This work is licensed under the Creative Commons Attribution 4.0 International License.
Ahmed, M., & Mahmood, A. N. (2023). A survey on machine learning-based intrusion detection techniques for IoT. Journal of Computing and Security, 29(1), 1–20. https://doi.org/10.1016/j.jocs.2023.101951.AhmedM.MahmoodA. N.2023A survey on machine learning-based intrusion detection techniques for IoTJournal of Computing and Security291120https://doi.org/10.1016/j.jocs.2023.101951Search in Google Scholar
Arshad, S., & Zubair, A. (2022). Hybrid deep learning models for efficient intrusion detection in IoT networks. Journal of Network and Computer Applications, 144, 102795. https://doi.org/10.1016/j.jnca.2021.102795.ArshadS.ZubairA.2022Hybrid deep learning models for efficient intrusion detection in IoT networksJournal of Network and Computer Applications144102795https://doi.org/10.1016/j.jnca.2021.102795Search in Google Scholar
Al-Maadeed, S., & Khamis, A. (2022). Enhancing security in IoT networks using hybrid machine learning algorithms. Journal of Internet of Things and Cyber-Physical Systems, 10(3), 104–113. https://doi.org/10.1007/s42152-022-00260-0.Al-MaadeedS.KhamisA.2022Enhancing security in IoT networks using hybrid machine learning algorithmsJournal of Internet of Things and Cyber-Physical Systems103104113https://doi.org/10.1007/s42152-022-00260-0Search in Google Scholar
Yadav, A., & Sharma, S. (2023). A hybrid deep learning model for intrusion detection in IoT networks: A comparative study. Future Generation Computer Systems, 118, 85–96. https://doi.org/10.1016/j.future.2021.10.041.YadavA.SharmaS.2023A hybrid deep learning model for intrusion detection in IoT networks: A comparative studyFuture Generation Computer Systems1188596https://doi.org/10.1016/j.future.2021.10.041Search in Google Scholar
Li, S., & Zhang, Z. (2023). A novel hybrid intrusion detection approach for IoT systems using deep learning and feature selection. Journal of Sensors, 23(8), 2769. https://doi.org/10.3390/s23082769.LiS.ZhangZ.2023A novel hybrid intrusion detection approach for IoT systems using deep learning and feature selectionJournal of Sensors2382769https://doi.org/10.3390/s23082769Search in Google Scholar
Zhang, H., & Zhang, Y. (2023). Hybrid feature selection and deep learning for intrusion detection in IoT networks. Computers, Materials & Continua, 71(2), 2199–2214. https://doi.org/10.32604/cmc.2023.017114.ZhangH.ZhangY.2023Hybrid feature selection and deep learning for intrusion detection in IoT networksComputers, Materials & Continua71221992214https://doi.org/10.32604/cmc.2023.017114Search in Google Scholar
Rani, R., & Meenakshi, S. (2022). An IoT-based hybrid intrusion detection system using ensemble machine learning models. Computers & Security, 108, 102384. https://doi.org/10.1016/j.cose.2021.102384.RaniR.MeenakshiS.2022An IoT-based hybrid intrusion detection system using ensemble machine learning modelsComputers & Security108102384https://doi.org/10.1016/j.cose.2021.102384Search in Google Scholar
Hasan, M., & Kaur, M. (2023). A hybrid deep learning framework for efficient anomaly detection in IoT networks. Artificial Intelligence in Medicine, 138, 102007. https://doi.org/10.1016/j.artmed.2023.102007.HasanM.KaurM.2023A hybrid deep learning framework for efficient anomaly detection in IoT networksArtificial Intelligence in Medicine138102007https://doi.org/10.1016/j.artmed.2023.102007Search in Google Scholar
Liu, Y., & Jin, Y. (2022). IoT security enhancement using hybrid deep neural network-based intrusion detection. International Journal of Security and Networks, 17(5), 349–360. https://doi.org/10.1504/IJSN.2022.120399.LiuY.JinY.2022IoT security enhancement using hybrid deep neural network-based intrusion detectionInternational Journal of Security and Networks175349360https://doi.org/10.1504/IJSN.2022.120399Search in Google Scholar
Liu, S., & Wang, X. (2022). A hybrid deep learning approach to intrusion detection in IoT networks. International Journal of Computer Science and Information Security, 20(3), 124–133.LiuS.WangX.2022A hybrid deep learning approach to intrusion detection in IoT networksInternational Journal of Computer Science and Information Security203124133Search in Google Scholar
