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Year 2025, Volume: 8 Issue: 2, 223 - 244

Onur Ceran , Erdal Özdoğan , Mevlüt Uysal

https://doi.org/10.35377/saucis...1663435

Abstract

References

  • K. V. V. N. L. Sai Kiran, R. N. K. Devisetty, N. P. Kalyan, K. Mukundini, and R. Karthi, ‘Building an Intrusion Detection System for IoT Environment using Machine Learning Techniques’, Procedia Computer Science, vol. 171, pp. 2372–2379, 2020, doi: 10.1016/j.procs.2020.04.257.
  • G. A. Mukhaini, M. Anbar, S. Manickam, T. A. Al-Amiedy, and A. A. Momani, ‘A systematic literature review of recent lightweight detection approaches leveraging machine and deep learning mechanisms in Internet of Things networks’, Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 1, p. 101866, Jan. 2024, doi: 10.1016/j.jksuci.2023.101866.
  • E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, ‘A Supervised Intrusion Detection System for Smart Home IoT Devices’, IEEE Internet Things J., vol. 6, no. 5, pp. 9042–9053, Oct. 2019, doi: 10.1109/JIOT.2019.2926365.
  • A. Nazir et al., ‘Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets’, Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 10, p. 101820, Dec. 2023, doi: 10.1016/j.jksuci.2023.101820.
  • I. Cvitić, D. Peraković, M. Periša, and B. Gupta, ‘Ensemble machine learning approach for classification of IoT devices in smart home’, Int. J. Mach. Learn. & Cyber., vol. 12, no. 11, pp. 3179–3202, Nov. 2021, doi: 10.1007/s13042-020-01241-0.
  • W. W. Lo, G. Kulatilleke, M. Sarhan, S. Layeghy, and M. Portmann, ‘XG-BoT: An explainable deep graph neural network for botnet detection and forensics’, Internet of Things, vol. 22, p. 100747, Jul. 2023, doi: 10.1016/j.iot.2023.100747.
  • T. Altaf, X. Wang, W. Ni, R. P. Liu, and R. Braun, ‘NE-GConv: A lightweight node edge graph convolutional network for intrusion detection’, Computers & Security, vol. 130, p. 103285, Jul. 2023, doi: 10.1016/j.cose.2023.103285.
  • K. Qian, H. Yang, R. Li, W. Chen, X. Luo, and L. Yin, ‘Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning’, Applied Sciences, vol. 14, no. 4, p. 1615, Feb. 2024, doi: 10.3390/app14041615.
  • S. Bhattacharya et al., ‘A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU’, Electronics, vol. 9, no. 2, p. 219, Jan. 2020, doi: 10.3390/electronics9020219.
  • X. Zhou, W. Liang, W. Li, K. Yan, S. Shimizu, and K. I.-K. Wang, ‘Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System’, IEEE Internet Things J., vol. 9, no. 12, pp. 9310–9319, Jun. 2022, doi: 10.1109/JIOT.2021.3130434.
  • M. A. Jabraeil Jamali, B. Bahrami, A. Heidari, P. Allahverdizadeh, and F. Norouzi, ‘IoT Architecture’, in Towards the Internet of Things, in EAI/Springer Innovations in Communication and Computing. , Cham: Springer International Publishing, 2020, pp. 9–31. doi: 10.1007/978-3-030-18468-1_2.
  • A. Heidari and M. A. Jabraeil Jamali, ‘Internet of Things intrusion detection systems: a comprehensive review and future directions’, Cluster Comput, vol. 26, no. 6, pp. 3753–3780, Dec. 2023, doi: 10.1007/s10586-022-03776-z.
  • Q. Li, L. Sun, B. Tang, H. Lu, J. Du, and X. Yu, ‘Structure Enhancement Network Intrusion Detection Based on Graph Neural Network’, in Computer Supported Cooperative Work and Social Computing, vol. 2344, H. Sun, H. Fan, Y. Gao, X. Wang, D. Liu, B. Du, and T. Lu, Eds., in Communications in Computer and Information Science, vol. 2344. , Singapore: Springer Nature Singapore, 2025, pp. 352–364. doi: 10.1007/978-981-96-2376-1_26.
  • A. S. Ahanger, S. M. Khan, F. Masoodi, and A. O. Salau, ‘Advanced intrusion detection in internet of things using graph attention networks’, Sci Rep, vol. 15, no. 1, p. 9831, Mar. 2025, doi: 10.1038/s41598-025-94624-8.
  • J. Sweeten, A. Takiddin, M. Ismail, S. S. Refaat, and R. Atat, ‘Cyber-Physical GNN-Based Intrusion Detection in Smart Power Grids’, in 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Glasgow, United Kingdom: IEEE, Oct. 2023, pp. 1–6. doi: 10.1109/SmartGridComm57358.2023.10333949.
  • E. Caville, W. W. Lo, S. Layeghy, and M. Portmann, ‘Anomal-E: A self-supervised network intrusion detection system based on graph neural networks’, Knowledge-Based Systems, vol. 258, p. 110030, Dec. 2022, doi: 10.1016/j.knosys.2022.110030.
  • T. Altaf, X. Wang, W. Ni, G. Yu, R. P. Liu, and R. Braun, ‘A new concatenated Multigraph Neural Network for IoT intrusion detection’, Internet of Things, vol. 22, p. 100818, Jul. 2023, doi: 10.1016/j.iot.2023.100818.
  • G. Duan, H. Lv, H. Wang, and G. Feng, ‘Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning’, IEEE Trans.Inform.Forensic Secur., vol. 18, pp. 699–714, 2023, doi: 10.1109/TIFS.2022.3228493.
  • M. Zivkovic, M. Tair, V. K, N. Bacanin, Š. Hubálovský, and P. Trojovský, ‘Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification’, PeerJ Computer Science, vol. 8, p. e956, Apr. 2022, doi: 10.7717/peerj-cs.956.
  • O. H. Abdulganiyu, T. A. Tchakoucht, Y. K. Saheed, and H. A. Ahmed, ‘XIDINTFL-VAE: XGBoost-based intrusion detection of imbalance network traffic via class-wise focal loss variational autoencoder’, J Supercomput, vol. 81, no. 1, p. 16, Jan. 2025, doi: 10.1007/s11227-024-06552-5.
  • Y. Song, H. Li, P. Xu, and D. Liu, ‘A Method of Intrusion Detection Based on WOA‐XGBoost Algorithm’, Discrete Dynamics in Nature and Society, vol. 2022, no. 1, p. 5245622, Jan. 2022, doi: 10.1155/2022/5245622.
  • S. Amaouche, AzidineGuezzaz, S. Benkirane, and MouradeAzrour, ‘IDS-XGbFS: a smart intrusion detection system using XGboostwith recent feature selection for VANET safety’, Cluster Comput, vol. 27, no. 3, pp. 3521–3535, Jun. 2024, doi: 10.1007/s10586-023-04157-w.
  • S. L(y)u, K. Wang, L. Zhang, and B. Wang, ‘Global-local integration for GNN-based anomalous device state detection in industrial control systems’, Expert Systems with Applications, vol. 209, p. 118345, Dec. 2022, doi: 10.1016/j.eswa.2022.118345.
