Optimized Deep Neural Network to Optimally Classify Attacks as an Intrusion Detection System
Keywords:
convolutional neural network, genetic algorithm, machine learning, intrusion detection systems, NSL-KDD dataset, optimized deep neural networkAbstract
The expansion of internet usage has made networks increasingly vulnerable to continuous and escalating breaches. This is due to the critical nature of the information exchanged across these networks. Given the importance of this information, a method for protecting it from breaches is essential. Several such methods exist, including those within machine learning (ML) and deep learning (DL). In this research, we will utilize the Genetic Algorithm (GA) to determine the optimal hyperparameter values for the deep learning model, which will then be used to classify attacks. Results show that the accuracy was comparable to other works, 98.54%, with a precision of 98.55%. The proposed deep neural network is based on convolutional neural network (CNN). On the other hand, the suggested model will need, of course, to a training/testing dataset, thus, the NSL-Kdd dataset was affordable, where it was cleaned and prepared for the training and testing purposes. Last but not least, it is recommended to make use of the suggested approach to be generalized against attacks in real-world systems.
References
M. Al-Qatf, Y. Lasheng, M. Al-Habib, and K. Al-Sabahi, "Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection," IEEE Access, vol. 6, pp. 52843-52856, 2018, doi: 10.1109/ACCESS.2018.2869577.
M. Safaldin, M. Otair, and L. Abualigah, "Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp. 1559-1576, 2021/02/01 2021, doi: 10.1007/s12652-020-02228-z.
S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, "Multi-Verse Optimizer: a nature-inspired algorithm for global optimization," Neural Computing and Applications, vol. 27, no. 2, pp. 495-513, 2016/02/01 2016, doi: 10.1007/s00521-015-1870-7.
N. Moustafa and J. Slay, "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)," in 2015 Military Communications and Information Systems Conference (MilCIS), 10-12 Nov. 2015 2015, pp. 1-6, doi: 10.1109/MilCIS.2015.7348942.
T. Sood, S. Prakash, S. Sharma, A. Singh, and H. Choubey, "Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network," Wireless Personal Communications, vol. 126, no. 1, pp. 911-931, 2022/09/01 2022, doi: 10.1007/s11277-022-09776-x.
Z. Zhang, Y. Zhao, A. Canes, D. Steinberg, and O. Lyashevska, "Predictive analytics with gradient boosting in clinical medicine," (in eng), Ann Transl Med, vol. 7, no. 7, p. 152, Apr 2019, doi: 10.21037/atm.2019.03.29.
M. Faris, M. N. Mahmud, M. F. Mohd Salleh, and B. Alsharaa, "A differential evolution-based algorithm with maturity extension for feature selection in intrusion detection system," Alexandria Engineering Journal, vol. 81, pp. 178-192, 2023/10/15/ 2023, doi: https://doi.org/10.1016/j.aej.2023.09.032.
R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial Intelligence, vol. 97, no. 1, pp. 273-324, 1997/12/01/ 1997, doi: https://doi.org/10.1016/S0004-3702(97)00043-X.
I. Kononenko, "Semi-naive bayesian classifier," Berlin, Heidelberg, 1991: Springer Berlin Heidelberg, in Machine Learning — EWSL-91, pp. 206-219.
S. Shyam Sundar, R. Bhuvaneswaran, and L. SaiRamesh, "Ensemble Based Optimal Feature Selection Algorithm for Efficient Intrusion Detection in Wireless Sensor Network," KSII Transactions on Internet and Information Systems (TIIS), vol. 18, no. 8, pp. 2214-2229, 2024, doi: 10.3837/tiis.2024.08.009.
L. Abualigah et al., "Optimizing Intrusion Detection in Wireless Sensor Networks via the Improved Chameleon Swarm Algorithm for Feature Selection," IET Communications, vol. 19, no. 1, p. e70029, 2025, doi: https://doi.org/10.1049/cmu2.70029.
P. Ananth, S. Saravanan, S. Chinnathambi, and S. Karthikeyan, "IoT intrusion detection in wireless sensor networks: A comparative analysis of machine learning algorithms for enhanced security," AIP Conference Proceedings, vol. 3279, no. 1, 2025, doi: 10.1063/5.0262752.
M. Devi, P. Nandal, and H. Sehrawat, "A lightweight approach for intrusion detection in WSNs based on DCGAN," International Journal of Information Technology, vol. 17, no. 2, pp. 951-957, 2025/03/01 2025, doi: 10.1007/s41870-024-02347-2.
D. Karunkuzhali, K. P. Arunachalam, R. Ramamoorthi, and R. K. Kadu, "Cyber-physical system for enhanced WSN-IoT security using spherical graph triple convolutional neural network with planet optimization algorithm," Progress in Engineering Science, vol. 2, no. 3, p. 100108, 2025/09/01/ 2025, doi: https://doi.org/10.1016/j.pes.2025.100108.
J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95 - International Conference on Neural Networks, 27 Nov.-1 Dec. 1995 1995, vol. 4, pp. 1942-1948 vol.4, doi: 10.1109/ICNN.1995.488968.
P. Rahmani, M. Arefi, S. M. S. S. Shojae, and A. Mirzaee, "IoT-RNNEI: An Internet of Things Attack Detection Model Leveraging Random Neural Network and Evolutionary Intelligence," IET Communications, vol. 19, no. 1, p. e70055, 2025, doi: https://doi.org/10.1049/cmu2.70055.
R. Saravana Ram and A. Gopi Saminathan, "An Intrusion Detection System in WSN Using an Optimized Self-Attention-Based Progressive Generative Adversarial Network," IETE Journal of Research, vol. 71, no. 4, pp. 1176-1189, 2025/04/03 2025, doi: 10.1080/03772063.2025.2452339.
I. Almomani, B. Al-Kasasbeh, and M. Al-Akhras, "WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks," Journal of Sensors, vol. 2016, p. 4731953, 2016/09/29 2016, doi: 10.1155/2016/4731953.
T. S. L. V. Ayyarao et al., "War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization," IEEE Access, vol. 10, pp. 25073-25105, 2022, doi: 10.1109/ACCESS.2022.3153493.
M. Chahardoli, N. O. Eraghi, and S. Nazari, "Namib beetle optimization algorithm: A new meta-heuristic method for feature selection and dimension reduction," Concurrency and Computation: Practice and Experience, vol. 34, no. 1, p. e6524, 2022, doi: https://doi.org/10.1002/cpe.6524.
M. Sriraghavendra, M. Elsadig, I. H. Jaghdam, S. Abdel-Khalek, B. Galeebathullah, and S. Alkhalaf, "Knowledge Improved Hybrid DNN–KAN Framework for Intrusion Detection in Wireless Sensor Networks," IEEE Access, vol. 13, pp. 127558-127569, 2025, doi: 10.1109/ACCESS.2025.3586783.
I. T. Jolliffe, Principal Component Analysis, 2nd edition (Springer Series in Statistics). New York, NY: Springer, 2002.
M. M. Rahman, S. A. Shakil, and M. R. Mustakim, "A survey on intrusion detection system in IoT networks," Cyber Security and Applications, vol. 3, p. 100082, 2025/12/01/ 2025, doi: https://doi.org/10.1016/j.csa.2024.100082.
