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This paper proposes a Green-Aware Intrusion Detection System (GA-IDS) integrating sustainability metrics with machine learning to balance detection performance and energy efficiency. The framework employs a lightweight convolutional neural network optimized for resource-constrained platforms with dynamic power management and adaptive computational scaling. A novel carbon-conscious scheduling algorithm dynamically adjusts model complexity based on real-time grid carbon intensity. Experimental evaluation on CICIDS2017, NSL-KDD, and ToN-IoT datasets demonstrates 98.7% detection accuracy with 43% energy reduction compared to traditional deep learning-based IDS. The carbon-aware scheduler reduces emissions by 58.7% during high-carbon grid conditions without compromising detection quality. Comprehensive ablation studies validate component contributions, and comparative analysis against ten state-of-the-art approaches confirms GA-IDS achieves the highest Green Efficiency Score (0.0796), demonstrating superior balance between security performance and environmental responsibility.<\/jats:p>","DOI":"10.1371\/journal.pone.0355453","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:37:06Z","timestamp":1792085826000},"page":"e0355453","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Green-aware intrusion detection systems: A sustainability-driven approach to network security"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0736-4283","authenticated-orcid":true,"given":"Abdullah","family":"Alshammari","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, University of Hafr Albatin","place":["Hafar Albatin, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"pone.0355453.ref001","doi-asserted-by":"crossref","unstructured":"Moustafa N, Slay J. UNSW-NB15: A comprehensive data set for network intrusion detection systems. In: Proc. Military Commun. Inf. Syst. Conf. (MilCIS), 2015. 1\u20136. https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"pone.0355453.ref002","doi-asserted-by":"crossref","first-page":"41525","DOI":"10.1109\/ACCESS.2019.2895334","article-title":"Deep learning approach for intelligent intrusion detection system","volume":"7","author":"R Vinayakumar","year":"2019","journal-title":"IEEE Access"},{"key":"pone.0355453.ref003","doi-asserted-by":"crossref","first-page":"107247","DOI":"10.1016\/j.comnet.2020.107247","article-title":"Building an efficient intrusion detection system based on feature selection and ensemble classifier","volume":"174","author":"Y Zhou","year":"2020","journal-title":"Computer Networks"},{"key":"pone.0355453.ref004","doi-asserted-by":"crossref","unstructured":"Zhang H, Wu CQ, Gao S, Wang Z, Xu Y, Liu Y. An Effective Deep Learning Based Scheme for Network Intrusion Detection. In: 2018 24th International Conference on Pattern Recognition (ICPR), 2018. 682\u20137. https:\/\/doi.org\/10.1109\/icpr.2018.8546162","DOI":"10.1109\/ICPR.2018.8546162"},{"issue":"2","key":"pone.0355453.ref005","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","article-title":"A survey of data mining and machine learning methods for cyber security intrusion detection","volume":"18","author":"AL Buczak","year":"2016","journal-title":"IEEE Commun Surv Tutorials"},{"key":"pone.0355453.ref006","doi-asserted-by":"crossref","unstructured":"Apruzzese G, Colajanni M, Ferretti L, Guido A, Marchetti M. On the effectiveness of machine and deep learning for cyber security. 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Four Char DNA Encoding for Anomaly Intrusion Detection System. In: Proceedings of the 2019 5th International Conference on Computer and Technology Applications, 2019. 86\u201392. https:\/\/doi.org\/10.1145\/3323933.3324069","DOI":"10.1145\/3323933.3324069"},{"key":"pone.0355453.ref027","first-page":"8410","article-title":"Matching algorithms for intrusion detection system based on DNA encoding","volume":"96","author":"OF Rashid","year":"2018","journal-title":"J Theor Appl Inf Technol"},{"issue":"3","key":"pone.0355453.ref028","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.1109\/COMST.2019.2896380","article-title":"Network intrusion detection for IoT security based on learning techniques","volume":"21","author":"N Chaabouni","year":"2019","journal-title":"IEEE Commun Surv Tutorials"},{"key":"pone.0355453.ref029","unstructured":"Vaswani N, Shazeer N, Parmar J, Uszkoreit L, Jones AN, Gomez \u0141, et al. Attention is all you need. In: Proc. Adv. Neural Inf. Process. Syst., 2017. 5998\u20136008. https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"pone.0355453.ref030","unstructured":"McMahan E, Moore D, Ramage S, Hampson S, y Arcas BA. Communication-efficient learning of deep networks from decentralized data. In: Proc. Int. Conf. Artif. Intell. Stat. (AISTATS), 2017. 