  • Q. Lin et al., ‘Robust Graph Neural Networks via Ensemble Learning’, Mathematics, vol. 10, no. 8, p. 1300, Apr. 2022, doi: 10.3390/math10081300.
  • T. Bilot, N. E. Madhoun, K. A. Agha, and A. Zouaoui, ‘Graph Neural Networks for Intrusion Detection: A Survey’, IEEE Access, vol. 11, pp. 49114–49139, 2023, doi: 10.1109/ACCESS.2023.3275789.
  • T. Chen and C. Guestrin, ‘XGBoost: A Scalable Tree Boosting System’, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA: ACM, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785.
  • E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani, ‘CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment’, Sensors, vol. 23, no. 13, p. 5941, Jun. 2023, doi: 10.3390/s23135941.
  • I. Sharafaldin, A. Habibi Lashkari, and A. A. Ghorbani, ‘Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization’:, in Proceedings of the 4th International Conference on Information Systems Security and Privacy, Funchal, Madeira, Portugal: SCITEPRESS - Science and Technology Publications, 2018, pp. 108–116. doi: 10.5220/0006639801080116.
  • M. Sarhan, S. Layeghy, N. Moustafa, and M. Portmann, ‘NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems’, in Big Data Technologies and Applications, vol. 371, Z. Deze, H. Huang, R. Hou, S. Rho, and N. Chilamkurti, Eds., in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 371. , Cham: Springer International Publishing, 2021, pp. 117–135. doi: 10.1007/978-3-030-72802-1_9.
  • N. Moustafa and J. Slay, ‘The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set’, Information Security Journal: A Global Perspective, vol. 25, no. 1–3, pp. 18–31, Apr. 2016, doi: 10.1080/19393555.2015.1125974.
  • N. Moustafa, G. Creech, and J. Slay, ‘Big Data Analytics for Intrusion Detection System: Statistical Decision-Making Using Finite Dirichlet Mixture Models’, in Data Analytics and Decision Support for Cybersecurity, I. Palomares Carrascosa, H. K. Kalutarage, and Y. Huang, Eds., in Data Analytics. , Cham: Springer International Publishing, 2017, pp. 127–156. doi: 10.1007/978-3-319-59439-2_5.
  • N. Moustafa, J. Slay, and G. Creech, ‘Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks’, IEEE Trans. Big Data, vol. 5, no. 4, pp. 481–494, Dec. 2019, doi: 10.1109/TBDATA.2017.2715166.
  • 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), Canberra, Australia: IEEE, Nov. 2015, pp. 1–6. doi: 10.1109/MilCIS.2015.7348942.
  • S. Dadkhah, E. Carlos Pinto Neto, R. Ferreira, R. Chukwuka Molokwu, S. Sadeghi, and A. Ghorbani, ‘CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security’, Feb. 16, 2024. doi: 10.20944/preprints202402.0898.v1.
  • L. Liu, P. Wang, J. Lin, and L. Liu, ‘Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning’, IEEE Access, vol. 9, pp. 7550–7563, 2021, doi: 10.1109/ACCESS.2020.3048198.
  • K. Polat, ‘A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)’, Neural Comput & Applic, vol. 21, no. 8, pp. 1987–1994, Nov. 2012, doi: 10.1007/s00521-011-0602-x.
  • K. P. N. V. Satya Sree, J. Karthik, C. Niharika, P. V. V. S. Srinivas, N. Ravinder, and C. Prasad, ‘Optimized Conversion of Categorical and Numerical Features in Machine Learning Models’, in 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India: IEEE, Nov. 2021, pp. 294–299. doi: 10.1109/I-SMAC52330.2021.9640967.
  • M. Mazziotta and A. Pareto, ‘Everything you always wanted to know about normalization (but were afraid to ask)’, Rivista Italiana di Economia Demografia e Statistica, pp. 41–52, 2021.
  • T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona, ‘A survey on missing data in machine learning’, J Big Data, vol. 8, no. 1, p. 140, Oct. 2021, doi: 10.1186/s40537-021-00516-9.
  • J. T. Hancock and T. M. Khoshgoftaar, ‘Survey on categorical data for neural networks’, J Big Data, vol. 7, no. 1, p. 28, Dec. 2020, doi: 10.1186/s40537-020-00305-w.
  • J. Huang, Y.-F. Li, and M. Xie, ‘An empirical analysis of data preprocessing for machine learning-based software cost estimation’, Information and Software Technology, vol. 67, pp. 108–127, Nov. 2015, doi: 10.1016/j.infsof.2015.07.004.
  • S. S. Dhaliwal, A.-A. Nahid, and R. Abbas, ‘Effective Intrusion Detection System Using XGBoost’, Information, vol. 9, no. 7, p. 149, Jun. 2018, doi: 10.3390/info9070149.
  • A. Churcher et al., ‘An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks’, Sensors, vol. 21, no. 2, p. 446, Jan. 2021, doi: 10.3390/s21020446.
  • Z. H. Hoo, J. Candlish, and D. Teare, ‘What is an ROC curve?’, Emerg Med J, vol. 34, no. 6, pp. 357–359, Jun. 2017, doi: 10.1136/emermed-2017-206735.
  • C. Marzban, ‘The ROC Curve and the Area under It as Performance Measures’, Weather and Forecasting, vol. 19, no. 6, pp. 1106–1114, Dec. 2004, doi: 10.1175/825.1.
  • Y. Qiu, J. Zhou, M. Khandelwal, H. Yang, P. Yang, and C. Li, ‘Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration’, Engineering with Computers, vol. 38, no. S5, pp. 4145–4162, Dec. 2022, doi: 10.1007/s00366-021-01393-9.
  • Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, ‘Network intrusion detection system: A systematic study of machine learning and deep learning approaches’, Trans Emerging Tel Tech, vol. 32, no. 1, p. e4150, Jan. 2021, doi: 10.1002/ett.4150.
  • N. Tekin, A. Acar, A. Aris, A. S. Uluagac, and V. C. Gungor, ‘Energy consumption of on-device machine learning models for IoT intrusion detection’, Internet of Things, vol. 21, p. 100670, Apr. 2023, doi: 10.1016/j.iot.2022.100670.
  • X. Wang and Q. Wang, ‘RETRACTED ARTICLE: An abnormal traffic detection method using GCN-BiLSTM-Attention in the internet of vehicles environment’, J Wireless Com Network, vol. 2023, no. 1, p. 70, Jul. 2023, doi: 10.1186/s13638-023-02274-z.
  • M. Bacevicius and A. Paulauskaite-Taraseviciene, ‘Machine Learning Algorithms for Raw and Unbalanced Intrusion Detection Data in a Multi-Class Classification Problem’, Applied Sciences, vol. 13, no. 12, p. 7328, Jun. 2023, doi: 10.3390/app13127328.
