Volume 12 • Issue 1 • PP: 66 –83 • 2027
Bio-Inspired Cyber Defense: Enhancing Intrusion Detection through the Waterwheel Plant Optimization Algorithm
Open Access & Copyright
© 2027 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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In today’s increasingly interconnected digital landscape, intrusion detection systems (IDS) are critical for ensuring the security and integrity of network infrastructures. However, the effectiveness of traditional IDS models can be hampered by challenges such as high-dimensional data and the complex dynamics of attack patterns. This paper addresses these challenges by leveraging the Waterwheel Plant Optimization Algorithm (WWPA), a bio-inspired metaheuristic, to optimize the hyperparameters of the Support Vector Machine (SVM), a widely used classifier for intrusion detection. Our contributions include the development of an optimized machine learning framework that significantly improves classification performance by integrating WWPA with SVM. Experimental results show that the baseline SVM achieves an accuracy of 86.36%, while the WWPA-optimized SVM yields an impressive accuracy of 97.00%. These results highlight the efficacy of WWPA in navigating the complex hyperparameter search space and enhancing the sensitivity and precision of intrusion detection models. The implications of this work are profound for cybersecurity, as it provides a robust, efficient, and adaptive solution for detecting intrusions with high accuracy, thereby improving the reliability and responsiveness of modern IDS systems.
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References
[1] Shailendra Mishra. Exploring the impact of ai-based cyber security financial sector management. Applied Sciences, 13:5875–5875, 2023.
[2] A Salem, Safaa M. Azzam, O. E. Emam, and Amr A. Abohany. Advancing cybersecurity: a comprehensive review of ai-driven detection techniques, 2024.
[3] Krishnashree Achuthan, Sasangan Ramanathan, Sethu-raman Srinivas, and Raghu Raman. Advancing cyber-security and privacy with artificial intelligence: current trends and future research directions, 2024.
[4] Sean Oesch, Phillipe Austria, Amul Chaulagain, Brian Weber, Cory Watson, Matthew Dixson, and Amir Sadovnik. The path to autonomous cyber defense. arXiv (Cornell University), 2024.
[5] Nachaat Mohamed. Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms. Knowledge and Information Systems, 2025.
[6] Nazhir Amaya-Tejera, Margarita Gamarra, Jorge I. Vélez, and Eduardo Zurek. A distance-based kernel for classification via support vector machines. Frontiers in Artificial Intelligence, 7, 2024.
[7] Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, and Tanner Boyle. Intrusion detection system based on machine learning using least square support vector machine. Scientific Reports, 15, 2025.
[8] Gayeong Eom and Haewon Byeon. Searching for opti-mal oversampling to process imbalanced data: Gen-erative adversarial networks and synthetic minority over-sampling technique. Mathematics, 11:3605–3605, 2023.
[9] Cataldo Guaragnella. Improving classification perfor-mance by addressing dataset imbalance: A case study for pest management, 2025.
[10] Alberto Mozó, Amit Karamchandani, Luis de la Cal, Sandra Gómez-Canaval, Antonio Pastor, and Lluís Gifre. A machine-learning-based cyberattack detector for a cloud-based sdn controller. Applied Sciences, 13:4914–4914, 2023.
[11] Mahdi Soltani, Khashayar Khajavi, Mahdi Jafari Siavoshani, and Amir Hossein Jahangir. A multi-agent adaptive deep learning framework for online intrusion detection. Cybersecurity, 7, 2024.
[12] Niloofar Heidarikohol, Shuvalaxmi Dass, and Ak-bar Siami Namin. Evolutionary defense: Advancing moving target strategies with bio-inspired reinforcement learning to secure misconfigured software applications. 2025.
[13] Tian Tian, Chen Zhang, Bo Jiang, Huamin Feng, and Zhigang Lu. Insider threat detection for specific threat scenarios. Cybersecurity, 8, 2025.
[14] Pierpaolo Dini, Abdussalam Elhanashi, Andrea Begni, Sergio Saponara, Qinghe Zheng, and Kaouther Gasmi. Overview on intrusion detection systems design exploit-ing machine learning for networking cybersecurity. Ap-plied Sciences, 13:7507–7507, 2023.
[15] Tanwir Ahmad and Drago¸s Tru¸scan. Efficient early anomaly detection of network security attacks using deep learning. pages 154–159, 2023.
[16] Wa’ad H. Aljuaid and Sultan S. Alshamrani. A deep learning approach for intrusion detection systems in cloud computing environments. Applied Sciences, 14:5381–5381, 2024.
