Smart deep learning model for enhanced IoT intrusion detection

Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated cyberattacks. Machine learning-based anomaly detection has the potential to be a viable solution through abnormal network traffic behavior identi...

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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 20577 - 23
Hlavný autor:	Alsubaei, Faisal S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 01.07.2025 Nature Portfolio
Predmet:	639/166 639/705 692/1807 Cyber security Humanities and Social Sciences Internet of things (IoT) Intrusion detection Intrusion detection systems (IDS) multidisciplinary Optimized sequential neural network Optimized XGBoost Science Science (multidisciplinary) Intrusion detection systems (IDS) Optimized sequential neural network Optimized XGBoost Cyber security Intrusion detection Internet of things (IoT)
ISSN:	2045-2322, 2045-2322
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Abstract	Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated cyberattacks. Machine learning-based anomaly detection has the potential to be a viable solution through abnormal network traffic behavior identification that foretells intrusions. Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. These deep models were then augmented with a variety of various filters, kernels, activation functions, and regularization techniques in an attempt to boost them in detecting complex, multiclass intrusion patterns. The proposed system was tested comprehensively on three challenging datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The optimized XGBoost model worked exceptionally well on the NSL-KDD dataset with very high accuracy (99.93%), F1-score (99.84%), MCC (99.86%), and a very low FPR (0.0004). The optimized SNN model also performed well on the NSL-KDD dataset with an accuracy of 99.0% and an AUC of 1.00. Also, the OSNN model performed very well on UNSW-NB15 dataset with an accuracy of 96.80% and a loss of 0.0777, as well as on the CICIDS-2017 dataset with an accuracy of 99.53% and a loss of 0.0236. This superb performance of the OSNN model can be explained by the careful optimization of hyperparameters like strong activation functions (ReLU, GeLU, LeakyReLU), learning rates, dropout rates, and regularization techniques that enable it to learn intricate intrusion patterns efficiently using various datasets. These results highlight the potential of our proposed method to enhance intrusion detection, system integrity, fraud prevention, and ultimately optimize overall network performance.
AbstractList	Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated cyberattacks. Machine learning-based anomaly detection has the potential to be a viable solution through abnormal network traffic behavior identification that foretells intrusions. Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. These deep models were then augmented with a variety of various filters, kernels, activation functions, and regularization techniques in an attempt to boost them in detecting complex, multiclass intrusion patterns. The proposed system was tested comprehensively on three challenging datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The optimized XGBoost model worked exceptionally well on the NSL-KDD dataset with very high accuracy (99.93%), F1-score (99.84%), MCC (99.86%), and a very low FPR (0.0004). The optimized SNN model also performed well on the NSL-KDD dataset with an accuracy of 99.0% and an AUC of 1.00. Also, the OSNN model performed very well on UNSW-NB15 dataset with an accuracy of 96.80% and a loss of 0.0777, as well as on the CICIDS-2017 dataset with an accuracy of 99.53% and a loss of 0.0236. This superb performance of the OSNN model can be explained by the careful optimization of hyperparameters like strong activation functions (ReLU, GeLU, LeakyReLU), learning rates, dropout rates, and regularization techniques that enable it to learn intricate intrusion patterns efficiently using various datasets. These results highlight the potential of our proposed method to enhance intrusion detection, system integrity, fraud prevention, and ultimately optimize overall network performance.Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated cyberattacks. Machine learning-based anomaly detection has the potential to be a viable solution through abnormal network traffic behavior identification that foretells intrusions. Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. These deep models were then augmented with a variety of various filters, kernels, activation functions, and regularization techniques in an attempt to boost them in detecting complex, multiclass intrusion patterns. The proposed system was tested comprehensively on three challenging datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The optimized XGBoost model worked exceptionally well on the NSL-KDD dataset with very high accuracy (99.93%), F1-score (99.84%), MCC (99.86%), and a very low FPR (0.0004). The optimized SNN model also performed well on the NSL-KDD dataset with an accuracy of 99.0% and an AUC of 1.00. Also, the OSNN model performed very well on UNSW-NB15 dataset with an accuracy of 96.80% and a loss of 0.0777, as well as on the CICIDS-2017 dataset with an accuracy of 99.53% and a loss of 0.0236. This superb performance of the OSNN model can be explained by the careful optimization of hyperparameters like strong activation functions (ReLU, GeLU, LeakyReLU), learning rates, dropout rates, and regularization techniques that enable it to learn intricate intrusion patterns efficiently using various datasets. These results highlight the potential of our proposed method to enhance intrusion detection, system integrity, fraud prevention, and ultimately optimize overall network performance. Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated cyberattacks. Machine learning-based anomaly detection has the potential to be a viable solution through abnormal network traffic behavior identification that foretells intrusions. Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. These deep models were then augmented with a variety of various filters, kernels, activation functions, and regularization techniques in an attempt to boost them in detecting complex, multiclass intrusion patterns. The proposed system was tested comprehensively on three challenging datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The optimized XGBoost model worked exceptionally well on the NSL-KDD dataset with very high accuracy (99.93%), F1-score (99.84%), MCC (99.86%), and a very low FPR (0.0004). The optimized SNN model also performed well on the NSL-KDD dataset with an accuracy of 99.0% and an AUC of 1.00. Also, the OSNN model performed very well on UNSW-NB15 dataset with an accuracy of 96.80% and a loss of 0.0777, as well as on the CICIDS-2017 dataset with an accuracy of 99.53% and a loss of 0.0236. This superb performance of the OSNN model can be explained by the careful optimization of hyperparameters like strong activation functions (ReLU, GeLU, LeakyReLU), learning rates, dropout rates, and regularization techniques that enable it to learn intricate intrusion patterns efficiently using various datasets. These results highlight the potential of our proposed method to enhance intrusion detection, system integrity, fraud prevention, and ultimately optimize overall network performance. Abstract Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated cyberattacks. Machine learning-based anomaly detection has the potential to be a viable solution through abnormal network traffic behavior identification that foretells intrusions. Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. These deep models were then augmented with a variety of various filters, kernels, activation functions, and regularization techniques in an attempt to boost them in detecting complex, multiclass intrusion patterns. The proposed system was tested comprehensively on three challenging datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The optimized XGBoost model worked exceptionally well on the NSL-KDD dataset with very high accuracy (99.93%), F1-score (99.84%), MCC (99.86%), and a very low FPR (0.0004). The optimized SNN model also performed well on the NSL-KDD dataset with an accuracy of 99.0% and an AUC of 1.00. Also, the OSNN model performed very well on UNSW-NB15 dataset with an accuracy of 96.80% and a loss of 0.0777, as well as on the CICIDS-2017 dataset with an accuracy of 99.53% and a loss of 0.0236. This superb performance of the OSNN model can be explained by the careful optimization of hyperparameters like strong activation functions (ReLU, GeLU, LeakyReLU), learning rates, dropout rates, and regularization techniques that enable it to learn intricate intrusion patterns efficiently using various datasets. These results highlight the potential of our proposed method to enhance intrusion detection, system integrity, fraud prevention, and ultimately optimize overall network performance.
ArticleNumber	20577
Author	Alsubaei, Faisal S.