  • J. Jose and D. V. Jose, ‘Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset’, IJECE, vol. 13, no. 1, p. 1134, Feb. 2023, doi: 10.11591/ijece.v13i1.pp1134-1141.
  • S. R and V. S, ‘An Improving Intrusion Detection Model Based on Novel CNN Technique Using Recent CIC-IDS Datasets’, in 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Bengaluru, India: IEEE, Mar. 2024, pp. 1–6. doi: 10.1109/ICDCOT61034.2024.10515433.
  • A. Thakkar and R. Lohiya, ‘Attack Classification of Imbalanced Intrusion Data for IoT Network Using Ensemble-Learning-Based Deep Neural Network’, IEEE Internet Things J., vol. 10, no. 13, pp. 11888–11895, Jul. 2023, doi: 10.1109/JIOT.2023.3244810.
  • M. I. T. Hussan, G. V. Reddy, P. T. Anitha, A. Kanagaraj, and P. Naresh, ‘DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization’, Cluster Comput, Nov. 2023, doi: 10.1007/s10586-023-04187-4.
  • F. M. Aswad, A. M. S. Ahmed, N. A. M. Alhammadi, B. A. Khalaf, and S. A. Mostafa, ‘Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks’, Journal of Intelligent Systems, vol. 32, no. 1, p. 20220155, Jan. 2023, doi: 10.1515/jisys-2022-0155.
  • S. Wang, W. Xu, and Y. Liu, ‘Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things’, Computer Networks, vol. 235, p. 109982, Nov. 2023, doi: 10.1016/j.comnet.2023.109982.
  • B. Bowen, A. Chennamaneni, A. Goulart, and D. Lin, ‘BLoCNet: a hybrid, dataset-independent intrusion detection system using deep learning’, Int. J. Inf. Secur., vol. 22, no. 4, pp. 893–917, Aug. 2023, doi: 10.1007/s10207-023-00663-5.
  • J. Cui, L. Zong, J. Xie, and M. Tang, ‘A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data’, Appl Intell, vol. 53, no. 1, pp. 272–288, Jan. 2023, doi: 10.1007/s10489-022-03361-2.
  • A. Meliboev, J. Alikhanov, and W. Kim, ‘Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets’, Electronics, vol. 11, no. 4, p. 515, Feb. 2022, doi: 10.3390/electronics11040515.
  • Y. F. Sallam et al., ‘Efficient implementation of image representation, VISUAL GEOMETRY GROUP WITH 19 LAYERS and RESIDUAL NETWORK WITH 152 LAYERS for intrusion detection from UNSW‐NB15 dataset’, Security and Privacy, vol. 6, no. 5, p. e300, Sep. 2023, doi: 10.1002/spy2.300.
  • M. H. Kabir, M. S. Rajib, A. S. M. T. Rahman, Md. M. Rahman, and S. K. Dey, ‘Network Intrusion Detection Using UNSW-NB15 Dataset: Stacking Machine Learning Based Approach’, in 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh: IEEE, Feb. 2022, pp. 1–6. doi: 10.1109/ICAEEE54957.2022.9836404.
  • A. Shehadeh, H. ALTaweel, and A. Qusef, ‘Analysis of Data Mining Techniques on KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets for Intrusion Detection’, in 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates: IEEE, Dec. 2023, pp. 1–6. doi: 10.1109/ACIT58888.2023.10453884.
  • I. Tareq, B. M. Elbagoury, S. El-Regaily, and E.-S. M. El-Horbaty, ‘Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT’, Applied Sciences, vol. 12, no. 19, p. 9572, Sep. 2022, doi: 10.3390/app12199572.
  • A. I. Jony and A. K. B. Arnob, ‘A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset’, J. Edge Comp., vol. 3, no. 1, pp. 28–42, May 2024, doi: 10.55056/jec.648.
  • F. L. Becerra-Suarez, V. A. Tuesta-Monteza, H. I. Mejia-Cabrera, and J. Arcila-Diaz, ‘Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks’, Informatics, vol. 11, no. 2, p. 32, May 2024, doi: 10.3390/informatics11020032.
  • N. V. Patil, C. R. Krishna, and K. Kumar, ‘SSK-DDoS: distributed stream processing framework based classification system for DDoS attacks’, Cluster Comput, vol. 25, no. 2, pp. 1355–1372, Apr. 2022, doi: 10.1007/s10586-022-03538-x.
  • T.-T.-H. Le, R. W. Wardhani, D. S. C. Putranto, U. Jo, and H. Kim, ‘Toward Enhanced Attack Detection and Explanation in Intrusion Detection System-Based IoT Environment Data’, IEEE Access, vol. 11, pp. 131661–131676, 2023, doi: 10.1109/ACCESS.2023.3336678.
  • K. Roshan and A. Zafar, ‘Ensemble adaptive online machine learning in data stream: a case study in cyber intrusion detection system’, Int. j. inf. tecnol., Feb. 2024, doi: 10.1007/s41870-024-01727-y.
  • Y. K. Beshah, S. L. Abebe, and H. M. Melaku, ‘Drift Adaptive Online DDoS Attack Detection Framework for IoT System’, Electronics, vol. 13, no. 6, p. 1004, Mar. 2024, doi: 10.3390/electronics13061004.
  • A. Salehpour, M. A. Balafar, and A. Souri, ‘An optimized intrusion detection system for resource-constrained IoMT environments: enhancing security through efficient feature selection and classification’, J Supercomput, vol. 81, no. 6, p. 783, Apr. 2025, doi: 10.1007/s11227-025-07253-3.
  • N. Sharma and P. G. Shambharkar, ‘Multi-attention DeepCRNN: an efficient and explainable intrusion detection framework for Internet of Medical Things environments’, Knowl Inf Syst, Apr. 2025, doi: 10.1007/s10115-025-02402-9.
  • G. Akar, S. Sahmoud, M. Onat, Ü. Cavusoglu, and E. Malondo, ‘L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT’, IEEE Access, vol. 13, pp. 7002–7013, 2025, doi: 10.1109/ACCESS.2025.3526883.
  • F. G. Abdiwi, ‘Hybrid Machine Learning and Blockchain Technology for Early Detection of Cyberattacks in Healthcare Systems’, IJSSE, vol. 14, no. 6, pp. 1883–1893, Dec. 2024, doi: 10.18280/ijsse.140622.
  • A. Misbah, A. Sebbar, and I. Hafidi, ‘Securing Internet of Medical Things: An Advanced Federated Learning Approach’, ijacsa, vol. 16, no. 2, 2025, doi: 10.14569/IJACSA.2025.01602129.
  • D. Torre, A. Chennamaneni, J. Jo, G. Vyas, and B. Sabrsula, ‘Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical Study’, ACM Trans. Softw. Eng. Methodol., vol. 34, no. 2, pp. 1–48, Feb. 2025, doi: 10.1145/3695998.
  • W. W. Lo, S. Layeghy, M. Sarhan, M. Gallagher, and M. Portmann, ‘E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT’, in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary: IEEE, Apr. 2022, pp. 1–9. doi: 10.1109/NOMS54207.2022.9789878.