[17] Lorenzo Diana, Pierpaolo Dini, and Davide Paolini. Overview on intrusion detection systems for computers networking security. Computers, 14:87–87, 2025.
[18] K.A. Dhanya, Sulakshan Vajipayajula, Kartik Srini-vasan, Anjali Tibrewal, Senthil Kumar Thangavel, and T. Gireesh Kumar. Detection of network attacks using machine learning and deep learning models. Procedia Computer Science, 218:57–66, 2023.
[19] M Eraqi Hesham, Mohammed Essam, Mohamed Ba-haa, Ahmed Mohamed, Mohamed Gomaa, Mena Hany, and Wael Elsersy. Evaluating predictive models in cy-bersecurity: A comparative analysis of machine and deep learning techniques for threat detection. 45:33–38, 2024.
[20] Md Liakat Ali, Kutub Thakur, Suzanna Schmeelk, Joan E. DeBello, and Denise Dragos. Deep learning vs. machine learning for intrusion detection in com-puter networks: A comparative study. Applied Sciences, 15:1903–1903, 2025.
[21] Aymen Akremi. Design and analysis of an effective architecture for machine learning based intrusion detec-tion systems, 2025.
[22] S. Saif, Aqeef Alim Ansari, Suparna Biswas, and D. Giri. A comprehensive analysis of machine learning-based intrusion detection systems: evaluating datasets and algorithms for internet of things. Journal of Cyber Security Technology, 0(0):1–27, 2024.
[23] G. Rohini, C. Gnana Kousalya, and J. Bino. Intrusion detection system with an ensemble learning and feature selection framework for iot networks. IETE Journal of Research, 69(12):8859–8875, 2023.
[24] M. Alweshah, S. Alkhalaileh, M. Beseiso, M. Almi-ani, and S. Abdullah. Intrusion detection for iot based on a hybrid shuffled shepherd optimization algorithm. The Journal of Supercomputing, 78(10):12278–12309, 2022.
[25] C. Zhou. Detection and classification of phishing web-sites using machine learning approach taking advantage of metaheuristic algorithms efficiency. Journal of Cyber Security Technology, 0(0):1–18, 2025.
[26] Y. K. Saheed, Temitope Olubanjo Kehinde, Mustafa Ayobami Raji, and U. A. Baba. Feature selection in intrusion detection systems: a new hybrid fusion of bat algorithm and residue number system. Journal of Infor-mation and Telecommunication, 8(2):189–207, 2024.
[27] R. Akula and G. S. N. Kumar. Optimizing intrusion detection systems using deep learning and genetic al-gorithms for network traffic analysis: A survey. In Artificial Intelligence in Internet of Things (IoT): Key Digital Trends, page 357–369. Springer Nature, 2025.
[28] G. G. Gebremariam, J. Panda, and S. Indu. Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks. Connection Science, 35(1):2246703, 2023.
[29] Y. K. Saheed and S. Misra. A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the internet of things. International Journal of Information Security, 23(3):1557–1581, 2024.
[30] D. Mehanovi´c, D. Keˇco, J. Kevri´c, S. Juki´c, A. Miljkovi´c, and Z. Mašeti´c. Feature selection us-ing cloud-based parallel genetic algorithm for intrusion detection data classification. Neural Computing and Applications, 33(18):11861–11873, 2021.
[31] S. Sivamohan, S. S. Sridhar, and S. Krishnaveni. Tea-ekho-ids: An intrusion detection system for industrial cps with trustworthy explainable ai and enhanced krill herd optimization. Peer-to-Peer Networking and Appli-cations, 16(4):1993–2021, 2023.
[32] M. Ozkan-Okay, R. Samet, O. Aslan, S. Kosunalp, T. Iliev, and I. Stoyanov. A novel feature selection approach to classify intrusion attacks in network com-munications. Applied Sciences, 13(19):Article 19, 2023.
[33] Y. Pourardebil Khah, M. Hosseini Shirvani, and H. Mo-tameni. A hybrid machine learning approach for feature selection in designing intrusion detection systems (ids) model for distributed computing networks. The Journal of Supercomputing, 81(1):254, 2024.
[34] B. Mallick. Enhancing cyber security: a comprehensive approach to the classification and prediction of signifi-cant cyber incidents (sci) through data mining and vari-ational neural network with fox optimization algorithm. International Journal of Computers and Applications, 46(8):621–627, 2024.
[35] D. K. K. Reddy, J. Nayak, H. S. Behera, V. Shanmu-ganathan, W. Viriyasitavat, and G. Dhiman. A sys-tematic literature review on swarm intelligence based intrusion detection system: Past, present and future. Archives of Computational Methods in Engineering, 31(5):2717–2784, 2024.
