A Crowding Distance-Driven Adaptive Genetic Algorithm with Multiobjective Evolutionary Optimization for Robust Attack Detection
Received: 21 September 2025 | Revised: 25 October 2025 and 15 November 2025 | Accepted: 17 November 2025 | Online: 9 February 2026
Corresponding author: D. Sudha
Abstract
With the growing sophistication of cyber-attacks, traditional detection mechanisms struggle to maintain high accuracy while minimizing false positives and false negatives. The selection of optimal feature subsets and configurations significantly affects detection performance, necessitating advanced optimization strategies. Conventional Genetic Algorithms (GAs) lack dynamic adaptability and often converge prematurely due to low diversity. Moreover, single-objective optimization fails to balance conflicting objectives such as maximizing detection accuracy while minimizing false alarms. This study proposes an Adaptive Genetic Algorithm (AGA) integrated with Crowding Distance Selection (CDS) in a Multiobjective Evolutionary Algorithm (MOEA) framework to optimize binary-encoded feature subsets and system configurations. The proposed AGA dynamically adjusts mutation rates based on population diversity metrics calculated using the Hamming distance. Higher mutation rates are triggered when diversity is low to encourage exploration. The crowding distance is used to select diverse solutions in the multiobjective space defined by accuracy, false positives, and false negatives. A diverse initial population of feature subsets is evolved using adaptive mutation and crossover, guided by fitness scores from MOEA. Experimental evaluation on the NSL-KDD dataset reveals that the proposed AGA-MOEA method achieves a detection accuracy of 97.4%, a false positive rate of 2.1%, and a false negative rate of 3.8%.
Keywords:
adaptive genetic algorithm, multiobjective optimization, crowding distance, feature selection, attack detectionDownloads
References
S. Wang, J. Liu, and Y. Jin, "A Computationally Efficient Evolutionary Algorithm for Multiobjective Network Robustness Optimization," IEEE Transactions on Evolutionary Computation, vol. 25, no. 3, pp. 419–432, June 2021. DOI: https://doi.org/10.1109/TEVC.2020.3048174
S. Wang, Y. Jin, and M. Cai, "Enhancing the Robustness of Networks Against Multiple Damage Models Using a Multifactorial Evolutionary Algorithm," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 7, pp. 4176–4188, July 2023. DOI: https://doi.org/10.1109/TSMC.2023.3241621
S. Zhou, M. Huang, Y. Sun, and K. Li, "Evolutionary Multi-objective Optimization for Contextual Adversarial Example Generation," Proceedings of the ACM Software Engineering, vol. 1, no. FSE, Apr. 2024, Art. no. 101. DOI: https://doi.org/10.1145/3660808
Y. Chen, Q. Lin, W. Wei, J. Ji, K. C. Wong, and C. A. C. Coello, "Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing," Knowledge-Based Systems, vol. 244, May 2022, Art. no. 108505. DOI: https://doi.org/10.1016/j.knosys.2022.108505
N. Nissar, N. Naja, and A. Jamali, "Securing VANETs: Multi-Objective Intrusion Detection With Variational Autoencoders," IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 3867–3874, Oct. 2024. DOI: https://doi.org/10.1109/TCE.2024.3372691
J. Wang, Z. Yin, J. Jiang, and Y. Du, "Attention-guided black-box adversarial attacks with large-scale multiobjective evolutionary optimization," International Journal of Intelligent Systems, vol. 37, no. 10, pp. 7526–7547, 2022. DOI: https://doi.org/10.1002/int.22892
J. Zhang, B. Gong, M. Waqas, S. Tu, and S. Chen, "Many-Objective Optimization Based Intrusion Detection for in-Vehicle Network Security," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 12, pp. 15051–15065, Sept. 2023. DOI: https://doi.org/10.1109/TITS.2023.3296002
S. Shao, Y. Tian, L. Zhang, K. C. Tan, and X. Zhang, "An Evolutionary Algorithm for Solving Large-Scale Robust Multi-Objective Optimization Problems," IEEE Transactions on Evolutionary Computation, 2024. DOI: https://doi.org/10.1109/TEVC.2024.3435006
A. Muneer, S. M. Taib, S. M. Fati, A. O. Balogun, and I. A. Aziz, "A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data," Computers, Materials & Continua, vol. 70, no. 3, pp. 5363–5381, 2022. DOI: https://doi.org/10.32604/cmc.2022.021113