Shahid, Usama & Hussain, Muhammad Zunnurain & Hasan, Muhammad Zulkifl & Haider, Ali & Ali, Jibran & Altaf, Jawad. (2024). Hybrid Intrusion Detection System for RPL IoT Networks Using Machine Learning and Deep Learning. IEEE Access. PP. 1–1. 10.1109/ACCESS.2024.3442529.ShahidUsamaHussainMuhammad ZunnurainHasanMuhammad ZulkiflHaiderAliAliJibranAltafJawad2024Hybrid Intrusion Detection System for RPL IoT Networks Using Machine Learning and Deep LearningIEEE Access1110.1109/ACCESS.2024.3442529Open DOISearch in Google Scholar
Yaras, S., & Dener, M. (2024). IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm. Electronics, 13(6), 1053. https://doi.org/10.3390/electronics13061053.YarasS.DenerM.2024IoT-Based Intrusion Detection System Using New Hybrid Deep Learning AlgorithmElectronics1361053https://doi.org/10.3390/electronics13061053Search in Google Scholar
Sajid, M., Malik, K.R., Almogren, A. et al. Enhancing intrusion detection: a hybrid machine and deep learning approach. J Cloud Comp 13, 123 (2024). https://doi.org/10.1186/s13677-024-00685-x.SajidM.MalikK.R.AlmogrenA.Enhancing intrusion detection: a hybrid machine and deep learning approachJ Cloud Comp131232024https://doi.org/10.1186/s13677-024-00685-xSearch in Google Scholar
Almotairi, A., Atawneh, S., Khashan, O. A., & Khafajah, N. M. (2024). Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models. Systems Science & Control Engineering, 12(1). https://doi.org/10.1080/21642583.2024.2321381.AlmotairiA.AtawnehS.KhashanO. A.KhafajahN. M.2024Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble modelsSystems Science & Control Engineering121https://doi.org/10.1080/21642583.2024.2321381Search in Google Scholar
Walling, S., & Lodh, S. (2024). Network intrusion detection system for IoT security using machine learning and statistical-based hybrid feature selection. Security and Privacy, e429. https://doi.org/10.1002/spy2.429.WallingS.LodhS.2024Network intrusion detection system for IoT security using machine learning and statistical-based hybrid feature selectionSecurity and Privacye429https://doi.org/10.1002/spy2.429Search in Google Scholar
Meliboyev, A. (2024). IoT network intrusion detection system using machine learning techniques. Qoqon Universitesi Xabarnomasi, 11, 112–115. https://doi.org/10.54613/ku.v11i11.972.MeliboyevA.2024IoT network intrusion detection system using machine learning techniquesQoqon Universitesi Xabarnomasi11112115https://doi.org/10.54613/ku.v11i11.972Search in Google Scholar
Al Sawafi, Y., Touzene, A., & Hedjam, R. (2023). Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks. Journal of Sensor and Actuator Networks, 12(2), 21. https://doi.org/10.3390/jsan12020021.Al SawafiY.TouzeneA.HedjamR.2023Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT NetworksJournal of Sensor and Actuator Networks12221https://doi.org/10.3390/jsan12020021Search in Google Scholar
Awajan, A. A Novel Deep Learning-Based Intrusion Detection System for IoT Networks. Computers 2023, 12, 34. https://doi.org/10.3390/computers12020034.AwajanA.A Novel Deep Learning-Based Intrusion Detection System for IoT NetworksComputers20231234https://doi.org/10.3390/computers12020034Search in Google Scholar
Singh, A., Chatterjee, K. & Satapathy, S.C. An edge based hybrid intrusion detection framework for mobile edge computing. Complex Intell. Syst. 8, 3719–3746 (2022). https://doi.org/10.1007/s40747-021-00498-4.SinghA.ChatterjeeK.SatapathyS.C.An edge based hybrid intrusion detection framework for mobile edge computingComplex Intell. Syst.8371937462022https://doi.org/10.1007/s40747-021-00498-4Search in Google Scholar
Smys, Smys & Basar, Dr & Wang, Dr. (2020). Hybrid Intrusion Detection System for Internet of Things (IoT). Journal of ISMAC. 2. 190–199. https://doi.org/10.36548/jismac.2020.4.002.SmysSmysBasarDrWangDr.2020Hybrid Intrusion Detection System for Internet of Things (IoT)Journal of ISMAC2190199https://doi.org/10.36548/jismac.2020.4.002Search in Google Scholar