Leveraging Graph Neural Networks for IoT Attack Detection

Year 2025, Volume: 8 Issue: 2, 223 - 244

Onur Ceran , Erdal Özdoğan , Mevlüt Uysal

https://doi.org/10.35377/saucis...1663435

Abstract

The widespread adoption of Internet of Things (IoT) devices in multiple sectors has driven technological progress; however, it has simultaneously rendered networks vulnerable to advanced cyber threats. Conventional intrusion detection systems face challenges adjusting to IoT environments' ever-changing and diverse characteristics. To address this challenge, researchers propose a novel hybrid approach combining Graph Neural Networks and XGBoost algorithm for robust intrusion detection in IoT ecosystems. This paper presents a comprehensive methodology for integrating GNNs and XGBoost in IoT intrusion detection and evaluates its effectiveness using diverse datasets. The proposed model preprocesses data by standardization, handling missing values, and encoding categorical features. It leverages GNNs to model spatial dependencies and interactions within IoT networks and utilizes XGBoost to distill complex features for predictive analysis. The late fusion technique combines predictions from both models to enhance overall performance. Experimental results on four datasets, including CICIoT-2023, CICIDS-2017, UNSW-NB15, and IoMT-2024, demonstrate the efficacy of the hybrid model. High accuracy, precision, recall, and AUC values indicate the model's robustness in detecting attacks while minimizing false alarms. The study advances IoT security by introducing synergistic solutions and provides practical insights for implementing intrusion detection systems in real-world IoT environments.