[36] K. Thakur and G. Kumar. Nature inspired techniques and applications in intrusion detection systems: Recent progress and updated perspective. Archives of Com-putational Methods in Engineering, 28(4):2897–2919, 2021.
[37] H. Kadry, A. Farouk, E. A. Zanaty, and O. Reyad. Intru-sion detection model using optimized quantum neural network and elliptical curve cryptography for data se-curity. Alexandria Engineering Journal, 71:491–500, 2023.
[38] A. Shanbhag, S. Vincent, S. B. B. Gowda, O. P. Kumar, and S. A. J. Francis. Leveraging metaheuristics for feature selection with machine learning classification for malicious packet detection in computer networks. IEEE Access, 12:21745–21764, 2024.
[39] P. K. Keserwani, Mahesh Chandra Govil, and E. S. Pilli. An optimal intrusion detection system using gwo-csa-dsae model. Cyber-Physical Systems, 7(4):197–220, 2021.
[40] N. Savanovi´c, A. Toskovic, A. Petrovic, M. Zivkovic, R. Damaševiˇcius, L. Jovanovic, N. Bacanin, and B. Nikolic. Intrusion detection in healthcare 4.0 internet of things systems via metaheuristics optimized machine learning. Sustainability, 15(16):Article 16, 2023.
[41] A. Naghib, F. S. Gharehchopogh, and A. Zamanifar.A comprehensive and systematic literature review on intrusion detection systems in the internet of medical things: current status, challenges, and opportunities. Artificial Intelligence Review, 58(4):114, 2025.
[42] A. H. Ali, B. Ammar, M. Charfeddine, and B. B. Hamed. Enhanced intrusion detection based hybrid meta-heuristic feature selection. In Advances in Com-putational Collective Intelligence, page 3–15. Springer Nature Switzerland, 2024.
[43] I. H. Hassan, A. Mohammed, and M. A. Masama. Metaheuristic algorithms in network intrusion detec-tion. In Comprehensive Metaheuristics, page 95–129. Academic Press, 2023.
[44] R. Ghanbarzadeh, A. Hosseinalipour, and A. Ghaf-fari. A novel network intrusion detection method based on metaheuristic optimisation algorithms. Jour-nal of Ambient Intelligence and Humanized Computing, 14(6):7575–7592, 2023.
[45] Vladimir Vapnik, Seymour Golowich, and Alex Smola. Support vector method for function approximation, re-gression estimation and signal processing. In Advances in Neural Information Processing Systems, volume 9, pages 281–287, 1996.
[46] Adele Cutler, David R. Cutler, and Joshua R. Stevens. Random forests. In Machine Learning - ML, volume 45, pages 157–176. Springer, 2011.
[47] Thomas Cover and Peter Hart. Nearest neighbor pat-tern classification. IEEE Transactions on Information Theory, 13(1):21–27, 1967.
[48] David D. Lewis. Naive (bayes) at forty: The indepen-dence assumption in information retrieval. In Claire Nédellec and Céline Rouveirol, editors, Machine Learn-ing: ECML-98, pages 4–15. Springer, 1998.
[49] Lior Rokach and Oded Maimon. Decision trees. In Oded Maimon and Lior Rokach, editors, Data Mining and Knowledge Discovery Handbook, pages 165–192. Springer US, 2005.
[50] Ahmed A. Abdelhamid, Shaimaa K. Towfek, Negin Khodadadi, Abdullah A. Alhussan, Doaa S. Khafaga, Mohamed M. Eid, and Abeer Ibrahim. Waterwheel plant algorithm: A novel metaheuristic optimization method. Processes, 11(5):Article 5, 2023.
[51] Colin Reeves. Genetic algorithms. In Handbook of Metaheuristics, volume 146, pages 109–139. Springer, 2010.
[52] James Kennedy and Russell Eberhart. Particle swarm optimization. In Proceedings of ICNN’95 - Interna-tional Conference on Neural Networks, volume 4, pages 1942–1948, 1995.
[53] Seyedali Mirjalili, Seyed Mohammad Mirjalili, and An-drew Lewis. Grey wolf optimizer. Advances in Engi-neering Software, 69:46–61, 2014.
[54] Noraini Johari, Aishah Zain, Noorhaniza Mustaffa, and Aini Udin. Firefly algorithm for optimization problem. Applied Mechanics and Materials, 421:512–517, 2013.
[55] Rainer Storn and Kenneth Price. Differential evolution -a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11:341–359, 1997.
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