Kharel, P., & Bhattarai, P. (2022). Hybrid feature selection and classification for intrusion detection in IoT networks. Information Sciences, 603, 46–59. https://doi.org/10.1016/j.ins.2022.04.016.KharelP.BhattaraiP.2022Hybrid feature selection and classification for intrusion detection in IoT networksInformation Sciences6034659https://doi.org/10.1016/j.ins.2022.04.016Search in Google Scholar
Ahmed, S., & Raza, M. (2023). Secure IoT network design using hybrid machine learning-based intrusion detection. Computer Networks, 212, 108196. https://doi.org/10.1016/j.comnet.2023.108196.AhmedS.RazaM.2023Secure IoT network design using hybrid machine learning-based intrusion detectionComputer Networks212108196https://doi.org/10.1016/j.comnet.2023.108196Search in Google Scholar
Kaur, P., & Sharma, D. (2023). Deep learning-based hybrid intrusion detection model for secure IoT communication. Journal of Network Security, 33(5), 94–105. https://doi.org/10.1016/j.jns.2023.01.002.KaurP.SharmaD.2023Deep learning-based hybrid intrusion detection model for secure IoT communicationJournal of Network Security33594105https://doi.org/10.1016/j.jns.2023.01.002Search in Google Scholar
Singh, G., & Kumar, S. (2023). A hybrid IoT intrusion detection system using genetic algorithm and machine learning classifiers. Journal of Intelligent Systems, 32(4), 553–567. https://doi.org/10.1515/jisys-2023-0426.SinghG.KumarS.2023A hybrid IoT intrusion detection system using genetic algorithm and machine learning classifiersJournal of Intelligent Systems324553567https://doi.org/10.1515/jisys-2023-0426Search in Google Scholar
Kumar, S., & Saha, S. (2022). Hybrid model for intrusion detection in IoT networks using a combination of deep learning and random forests. Journal of Machine Learning Research, 23(1), 54–67.KumarS.SahaS.2022Hybrid model for intrusion detection in IoT networks using a combination of deep learning and random forestsJournal of Machine Learning Research2315467Search in Google Scholar
Dinesh, P., & Reddy, S. (2023). Anomaly-based intrusion detection using hybrid machine learning for IoT. Security and Privacy, 6(2), e433. https://doi.org/10.1002/spy2.433.DineshP.ReddyS.2023Anomaly-based intrusion detection using hybrid machine learning for IoTSecurity and Privacy62e433https://doi.org/10.1002/spy2.433Search in Google Scholar
Wang, C., & Yao, L. (2022). A hybrid IoT intrusion detection framework using convolutional neural networks and long short-term memory networks. IEEE Transactions on Industrial Informatics, 18(7), 4525–4532. https://doi.org/10.1109/TII.2022.3156572.WangC.YaoL.2022A hybrid IoT intrusion detection framework using convolutional neural networks and long short-term memory networksIEEE Transactions on Industrial Informatics18745254532https://doi.org/10.1109/TII.2022.3156572Search in Google Scholar
Khalil, M., & Gohar, M. (2022). Hybrid deep neural network-based intrusion detection system for IoT security. Computers & Security, 111, 102420. https://doi.org/10.1016/j.cose.2021.102420.KhalilM.GoharM.2022Hybrid deep neural network-based intrusion detection system for IoT securityComputers & Security111102420https://doi.org/10.1016/j.cose.2021.102420Search in Google Scholar
Hossain, M. I., & Karim, R. (2023). A novel hybrid approach for intrusion detection in IoT using deep learning and feature selection. Journal of Cyber Security Technology, 7(2), 87–102. https://doi.org/10.1080/23742917.2023.1911781.HossainM. I.KarimR.2023A novel hybrid approach for intrusion detection in IoT using deep learning and feature selectionJournal of Cyber Security Technology7287102https://doi.org/10.1080/23742917.2023.1911781Search in Google Scholar
Thakur, R., & Arora, A. (2023). Hybrid feature selection and deep learning for intrusion detection in Internet of Things. Applied Soft Computing, 119, 108521. https://doi.org/10.1016/j.asoc.2022.108521.ThakurR.AroraA.2023Hybrid feature selection and deep learning for intrusion detection in Internet of ThingsApplied Soft Computing119108521https://doi.org/10.1016/j.asoc.2022.108521Search in Google Scholar