Keywords

GNN IoT IDS XGBoost IPS IoT Security

References

  • K. V. V. N. L. Sai Kiran, R. N. K. Devisetty, N. P. Kalyan, K. Mukundini, and R. Karthi, ‘Building an Intrusion Detection System for IoT Environment using Machine Learning Techniques’, Procedia Computer Science, vol. 171, pp. 2372–2379, 2020, doi: 10.1016/j.procs.2020.04.257.
  • G. A. Mukhaini, M. Anbar, S. Manickam, T. A. Al-Amiedy, and A. A. Momani, ‘A systematic literature review of recent lightweight detection approaches leveraging machine and deep learning mechanisms in Internet of Things networks’, Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 1, p. 101866, Jan. 2024, doi: 10.1016/j.jksuci.2023.101866.
  • E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, ‘A Supervised Intrusion Detection System for Smart Home IoT Devices’, IEEE Internet Things J., vol. 6, no. 5, pp. 9042–9053, Oct. 2019, doi: 10.1109/JIOT.2019.2926365.
  • A. Nazir et al., ‘Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets’, Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 10, p. 101820, Dec. 2023, doi: 10.1016/j.jksuci.2023.101820.
  • I. Cvitić, D. Peraković, M. Periša, and B. Gupta, ‘Ensemble machine learning approach for classification of IoT devices in smart home’, Int. J. Mach. Learn. & Cyber., vol. 12, no. 11, pp. 3179–3202, Nov. 2021, doi: 10.1007/s13042-020-01241-0.
  • W. W. Lo, G. Kulatilleke, M. Sarhan, S. Layeghy, and M. Portmann, ‘XG-BoT: An explainable deep graph neural network for botnet detection and forensics’, Internet of Things, vol. 22, p. 100747, Jul. 2023, doi: 10.1016/j.iot.2023.100747.
  • T. Altaf, X. Wang, W. Ni, R. P. Liu, and R. Braun, ‘NE-GConv: A lightweight node edge graph convolutional network for intrusion detection’, Computers & Security, vol. 130, p. 103285, Jul. 2023, doi: 10.1016/j.cose.2023.103285.
  • K. Qian, H. Yang, R. Li, W. Chen, X. Luo, and L. Yin, ‘Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning’, Applied Sciences, vol. 14, no. 4, p. 1615, Feb. 2024, doi: 10.3390/app14041615.
  • S. Bhattacharya et al., ‘A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU’, Electronics, vol. 9, no. 2, p. 219, Jan. 2020, doi: 10.3390/electronics9020219.
  • X. Zhou, W. Liang, W. Li, K. Yan, S. Shimizu, and K. I.-K. Wang, ‘Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System’, IEEE Internet Things J., vol. 9, no. 12, pp. 9310–9319, Jun. 2022, doi: 10.1109/JIOT.2021.3130434.
  • M. A. Jabraeil Jamali, B. Bahrami, A. Heidari, P. Allahverdizadeh, and F. Norouzi, ‘IoT Architecture’, in Towards the Internet of Things, in EAI/Springer Innovations in Communication and Computing. , Cham: Springer International Publishing, 2020, pp. 9–31. doi: 10.1007/978-3-030-18468-1_2.
  • A. Heidari and M. A. Jabraeil Jamali, ‘Internet of Things intrusion detection systems: a comprehensive review and future directions’, Cluster Comput, vol. 26, no. 6, pp. 3753–3780, Dec. 2023, doi: 10.1007/s10586-022-03776-z.
  • Q. Li, L. Sun, B. Tang, H. Lu, J. Du, and X. Yu, ‘Structure Enhancement Network Intrusion Detection Based on Graph Neural Network’, in Computer Supported Cooperative Work and Social Computing, vol. 2344, H. Sun, H. Fan, Y. Gao, X. Wang, D. Liu, B. Du, and T. Lu, Eds., in Communications in Computer and Information Science, vol. 2344. , Singapore: Springer Nature Singapore, 2025, pp. 352–364. doi: 10.1007/978-981-96-2376-1_26.
  • A. S. Ahanger, S. M. Khan, F. Masoodi, and A. O. Salau, ‘Advanced intrusion detection in internet of things using graph attention networks’, Sci Rep, vol. 15, no. 1, p. 9831, Mar. 2025, doi: 10.1038/s41598-025-94624-8.
  • J. Sweeten, A. Takiddin, M. Ismail, S. S. Refaat, and R. Atat, ‘Cyber-Physical GNN-Based Intrusion Detection in Smart Power Grids’, in 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Glasgow, United Kingdom: IEEE, Oct. 2023, pp. 1–6. doi: 10.1109/SmartGridComm57358.2023.10333949.
  • E. Caville, W. W. Lo, S. Layeghy, and M. Portmann, ‘Anomal-E: A self-supervised network intrusion detection system based on graph neural networks’, Knowledge-Based Systems, vol. 258, p. 110030, Dec. 2022, doi: 10.1016/j.knosys.2022.110030.
  • T. Altaf, X. Wang, W. Ni, G. Yu, R. P. Liu, and R. Braun, ‘A new concatenated Multigraph Neural Network for IoT intrusion detection’, Internet of Things, vol. 22, p. 100818, Jul. 2023, doi: 10.1016/j.iot.2023.100818.
  • G. Duan, H. Lv, H. Wang, and G. Feng, ‘Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning’, IEEE Trans.Inform.Forensic Secur., vol. 18, pp. 699–714, 2023, doi: 10.1109/TIFS.2022.3228493.
  • M. Zivkovic, M. Tair, V. K, N. Bacanin, Š. Hubálovský, and P. Trojovský, ‘Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification’, PeerJ Computer Science, vol. 8, p. e956, Apr. 2022, doi: 10.7717/peerj-cs.956.
  • O. H. Abdulganiyu, T. A. Tchakoucht, Y. K. Saheed, and H. A. Ahmed, ‘XIDINTFL-VAE: XGBoost-based intrusion detection of imbalance network traffic via class-wise focal loss variational autoencoder’, J Supercomput, vol. 81, no. 1, p. 16, Jan. 2025, doi: 10.1007/s11227-024-06552-5.
  • Y. Song, H. Li, P. Xu, and D. Liu, ‘A Method of Intrusion Detection Based on WOA‐XGBoost Algorithm’, Discrete Dynamics in Nature and Society, vol. 2022, no. 1, p. 5245622, Jan. 2022, doi: 10.1155/2022/5245622.
  • S. Amaouche, AzidineGuezzaz, S. Benkirane, and MouradeAzrour, ‘IDS-XGbFS: a smart intrusion detection system using XGboostwith recent feature selection for VANET safety’, Cluster Comput, vol. 27, no. 3, pp. 3521–3535, Jun. 2024, doi: 10.1007/s10586-023-04157-w.
  • S. L(y)u, K. Wang, L. Zhang, and B. Wang, ‘Global-local integration for GNN-based anomalous device state detection in industrial control systems’, Expert Systems with Applications, vol. 209, p. 118345, Dec. 2022, doi: 10.1016/j.eswa.2022.118345.
  • Q. Lin et al., ‘Robust Graph Neural Networks via Ensemble Learning’, Mathematics, vol. 10, no. 8, p. 1300, Apr. 2022, doi: 10.3390/math10081300.