Author_xml	– sequence: 1 givenname: Faisal S. surname: Alsubaei fullname: Alsubaei, Faisal S. email: fsalsubaei@uj.edu.sa organization: Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah
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Cites_doi	10.1109/ACCESS.2021.3119621 10.1007/s00366-021-01393-9 10.1109/INMIC50486.2020.9318106 10.1109/ACCESS.2022.3182333 10.1007/s00500-015-1942-8 10.1038/s41583-023-00705-w 10.1109/SIU.2016.7496029 10.1109/SURV.2010.032210.00054 10.1016/j.teler.2024.100130 10.1016/j.scs.2020.102275 10.1016/j.cose.2020.101752 10.1007/s13198-021-01558-1 10.1109/CISDA.2009.5356528 10.3390/electronics10151854 10.1007/s11042-023-17372-9 10.1109/eIT53891.2022.9813983 10.3390/info10110356 10.1016/j.phycom.2015.11.004 10.1080/01495933.2023.2236489 10.1109/ACCESS.2017.2762418 10.1016/j.sciaf.2020.e00497 10.1109/PST52912.2021.9647828 10.1080/17455030.2023.2189485 10.1109/ACCESS.2020.3034015 10.1080/09599916.2022.2070525 10.1007/978-3-030-70569-5_18 10.3390/app12010136 10.1016/j.petrol.2021.109520 10.1049/iet-ifs.2019.0294 10.1007/978-3-030-27192-3_9 10.1109/ICAML54311.2021.00019 10.1109/ACCESS.2021.3057654 10.1016/j.cose.2021.102585 10.3390/s23020890 10.1609/aaai.v30i1.10287 10.1007/s11276-020-02321-3 10.1016/j.vehcom.2022.100471 10.1016/j.procs.2024.04.072 10.1080/25751654.2021.1918932 10.1109/SURV.2013.102913.00020 10.1109/ACCESS.2020.2972627 10.1016/j.compeleceng.2023.108731 10.1016/j.eswa.2023.122181 10.3390/info12020050 10.32604/cmes.2022.020724 10.1016/j.cose.2025.104417 10.1007/s41870-022-01115-4 10.1007/s13042-024-02465-0 10.1007/s00521-017-3128-z 10.1109/TNSM.2020.3032618 10.1007/978-3-030-04212-7_53 10.3390/sym15030568 10.32628/CSEIT195215 10.1016/j.compeleceng.2022.108410 10.1016/j.dss.2024.114351 10.1016/j.jksuci.2020.10.013 10.32604/cmc.2023.032617 10.1007/978-3-030-81462-5_26 10.1109/ICASID.2019.8925239 10.1109/ACCESS.2022.3221400 10.1016/j.procs.2020.03.438 10.1016/j.comnet.2020.107247 10.1109/ISCAS.2019.8702583 10.1109/ACCESS.2019.2923640 10.1109/JIOT.2021.3060878 10.1016/j.energy.2022.126174 10.1007/978-3-031-54547-4_8 10.1016/j.eij.2025.100666 10.1080/08839514.2023.2166222 10.1007/s11277-022-10155-9 10.1002/ett.4150 10.1109/TPAMI.2021.3117837 10.1016/j.iot.2025.101519 10.1016/j.sciaf.2020.e00500 10.1007/978-981-16-8664-1_30 10.1109/ACCESS.2023.3266979 10.1016/B978-0-12-810408-8.00016-X 10.1007/978-3-030-04503-6_14 10.1109/ACCESS.2019.2928048 10.1109/ACCESS.2022.3190416 10.1109/WINCOM.2016.7777224 10.1016/j.seta.2022.102852 10.3389/fncel.2023.1220030 10.1109/ACCESS.2019.2905633 10.1109/BigDataSecurity-HPSC-IDS.2019.00062 10.1007/978-3-030-06158-6_9 10.1016/j.comcom.2022.12.010 10.1109/JPROC.2021.3067593 10.1109/ICC45855.2022.9838780 10.1109/AIC57670.2023.10263974 10.1038/s41598-018-27169-8 10.32604/cmc.2022.019127 10.1080/17455030.2022.2091807 10.36548/jismac.2020.4.002 10.1109/LCN48667.2020.9314858 10.14569/IJACSA.2020.0110670 10.1016/j.cose.2024.104109 10.1007/s00500-020-04963-z 10.2174/1872212112666180402122150 10.1109/LSENS.2018.2879990 10.1080/14751798.2020.1857911 10.1109/JIOT.2020.2996590 10.3390/sym13101764 10.1109/MIS.2014.92 10.1109/ICSSIT48917.2020.9214206 10.1029/2018WR022643 10.1016/j.neunet.2020.08.001