Y. K. Saheed, A. I. Abiodun, S. Misra, M. K. Holone, and R. Colomo-Palacios, "A machine learning-based intrusion detection for detecting internet of things network attacks," Alexandria Engineering Journal, vol. 61, no. 12, pp. 9395–9409, Dec. 2022. DOI: https://doi.org/10.1016/j.aej.2022.02.063
M. T. Tally and H. Amintoosi, "A hybrid method of genetic algorithm and support vector machine for intrusion detection," International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 1, Feb. 2021, Art. no. 900. DOI: https://doi.org/10.11591/ijece.v11i1.pp900-908
M. Ajdani and H. Ghaffary, "Introduced a new method for enhancement of intrusion detection with random forest and PSO algorithm," Security and Privacy, vol. 4, no. 2, 2021, Art. no. e147. DOI: https://doi.org/10.1002/spy2.147
J. C. Huang, G. Q. Zeng, G. G. Geng, J. Weng, K. D. Lu, and Y. Zhang, "Differential evolution-based convolutional neural networks: An automatic architecture design method for intrusion detection in industrial control systems," Computers & Security, vol. 132, Sept. 2023, Art. no. 103310. DOI: https://doi.org/10.1016/j.cose.2023.103310
K. P. Chandrasekaran and V. M. Chidambaram, "Integrating Novel Mechanisms for Threat Detection in Enhanced Data Classification using Ant Colony Optimization with Recurrent Neural Network," Journal of Cybersecurity and Information Management, vol. 14, no. 2, pp. 132–147, 2024. DOI: https://doi.org/10.54216/JCIM.140209
R. R. N. A. Ogaili et al., "AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection," Mesopotamian Journal of CyberSecurity, vol. 5, no. 1, pp. 104–120, Feb. 2025. DOI: https://doi.org/10.58496/MJCS/2025/008
T. Chen and M. Li, "The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software Engineering," ACM Transactions on Software Engineering Methodology, vol. 32, no. 1, Oct. 2023, Art. no. 5. DOI: https://doi.org/10.1145/3514233
I. Rahimi, A. H. Gandomi, M. R. Nikoo, and F. Chen, "A comparative study on evolutionary multi-objective algorithms for next release problem," Applied Soft Computing, vol. 144, Sept. 2023, Art. no. 110472. DOI: https://doi.org/10.1016/j.asoc.2023.110472
X. Xu, C. Xie, Z. Luo, C. Zhang, and T. Zhang, "A multi-objective evolutionary algorithm based on dimension exploration and discrepancy evolution for UAV path planning problem," Information Sciences, vol. 657, Feb. 2024, Art. no. 119977. DOI: https://doi.org/10.1016/j.ins.2023.119977
T. Yang and Y. Zhou, "Analysis of Multiobjective Evolutionary Algorithms on Fitness Function With Time-Linkage Property," IEEE Transactions on Evolutionary Computation, vol. 28, no. 3, pp. 837–843, June 2024. DOI: https://doi.org/10.1109/TEVC.2024.3371519
R. Basfar, M. Y. Dahab, A. M. Ali, F. Eassa, and K. Bajunaied, "Enhanced Intrusion Detection in Software-Defined Networking using Advanced Feature Selection: The EMRMR Approach," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 19001–19008, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9256
D. K. Singh and M. Shrivastava, "Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7130–7134, June 2021. DOI: https://doi.org/10.48084/etasr.4149
"Intrusion Detection System [NSL-KDD]." Kaggle, [Online]. Available: https://kaggle.com/code/eneskosar19/intrusion-detection-system-nsl-kdd.
"IDS 2017." Canadian Institute for Cybersecurity, [Online]. Available: https://www.unb.ca/cic/datasets/ids-2017.html.
"UNSW-NB15." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/dhoogla/unswnb15.
"Ton-IoT." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/amaniabourida/ton-iot.
A. K. Shukla and A. Sharma, "Reduce false intrusion alerts by using PSO feature selection in NSL-KDD dataset," IET Conference Proceedings, vol. 2023, no. 5, pp. 226–231, July 2023. DOI: https://doi.org/10.1049/icp.2023.1495
K. Rithesh, A. V. Gautham, and K. Chandra Sekaran, "Network Anomaly Detection Using Artificial Neural Networks Optimised with PSO-DE Hybrid," in Security in Computing and Communications, vol. 969, S. M. Thampi, S. Madria, G. Wang, D. B. Rawat, and J. M. Alcaraz Calero, Eds. Springer Singapore, 2019, pp. 257–270. DOI: https://doi.org/10.1007/978-981-13-5826-5_19
Downloads
How to Cite
License
Copyright (c) 2025 D. Sudha, D. Ganesh
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