  • T. Bilot, N. E. Madhoun, K. A. Agha, and A. Zouaoui, ‘Graph Neural Networks for Intrusion Detection: A Survey’, IEEE Access, vol. 11, pp. 49114–49139, 2023, doi: 10.1109/ACCESS.2023.3275789.
  • T. Chen and C. Guestrin, ‘XGBoost: A Scalable Tree Boosting System’, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA: ACM, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785.
  • E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani, ‘CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment’, Sensors, vol. 23, no. 13, p. 5941, Jun. 2023, doi: 10.3390/s23135941.
  • I. Sharafaldin, A. Habibi Lashkari, and A. A. Ghorbani, ‘Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization’:, in Proceedings of the 4th International Conference on Information Systems Security and Privacy, Funchal, Madeira, Portugal: SCITEPRESS - Science and Technology Publications, 2018, pp. 108–116. doi: 10.5220/0006639801080116.
  • M. Sarhan, S. Layeghy, N. Moustafa, and M. Portmann, ‘NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems’, in Big Data Technologies and Applications, vol. 371, Z. Deze, H. Huang, R. Hou, S. Rho, and N. Chilamkurti, Eds., in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 371. , Cham: Springer International Publishing, 2021, pp. 117–135. doi: 10.1007/978-3-030-72802-1_9.
  • N. Moustafa and J. Slay, ‘The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set’, Information Security Journal: A Global Perspective, vol. 25, no. 1–3, pp. 18–31, Apr. 2016, doi: 10.1080/19393555.2015.1125974.
  • N. Moustafa, G. Creech, and J. Slay, ‘Big Data Analytics for Intrusion Detection System: Statistical Decision-Making Using Finite Dirichlet Mixture Models’, in Data Analytics and Decision Support for Cybersecurity, I. Palomares Carrascosa, H. K. Kalutarage, and Y. Huang, Eds., in Data Analytics. , Cham: Springer International Publishing, 2017, pp. 127–156. doi: 10.1007/978-3-319-59439-2_5.
  • N. Moustafa, J. Slay, and G. Creech, ‘Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks’, IEEE Trans. Big Data, vol. 5, no. 4, pp. 481–494, Dec. 2019, doi: 10.1109/TBDATA.2017.2715166.
  • 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), Canberra, Australia: IEEE, Nov. 2015, pp. 1–6. doi: 10.1109/MilCIS.2015.7348942.
  • S. Dadkhah, E. Carlos Pinto Neto, R. Ferreira, R. Chukwuka Molokwu, S. Sadeghi, and A. Ghorbani, ‘CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security’, Feb. 16, 2024. doi: 10.20944/preprints202402.0898.v1.
  • L. Liu, P. Wang, J. Lin, and L. Liu, ‘Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning’, IEEE Access, vol. 9, pp. 7550–7563, 2021, doi: 10.1109/ACCESS.2020.3048198.
  • K. Polat, ‘A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)’, Neural Comput & Applic, vol. 21, no. 8, pp. 1987–1994, Nov. 2012, doi: 10.1007/s00521-011-0602-x.
  • K. P. N. V. Satya Sree, J. Karthik, C. Niharika, P. V. V. S. Srinivas, N. Ravinder, and C. Prasad, ‘Optimized Conversion of Categorical and Numerical Features in Machine Learning Models’, in 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India: IEEE, Nov. 2021, pp. 294–299. doi: 10.1109/I-SMAC52330.2021.9640967.
  • M. Mazziotta and A. Pareto, ‘Everything you always wanted to know about normalization (but were afraid to ask)’, Rivista Italiana di Economia Demografia e Statistica, pp. 41–52, 2021.
  • T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona, ‘A survey on missing data in machine learning’, J Big Data, vol. 8, no. 1, p. 140, Oct. 2021, doi: 10.1186/s40537-021-00516-9.
  • J. T. Hancock and T. M. Khoshgoftaar, ‘Survey on categorical data for neural networks’, J Big Data, vol. 7, no. 1, p. 28, Dec. 2020, doi: 10.1186/s40537-020-00305-w.
  • J. Huang, Y.-F. Li, and M. Xie, ‘An empirical analysis of data preprocessing for machine learning-based software cost estimation’, Information and Software Technology, vol. 67, pp. 108–127, Nov. 2015, doi: 10.1016/j.infsof.2015.07.004.
  • S. S. Dhaliwal, A.-A. Nahid, and R. Abbas, ‘Effective Intrusion Detection System Using XGBoost’, Information, vol. 9, no. 7, p. 149, Jun. 2018, doi: 10.3390/info9070149.
  • A. Churcher et al., ‘An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks’, Sensors, vol. 21, no. 2, p. 446, Jan. 2021, doi: 10.3390/s21020446.
  • Z. H. Hoo, J. Candlish, and D. Teare, ‘What is an ROC curve?’, Emerg Med J, vol. 34, no. 6, pp. 357–359, Jun. 2017, doi: 10.1136/emermed-2017-206735.
  • C. Marzban, ‘The ROC Curve and the Area under It as Performance Measures’, Weather and Forecasting, vol. 19, no. 6, pp. 1106–1114, Dec. 2004, doi: 10.1175/825.1.
  • Y. Qiu, J. Zhou, M. Khandelwal, H. Yang, P. Yang, and C. Li, ‘Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration’, Engineering with Computers, vol. 38, no. S5, pp. 4145–4162, Dec. 2022, doi: 10.1007/s00366-021-01393-9.
  • Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, ‘Network intrusion detection system: A systematic study of machine learning and deep learning approaches’, Trans Emerging Tel Tech, vol. 32, no. 1, p. e4150, Jan. 2021, doi: 10.1002/ett.4150.
  • N. Tekin, A. Acar, A. Aris, A. S. Uluagac, and V. C. Gungor, ‘Energy consumption of on-device machine learning models for IoT intrusion detection’, Internet of Things, vol. 21, p. 100670, Apr. 2023, doi: 10.1016/j.iot.2022.100670.
  • X. Wang and Q. Wang, ‘RETRACTED ARTICLE: An abnormal traffic detection method using GCN-BiLSTM-Attention in the internet of vehicles environment’, J Wireless Com Network, vol. 2023, no. 1, p. 70, Jul. 2023, doi: 10.1186/s13638-023-02274-z.
  • M. Bacevicius and A. Paulauskaite-Taraseviciene, ‘Machine Learning Algorithms for Raw and Unbalanced Intrusion Detection Data in a Multi-Class Classification Problem’, Applied Sciences, vol. 13, no. 12, p. 7328, Jun. 2023, doi: 10.3390/app13127328.
  • J. Jose and D. V. Jose, ‘Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset’, IJECE, vol. 13, no. 1, p. 1134, Feb. 2023, doi: 10.11591/ijece.v13i1.pp1134-1141.