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References	B Kaur (6363_CR72) 2023 N AL-Nomasy (6363_CR47) 2025; 154 W-C Shi (6363_CR46) 2020; 24 Y Zhou (6363_CR53) 2020; 174 S Iqbal (6363_CR86) 2021; 9 M Abdel-Basset (6363_CR32) 2021; 8 Z Wu (6363_CR30) 2022; 10 M Sajid Farooq (6363_CR9) 2023; 74 P Kumar (6363_CR49) 2025; 148 M Davies (6363_CR78) 2021; 109 CU Om Kumar (6363_CR43) 2023; 129 Z Gholami Doborjeh (6363_CR76) 2018; 8 H Bostani (6363_CR108) 2017; 21 6363_CR48 S Shaheen (6363_CR73) 2021; 4 6363_CR45 T Kim (6363_CR92) 2022; 10 EH Houssein (6363_CR77) 2021; 9 FH Almasoud y (6363_CR109) 2020; 167 6363_CR41 6363_CR40 IA Fares (6363_CR52) 2025; 30 W Lo (6363_CR28) 2022; 35 6363_CR42 J Toldinas (6363_CR34) 2021; 10 E Jaw (6363_CR54) 2021; 13 A Henry (6363_CR7) 2023; 23 OA Sarumi (6363_CR110) 2020; 9 D Nedeljkovic (6363_CR11) 2022; 114 SK Smmarwar (6363_CR24) 2022; 104 W He (6363_CR82) 2020; 132 J Lehman (6363_CR83) 2014; 29 BA Tama (6363_CR99) 2019; 31 6363_CR70 D Upadhyay (6363_CR57) 2021; 18 S Sharma (6363_CR55) 2019; 13 S Ahmed (6363_CR60) 2023; 37 6363_CR6 6363_CR1 6363_CR2 6363_CR67 6363_CR66 6363_CR69 SM Kasongo (6363_CR90) 2020; 92 I Moric (6363_CR74) 2023; 42 A Sarkar (6363_CR4) 2023; 15 6363_CR16 6363_CR15 6363_CR14 6363_CR13 6363_CR96 SK Smmarwar (6363_CR18) 2022; 54 6363_CR98 6363_CR97 H Alqahtani (6363_CR26) 2024 6363_CR93 ND K S NP (6363_CR65) 2022; 10 C Wang (6363_CR8) 2023; 15 J Johnson (6363_CR75) 2020; 36 Y Qiu (6363_CR63) 2022; 38 Y Xiao (6363_CR36) 2019; 10 E Aguilar Madrid (6363_CR59) 2021; 12 BA Tama (6363_CR107) 2019; 7 6363_CR85 SM Kasongo (6363_CR95) 2019; 7 T-N Hoang (6363_CR37) 2024; 238 6363_CR87 V Hnamte (6363_CR91) 2023; 11 6363_CR102 X Ding (6363_CR58) 2023; 264 6363_CR80 6363_CR101 S Dr (6363_CR31) 2020; 2 6363_CR100 M Catillo (6363_CR51) 2025; 30 E Bou-Harb (6363_CR56) 2014; 16 JO Mebawondu (6363_CR12) 2020; 9 R Dong (6363_CR88) 2020; 14 G Geeta Kocher (6363_CR27) 2022; 54 A Doerig (6363_CR84) 2023; 24 G Kumar (6363_CR29) 2023; 134 6363_CR39 SK Smmarwar (6363_CR19) 2024; 14 R Abdulhammed (6363_CR3) 2019; 3 6363_CR33 R Khalid (6363_CR71) 2020; 61 6363_CR35 O Alkadi (6363_CR44) 2021; 8 T Su (6363_CR104) 2020; 8 S Pan (6363_CR68) 2022; 208 M Mehmood (6363_CR10) 2022; 70 Y Han (6363_CR79) 2022; 44 SK Smmarwar (6363_CR25) 2023; 108 S Hosseini (6363_CR103) 2020; 26 6363_CR23 6363_CR22 C Yin (6363_CR89) 2017; 5 K Budholiya (6363_CR61) 2022; 34 Z Hu (6363_CR106) 2020; 8 A Shaikh (6363_CR38) 2022 AK Dey (6363_CR50) 2024; 235 6363_CR21 A Hjort (6363_CR62) 2022; 39 X Gao (6363_CR105) 2019; 7 6363_CR20 I Ullah (6363_CR64) 2021; 12 C Shen (6363_CR81) 2018; 54 SM Kasongo (6363_CR94) 2023; 199 A Adeel (6363_CR5) 2016; 19 A Sperotto (6363_CR17) 2010; 12
References_xml	– volume: 9 start-page: 140628 year: 2021 ident: 6363_CR77 publication-title: IEEE Access. doi: 10.1109/ACCESS.2021.3119621 – volume: 38 start-page: 4145 year: 2022 ident: 6363_CR63 publication-title: Eng. Comput. doi: 10.1007/s00366-021-01393-9 – ident: 6363_CR40 doi: 10.1109/INMIC50486.2020.9318106 – volume: 10 start-page: 64375 year: 2022 ident: 6363_CR30 publication-title: IEEE Access. doi: 10.1109/ACCESS.2022.3182333 – volume: 21 start-page: 2307 year: 2017 