1273\u201382. https:\/\/arxiv.org\/abs\/1602.05629"},{"key":"pone.0355453.ref031","unstructured":"Cai H, Gan C, Wang T, Zhang Z, Han S. Once-for-all: Train one network and specialize it for efficient deployment. In: Proc. Int. Conf. Learn. Representations (ICLR), 2020. https:\/\/arxiv.org\/abs\/1908.09791"},{"key":"pone.0355453.ref032","doi-asserted-by":"crossref","unstructured":"Potluri S, Diedrich C. Accelerated deep neural networks for enhanced Intrusion Detection System. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), 2016. 1\u20138. https:\/\/doi.org\/10.1109\/etfa.2016.7733515","DOI":"10.1109\/ETFA.2016.7733515"},{"key":"pone.0355453.ref033","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","article-title":"A survey of deep neural network architectures and their applications","volume":"234","author":"W Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"pone.0355453.ref034","doi-asserted-by":"crossref","first-page":"109318","DOI":"10.1016\/j.comnet.2022.109318","article-title":"Network intrusion detection based on DNA spatial information","volume":"217","author":"T Hou","year":"2022","journal-title":"Computer Networks"},{"key":"pone.0355453.ref035","doi-asserted-by":"crossref","unstructured":"Hagen LS, d. S. Ilha A, Parizotto R, Gaspary LP. RNA: Automating IDS\/IPS event detection offload into programmable forwarding devices. In: Proc. NOMS 2025 \u2013 IEEE Network Operations and Management Symp., Honolulu, HI, USA, 2025. 1\u20139. https:\/\/doi.org\/10.1109\/NOMS57970.2025.11073692","DOI":"10.1109\/NOMS57970.2025.11073692"},{"issue":"6","key":"pone.0355453.ref036","first-page":"446","article-title":"A study on NSL-KDD dataset for intrusion detection system based on classification algorithms","volume":"4","author":"L Dhanabal","year":"2015","journal-title":"International Journal of Advanced Research in Computer and Communication Engineering"},{"key":"pone.0355453.ref037","unstructured":"Tan M, Le QV. EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proc. Int. Conf. Mach. Learn. (ICML), 2019. 6105\u201314. https:\/\/arxiv.org\/abs\/1905.11946"},{"key":"pone.0355453.ref038","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.aiopen.2022.10.001","article-title":"A survey of transformers","volume":"3","author":"T Lin","year":"2022","journal-title":"AI Open"},{"issue":"5","key":"pone.0355453.ref039","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MIC.2012.110","article-title":"Integrating haptics in web interfaces: State of the art and open issues","volume":"16","author":"S Comai","year":"2012","journal-title":"IEEE Internet Comput"},{"issue":"12","key":"pone.0355453.ref040","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1145\/3381831","article-title":"Green AI","volume":"63","author":"J Schwartz","year":"2020","journal-title":"Commun ACM"},{"key":"pone.0355453.ref041","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 770\u20138. https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"pone.0355453.ref042","doi-asserted-by":"crossref","unstructured":"Xie J, Xie G. Research and Implementation of Virus Infection Detection Mechanism Based on Brute-Force Algorithm. In: 2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT), 2023. 1532\u20136. https:\/\/doi.org\/10.1109\/iccect57938.2023.10140924","DOI":"10.1109\/ICCECT57938.2023.10140924"},{"key":"pone.0355453.ref043","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: Hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), 2021. 9992\u201310002. https:\/\/doi.org\/10.1109\/iccv48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"pone.0355453.ref044","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. https:\/\/doi.org\/arXiv:1412.3555"},{"issue":"7","key":"pone.0355453.ref045","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/app9071392","article-title":"Network intrusion detection based on novel feature selection model and various recurrent neural networks","volume":"9","author":"TTH Le","year":"2019","journal-title":"Appl Sci"},{"key":"pone.0355453.ref046","doi-asserted-by":"crossref","unstructured":"Sharafaldin AH, Lashkari A, Ghorbani AA. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proc. 4th Int. Conf. Information Systems Security and Privacy (ICISSP), Funchal, Portugal, 2018. 108\u201316. https:\/\/www.unb.ca\/cic\/datasets\/ids-2017.html","DOI":"10.5220\/0006639801080116"},{"key":"pone.0355453.ref047","doi-asserted-by":"crossref","unstructured":"Tavallaee E, Bagheri W, Lu A, Ghorbani AA. A Detailed Analysis of the KDD CUP 99 Data Set. In: Proc. IEEE Symp. 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