  • S. R and V. S, ‘An Improving Intrusion Detection Model Based on Novel CNN Technique Using Recent CIC-IDS Datasets’, in 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Bengaluru, India: IEEE, Mar. 2024, pp. 1–6. doi: 10.1109/ICDCOT61034.2024.10515433.
  • A. Thakkar and R. Lohiya, ‘Attack Classification of Imbalanced Intrusion Data for IoT Network Using Ensemble-Learning-Based Deep Neural Network’, IEEE Internet Things J., vol. 10, no. 13, pp. 11888–11895, Jul. 2023, doi: 10.1109/JIOT.2023.3244810.
  • M. I. T. Hussan, G. V. Reddy, P. T. Anitha, A. Kanagaraj, and P. Naresh, ‘DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization’, Cluster Comput, Nov. 2023, doi: 10.1007/s10586-023-04187-4.
  • F. M. Aswad, A. M. S. Ahmed, N. A. M. Alhammadi, B. A. Khalaf, and S. A. Mostafa, ‘Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks’, Journal of Intelligent Systems, vol. 32, no. 1, p. 20220155, Jan. 2023, doi: 10.1515/jisys-2022-0155.
  • S. Wang, W. Xu, and Y. Liu, ‘Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things’, Computer Networks, vol. 235, p. 109982, Nov. 2023, doi: 10.1016/j.comnet.2023.109982.
  • B. Bowen, A. Chennamaneni, A. Goulart, and D. Lin, ‘BLoCNet: a hybrid, dataset-independent intrusion detection system using deep learning’, Int. J. Inf. Secur., vol. 22, no. 4, pp. 893–917, Aug. 2023, doi: 10.1007/s10207-023-00663-5.
  • J. Cui, L. Zong, J. Xie, and M. Tang, ‘A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data’, Appl Intell, vol. 53, no. 1, pp. 272–288, Jan. 2023, doi: 10.1007/s10489-022-03361-2.
  • A. Meliboev, J. Alikhanov, and W. Kim, ‘Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets’, Electronics, vol. 11, no. 4, p. 515, Feb. 2022, doi: 10.3390/electronics11040515.
  • Y. F. Sallam et al., ‘Efficient implementation of image representation, VISUAL GEOMETRY GROUP WITH 19 LAYERS and RESIDUAL NETWORK WITH 152 LAYERS for intrusion detection from UNSW‐NB15 dataset’, Security and Privacy, vol. 6, no. 5, p. e300, Sep. 2023, doi: 10.1002/spy2.300.
  • M. H. Kabir, M. S. Rajib, A. S. M. T. Rahman, Md. M. Rahman, and S. K. Dey, ‘Network Intrusion Detection Using UNSW-NB15 Dataset: Stacking Machine Learning Based Approach’, in 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh: IEEE, Feb. 2022, pp. 1–6. doi: 10.1109/ICAEEE54957.2022.9836404.
  • A. Shehadeh, H. ALTaweel, and A. Qusef, ‘Analysis of Data Mining Techniques on KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets for Intrusion Detection’, in 2023 24th International Arab Conference on Information Technology (ACIT), Ajman, United Arab Emirates: IEEE, Dec. 2023, pp. 1–6. doi: 10.1109/ACIT58888.2023.10453884.
  • I. Tareq, B. M. Elbagoury, S. El-Regaily, and E.-S. M. El-Horbaty, ‘Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT’, Applied Sciences, vol. 12, no. 19, p. 9572, Sep. 2022, doi: 10.3390/app12199572.
  • A. I. Jony and A. K. B. Arnob, ‘A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset’, J. Edge Comp., vol. 3, no. 1, pp. 28–42, May 2024, doi: 10.55056/jec.648.
  • F. L. Becerra-Suarez, V. A. Tuesta-Monteza, H. I. Mejia-Cabrera, and J. Arcila-Diaz, ‘Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks’, Informatics, vol. 11, no. 2, p. 32, May 2024, doi: 10.3390/informatics11020032.
  • N. V. Patil, C. R. Krishna, and K. Kumar, ‘SSK-DDoS: distributed stream processing framework based classification system for DDoS attacks’, Cluster Comput, vol. 25, no. 2, pp. 1355–1372, Apr. 2022, doi: 10.1007/s10586-022-03538-x.
  • T.-T.-H. Le, R. W. Wardhani, D. S. C. Putranto, U. Jo, and H. Kim, ‘Toward Enhanced Attack Detection and Explanation in Intrusion Detection System-Based IoT Environment Data’, IEEE Access, vol. 11, pp. 131661–131676, 2023, doi: 10.1109/ACCESS.2023.3336678.
  • K. Roshan and A. Zafar, ‘Ensemble adaptive online machine learning in data stream: a case study in cyber intrusion detection system’, Int. j. inf. tecnol., Feb. 2024, doi: 10.1007/s41870-024-01727-y.
  • Y. K. Beshah, S. L. Abebe, and H. M. Melaku, ‘Drift Adaptive Online DDoS Attack Detection Framework for IoT System’, Electronics, vol. 13, no. 6, p. 1004, Mar. 2024, doi: 10.3390/electronics13061004.
  • A. Salehpour, M. A. Balafar, and A. Souri, ‘An optimized intrusion detection system for resource-constrained IoMT environments: enhancing security through efficient feature selection and classification’, J Supercomput, vol. 81, no. 6, p. 783, Apr. 2025, doi: 10.1007/s11227-025-07253-3.
  • N. Sharma and P. G. Shambharkar, ‘Multi-attention DeepCRNN: an efficient and explainable intrusion detection framework for Internet of Medical Things environments’, Knowl Inf Syst, Apr. 2025, doi: 10.1007/s10115-025-02402-9.
  • G. Akar, S. Sahmoud, M. Onat, Ü. Cavusoglu, and E. Malondo, ‘L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT’, IEEE Access, vol. 13, pp. 7002–7013, 2025, doi: 10.1109/ACCESS.2025.3526883.
  • F. G. Abdiwi, ‘Hybrid Machine Learning and Blockchain Technology for Early Detection of Cyberattacks in Healthcare Systems’, IJSSE, vol. 14, no. 6, pp. 1883–1893, Dec. 2024, doi: 10.18280/ijsse.140622.
  • A. Misbah, A. Sebbar, and I. Hafidi, ‘Securing Internet of Medical Things: An Advanced Federated Learning Approach’, ijacsa, vol. 16, no. 2, 2025, doi: 10.14569/IJACSA.2025.01602129.
  • D. Torre, A. Chennamaneni, J. Jo, G. Vyas, and B. Sabrsula, ‘Toward Enhancing Privacy Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical Study’, ACM Trans. Softw. Eng. Methodol., vol. 34, no. 2, pp. 1–48, Feb. 2025, doi: 10.1145/3695998.
  • W. W. Lo, S. Layeghy, M. Sarhan, M. Gallagher, and M. Portmann, ‘E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT’, in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary: IEEE, Apr. 2022, pp. 1–9. doi: 10.1109/NOMS54207.2022.9789878.
There are 76 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Onur Ceran 0000-0003-2147-0506

