ann. Telecommun. 2022, 77, 371–394.
[15] Obeidat, A.; Yaqbeh, R. Smart Approach for Botnet Detection Based on Network Traffic Analysis. J. Electr. Comput. Eng. 2022, 2022, 3073932.
[16] Zhang, X.; Zou, D.; Shen, X. A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization. Mathematics 2018, 6, 287.
[17] Moustafa, N.; Slay, J. UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, 10–12 November 2015; pp. 1–6.
[18] Sharafaldin, I.; Habibi Lashkari, A.; Ghorbani, A.A. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy—ICISSP, Funchal, Portugal, 22–24 January 2018; pp. 108–116.
[19] Tavallaee, M.; Bagheri, E.; Lu, W.; Ghorbani, A.A. A detailed analysis of the KDD CUP 99 data set. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, 8–10 July 2009; pp. 1–6.
[20] Gautam, S.; Henry, A.; Zuhair, M.; Rashid, M.; Javed, A.R.; Maddikunta, P.K.R. A Composite Approach of Intrusion Detection Systems: Hybrid RNN and Correlation-Based Feature Optimization. Electronics 2022, 11, 3529.
[21] Preuveneers, D.; Rimmer, V.; Tsingenopoulos, I.; Spooren, J.; Joosen, W.; Ilie-Zudor, E. Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study. Appl. Sci. 2018, 8, 2663.
[22] Potluri, S.; Ahmed, S.; Diedrich, C. Convolutional Neural Networks for Multi-class Intrusion Detection System. In Proceedings of the Mining Intelligence and Knowledge Exploration, Cluj-Napoca, Romania, 20–22 December 2018; Groza, A., Prasath, R., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 225–238.
[23] Azizjon, M.; Jumabek, A.; Kim, W. 1D CNN based network intrusion detection with normalization on imbalanced data. In Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, 19–21 February 2020; pp. 218–224.
[24] Gamal, M.; Abbas, H.M.; Moustafa, N.; Sitnikova, E.; Sadek, R.A. Few-Shot Learning for Discovering Anomalous Behaviors in Edge Networks. Comput. Mater. Contin. 2021, 69, 1823–1837.
[25] Al-Turaiki, I.; Altwaijry, N. A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection. Big Data 2021, 9, 233–252.
[26] Mohammadpour, L.; Ling, T.C.; Liew, C.S.; Aryanfar, A. A mean convolutional layer for intrusion detection system. Secur. Commun. Netw. 2020, 2020, 8891185.
[27] Aldarwbi, M.Y.; Lashkari, A.H.; Ghorbani, A.A. The sound of intrusion: A novel network intrusion detection system. Comput. Electr. Eng. 2022, 104, 108455.
[28] Qazi, E.U.H.; Almorjan, A.; Zia, T. A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection. Appl. Sci. 2022, 12, 7986.
[29] Ding, Y.; Zhai, Y. Intrusion detection system for NSL-KDD dataset using convolutional neural networks. In Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, Shenzhen, China, 8–10 December 2018; pp. 81–85.
[30] Zhang, X.; Ran, J.; Mi, J. An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic. In Proceedings of the 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China, 19–20 October 2019; pp. 456–460.
[31] Verma, A.K.; Kaushik, P.; Shrivastava, G. A Network Intrusion Detection Approach Using Variant of Convolution Neural Network. In Proceedings of the 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 17–19 July 2019; pp. 409–416.
[32] Li, Y.; Xu, Y.; Liu, Z.; Hou, H.; Zheng, Y.; Xin, Y.; Zhao, Y.; Cui, L. Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement 2020, 154, 107450.
[33] Yang, H.; Wang, F. Wireless Network Intrusion Detection Based on Improved Convolutional Neural Network. IEEE Access 2019, 7, 64366–64374.
[34] Khan, R.U.; Zhang, X.; Alazab, M.; Kumar, R. An Improved Convolutional Neural Network Model for Intrusion Detection in Networks. In Proceedings of the 2019 Cybersecurity and Cyberforensics Conference (CCC), Melbourne, Australia, 8–9 May 2019; pp. 74–77.
[35] Wang, H.; Cao, Z.; Hong, B. A network intrusion detection system based on convolutional neural network. J. Intell. Fuzzy Syst. 2020, 38, 7623–7637.
[36] Wang, X.; Yin, S.; Li, H.; Wang, J.; Teng, L. A network intrusion detection method based on deep multi-scale convolutional neural network. Int. J. Wirel. Inf. Netw. 2020, 27, 503–517.
[37] Jo, W.; Kim, S.; Lee, C.; Shon, T. Packet Preprocessing in CNN-Based Network Intrusion Detection System. Electronics 2020, 9, 1151.
[38] Hu, Z.; Wang, L.; Qi, L.; Li, Y.; Yang, W. A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network. IEEE Access 2020, 8, 195741–195751.
[39] Akhtar, M.S.; Feng, T. Deep learning-based framework for the detection of cyberattack using feature engineering. Secur. Commun. Netw. 2021, 2021, 6129210.
[40] Zhang, H.; Huang, L.; Wu, C.Q.; Li, Z. An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset. Comput. Netw. 2020, 177, 107315.
[41] Meliboev, A.; Alikhanov, J.; Kim, W. Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets. Electronics 2022, 11, 515.
[42] Altunay, H.C.; Albayrak, Z. A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks. Eng. Sci. Technol. Int. J. 2023, 38, 101322.
[43] Thilagam, T.; Aruna, R. LM-GA: A Novel IDS with AES and Machine Learning Architecture for Enhanced Cloud Storage Security. J. Mach. Comput. 2023, 3, 69–79.
[44] Karthic, S.; Kumar, S.M. Hybrid Optimized Deep Neural Network with Enhanced Conditional Random Field Based Intrusion Detection on Wireless Sensor Network. Neural Process. Lett. 2023, 55, 459–479.
[45] Khan, A.S.; Ahmad, Z.; Abdullah, J.; Ahmad, F. A Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network. IEEE Access 2021, 9, 87079–87093.
[46] Mendonça, R.V.; Teodoro, A.A.M.; Rosa, R.L.; Saadi, M.; Melgarejo, D.C.; Nardelli, P.H.J.; Rodríguez, D.Z. Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network. IEEE Access 2021, 9, 61024–61034.
[47] Bowen, B.; Chennamaneni, A.; Goulart, A.; Lin, D. BLoCNet: A hybrid, dataset-independent intrusion detection system using deep learning. Int. J. Inf. Secur. 2023, 22, 893–917.
[48] Li, S.; Li, Q.; Li, M. A Method for Network Intrusion Detection Based on GAN-CNN-BiLSTM. Int. J. Adv. Comput. Sci. Appl. 2023, 14, 507–515.
[49] Nguyen, M.T.; Kim, K. Genetic convolutional neural network for intrusion detection systems. Future Gener. Comput. Syst. 2020, 113, 418–427.
[50] Bhuvaneshwari, K.S.; Venkatachalam, K.; Hubálovský, S.; Trojovský, P.; Prabu, P. Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier. Comput. Mater. Contin. 2022, 70, 5949–5965.