Erdal Özdoğan 0000-0002-3339-0493

Mevlüt Uysal 0000-0002-6934-4421

Early Pub Date June 16, 2025
Publication Date
Submission Date March 22, 2025
Acceptance Date May 28, 2025
Published in Issue Year 2025Volume: 8 Issue: 2

Cite

APA Ceran, O., Özdoğan, E., & Uysal, M. (2025). Leveraging Graph Neural Networks for IoT Attack Detection. Sakarya University Journal of Computer and Information Sciences, 8(2), 223-244. https://doi.org/10.35377/saucis...1663435
AMA Ceran O, Özdoğan E, Uysal M. Leveraging Graph Neural Networks for IoT Attack Detection. SAUCIS. June 2025;8(2):223-244. doi:10.35377/saucis.1663435
Chicago Ceran, Onur, Erdal Özdoğan, and Mevlüt Uysal. “Leveraging Graph Neural Networks for IoT Attack Detection”. Sakarya University Journal of Computer and Information Sciences 8, no. 2 (June 2025): 223-44. https://doi.org/10.35377/saucis. 1663435.
EndNote Ceran O, Özdoğan E, Uysal M (June 1, 2025) Leveraging Graph Neural Networks for IoT Attack Detection. Sakarya University Journal of Computer and Information Sciences 8 2 223–244.
IEEE O. Ceran, E. Özdoğan, and M. Uysal, “Leveraging Graph Neural Networks for IoT Attack Detection”, SAUCIS, vol. 8, no. 2, pp. 223–244, 2025, doi: 10.35377/saucis...1663435.
ISNAD Ceran, Onur et al. “Leveraging Graph Neural Networks for IoT Attack Detection”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 2025), 223-244. https://doi.org/10.35377/saucis. 1663435.
JAMA Ceran O, Özdoğan E, Uysal M. Leveraging Graph Neural Networks for IoT Attack Detection. SAUCIS. 2025;8:223–244.
MLA Ceran, Onur et al. “Leveraging Graph Neural Networks for IoT Attack Detection”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, 2025, pp. 223-44, doi:10.35377/saucis. 1663435.
Vancouver Ceran O, Özdoğan E, Uysal M. Leveraging Graph Neural Networks for IoT Attack Detection. SAUCIS. 2025;8(2):223-44.
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