ident: 6363_CR108 publication-title: Soft Comput. doi: 10.1007/s00500-015-1942-8 – volume: 24 start-page: 431 year: 2023 ident: 6363_CR84 publication-title: Nat. Rev. Neurosci. doi: 10.1038/s41583-023-00705-w – ident: 6363_CR45 doi: 10.1109/SIU.2016.7496029 – volume: 12 start-page: 343 year: 2010 ident: 6363_CR17 publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/SURV.2010.032210.00054 – volume: 14 start-page: 100130 year: 2024 ident: 6363_CR19 publication-title: Telemat Inf. Rep. doi: 10.1016/j.teler.2024.100130 – volume: 61 start-page: 102275 year: 2020 ident: 6363_CR71 publication-title: Sustain. Cities Soc. doi: 10.1016/j.scs.2020.102275 – volume: 92 start-page: 101752 year: 2020 ident: 6363_CR90 publication-title: Comput. Secur. doi: 10.1016/j.cose.2020.101752 – year: 2022 ident: 6363_CR38 publication-title: Int. J. Syst. Assur. Eng. Manag doi: 10.1007/s13198-021-01558-1 – ident: 6363_CR13 doi: 10.1109/CISDA.2009.5356528 – volume: 10 start-page: 1854 year: 2021 ident: 6363_CR34 publication-title: Electronics doi: 10.3390/electronics10151854 – year: 2023 ident: 6363_CR72 publication-title: Multimed Tools Appl. doi: 10.1007/s11042-023-17372-9 – ident: 6363_CR42 doi: 10.1109/eIT53891.2022.9813983 – volume: 10 start-page: 356 year: 2019 ident: 6363_CR36 publication-title: Information doi: 10.3390/info10110356 – volume: 19 start-page: 106 year: 2016 ident: 6363_CR5 publication-title: Phys. Commun. doi: 10.1016/j.phycom.2015.11.004 – volume: 54 start-page: 1503 year: 2022 ident: 6363_CR27 publication-title: Adv. Eng. Sci. – volume: 42 start-page: 621 year: 2023 ident: 6363_CR74 publication-title: Comp. Strateg doi: 10.1080/01495933.2023.2236489 – volume: 5 start-page: 21954 year: 2017 ident: 6363_CR89 publication-title: IEEE Access. doi: 10.1109/ACCESS.2017.2762418 – volume: 9 start-page: e00497 year: 2020 ident: 6363_CR12 publication-title: Sci. Afr. doi: 10.1016/j.sciaf.2020.e00497 – ident: 6363_CR39 doi: 10.1109/PST52912.2021.9647828 – ident: 6363_CR85 doi: 10.1080/17455030.2023.2189485 – volume: 8 start-page: 195741 year: 2020 ident: 6363_CR106 publication-title: IEEE Access. doi: 10.1109/ACCESS.2020.3034015 – volume: 39 start-page: 338 year: 2022 ident: 6363_CR62 publication-title: J. Prop. Res. doi: 10.1080/09599916.2022.2070525 – ident: 6363_CR69 doi: 10.1007/978-3-030-70569-5_18 – volume: 12 start-page: 136 year: 2021 ident: 6363_CR64 publication-title: Appl. Sci. doi: 10.3390/app12010136 – volume: 208 start-page: 109520 year: 2022 ident: 6363_CR68 publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2021.109520 – volume: 14 start-page: 166 year: 2020 ident: 6363_CR88 publication-title: IET Inf. Secur. doi: 10.1049/iet-ifs.2019.0294 – ident: 6363_CR15 doi: 10.1007/978-3-030-27192-3_9 – ident: 6363_CR35 doi: 10.1109/ICAML54311.2021.00019 – volume: 9 start-page: 27085 year: 2021 ident: 6363_CR86 publication-title: IEEE Access. doi: 10.1109/ACCESS.2021.3057654 – volume: 114 start-page: 102585 year: 2022 ident: 6363_CR11 publication-title: Comput. Secur. doi: 10.1016/j.cose.2021.102585 – volume: 23 start-page: 890 year: 2023 ident: 6363_CR7 publication-title: Sensors doi: 10.3390/s23020890 – ident: 6363_CR6 doi: 10.1609/aaai.v30i1.10287 – volume: 26 start-page: 4149 year: 2020 ident: 6363_CR103 publication-title: Wirel. Networks doi: 10.1007/s11276-020-02321-3 – volume: 35 start-page: 100471 year: 2022 ident: 6363_CR28 publication-title: Veh. Commun. doi: 10.1016/j.vehcom.2022.100471 – volume: 235 start-page: 757 year: 2024 ident: 6363_CR50 publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2024.04.072 – volume: 4 start-page: 141 year: 2021 ident: 6363_CR73 publication-title: J. Peace Nucl. Disarm doi: 10.1080/25751654.2021.1918932 – volume: 16 start-page: 1496 year: 2014 ident: 6363_CR56 publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/SURV.2013.102913.00020 – volume: 8 start-page: 29575 year: 2020 ident: 6363_CR104 publication-title: IEEE Access. doi: 10.1109/ACCESS.2020.2972627 – volume: 108 start-page: 108731 year: 2023 ident: 6363_CR25 publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2023.108731 – volume: 238 start-page: 122181 year: 2024 ident: 6363_CR37 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.122181 – volume: 12 start-page: 50 year: 2021 ident: 6363_CR59 publication-title: Information doi: 10.3390/info12020050 – ident: 6363_CR70 – volume: 134 start-page: 89 year: 2023 ident: 6363_CR29 publication-title: Comput. Model. Eng. Sci. doi: 10.32604/cmes.2022.020724 – volume: 154 start-page: 104417 year: 2025 ident: 6363_CR47 publication-title: Comput. Secur. doi: 10.1016/j.cose.2025.104417 – volume: 15 start-page: 423 year: 2023 ident: 6363_CR4 publication-title: Int. J. Inf. Technol. doi: 10.1007/s41870-022-01115-4 – year: 2024 ident: 6363_CR26 publication-title: Int. J. Mach. Learn. Cybern doi: 10.1007/s13042-024-02465-0 – volume: 31 start-page: 955 year: 2019 ident: 6363_CR99 publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-3128-z – volume: 18 start-page: 1104 year: 2021 ident: 6363_CR57 publication-title: IEEE Trans. Netw. Serv. Manag doi: 10.1109/TNSM.2020.3032618 – ident: 6363_CR96 doi: 10.1007/978-3-030-04212-7_53 – volume: 15 start-page: 568 year: 2023 ident: 6363_CR8 publication-title: Symmetry (Basel) doi: 10.3390/sym15030568 – ident: 6363_CR1 doi: 10.32628/CSEIT195215 – volume: 104 start-page: 108410 year: 2022 ident: 6363_CR24 publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2022.108410 – ident: 6363_CR48 doi: 10.1016/j.dss.2024.114351 – volume: 34 start-page: 4514 year: 2022 ident: 6363_CR61 publication-title: J. King Saud Univ. - Comput. Inf. Sci. doi: 10.1016/j.jksuci.2020.10.013 – volume: 74 start-page: 2607 year: 2023 ident: 6363_CR9 publication-title: Comput. Mater. Contin doi: 10.32604/cmc.2023.032617 – ident: 6363_CR21 doi: 10.1007/978-3-030-81462-5_26 – ident: 6363_CR16 doi: 10.1109/ICASID.2019.8925239 – volume: 10 start-page: 119357 year: 2022 ident: 6363_CR92 publication-title: IEEE Access. doi: 10.1109/ACCESS.2022.3221400 – volume: 167 start-page: 1230 year: 2020 ident: 6363_CR109 publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2020.03.438 – volume: 174 start-page: 107247 year: 2020 ident: 6363_CR53 publication-title: Comput. Networks doi: 10.1016/j.comnet.2020.107247 – ident: 6363_CR97 doi: 10.1109/ISCAS.2019.8702583 – volume: 7 start-page: 82512 year: 2019 ident: 6363_CR105 publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2923640 – volume: 8 start-page: 12251 year: 2021 ident: 6363_CR32 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3060878 – volume: 264 start-page: 126174 year: 2023 ident: 6363_CR58 publication-title: Energy doi: 10.1016/j.energy.2022.126174 – ident: 6363_CR22 doi: 10.1007/978-3-031-54547-4_8 – volume: 30 start-page: 100666 year: 2025 ident: 6363_CR52 publication-title: Egypt. Inf. J. doi: 10.1016/j.eij.2025.100666 – ident: 6363_CR67 doi: 10.1080/08839514.2023.2166222 – volume: 129 start-page: 783 year: 2023 ident: 6363_CR43 publication-title: Wirel. Pers. Commun. doi: 10.1007/s11277-022-10155-9 – ident: 6363_CR93 doi: 10.1002/ett.4150 – volume: 44 start-page: 7436 year: 2022 ident: 6363_CR79 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2021.3117837 – volume: 30 start-page: 101519 year: 2025 ident: 6363_CR51 publication-title: Internet Things doi: 10.1016/j.iot.2025.101519 – volume: 9 start-page: e00500 year: 2020 ident: 6363_CR110 publication-title: Sci. Afr. doi: 10.1016/j.sciaf.2020.e00500 – ident: 6363_CR20 doi: 10.1007/978-981-16-8664-1_30 – volume: 11 start-page: 37131 year: 2023 ident: 6363_CR91 publication-title: IEEE Access. doi: 10.1109/ACCESS.2023.3266979 – ident: 6363_CR101 doi: 10.1016/B978-0-12-810408-8.00016-X – ident: 6363_CR14 doi: 10.1007/978-3-030-04503-6_14 – volume: 7 start-page: 94497 year: 2019 ident: 6363_CR107 publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2928048 – volume: 10 start-page: 75158 year: 2022 ident: 6363_CR65 publication-title: IEEE Access. doi: 10.1109/ACCESS.2022.3190416 – ident: 6363_CR102 doi: 10.1109/WINCOM.2016.7777224 – volume: 54 start-page: 102852 year: 2022 ident: 6363_CR18 publication-title: Sustain. Energy Technol. Assessments doi: 10.1016/j.seta.2022.102852 – ident: 6363_CR80 doi: 10.3389/fncel.2023.1220030 – volume: 7 start-page: 38597 year: 2019 ident: 6363_CR95 publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2905633 – ident: 6363_CR98 doi: 10.1109/BigDataSecurity-HPSC-IDS.2019.00062 – ident: 6363_CR100 doi: 10.1007/978-3-030-06158-6_9 – volume: 199 start-page: 113 year: 2023 ident: 6363_CR94 publication-title: Comput. Commun. doi: 10.1016/j.comcom.2022.12.010 – volume: 109 start-page: 911 year: 2021 ident: 6363_CR78 publication-title: Proc. IEEE doi: 10.1109/JPROC.2021.3067593 – ident: 6363_CR33 doi: 10.1109/ICC45855.2022.9838780 – ident: 6363_CR23 doi: 10.1109/AIC57670.2023.10263974 – volume: 8 start-page: 8912 year: 2018 ident: 6363_CR76 publication-title: Sci. Rep. doi: 10.1038/s41598-018-27169-8 – volume: 70 start-page: 91 year: 2022 ident: 6363_CR10 publication-title: Comput. Mater. Contin doi: 10.32604/cmc.2022.019127 – volume: 37 start-page: 2166222 year: 2023 ident: 6363_CR60 publication-title: Appl. Artif. Intell. doi: 10.1080/08839514.2023.2166222 – ident: 6363_CR87 doi: 10.1080/17455030.2022.2091807 – volume: 2 start-page: 190 year: 2020 ident: 6363_CR31 publication-title: J. ISMAC doi: 10.36548/jismac.2020.4.002 – ident: 6363_CR41 doi: 10.1109/LCN48667.2020.9314858 – ident: 6363_CR2 doi: 10.14569/IJACSA.2020.0110670 – volume: 148 start-page: 104109 year: 2025 ident: 6363_CR49 publication-title: Comput. Secur. doi: 10.1016/j.cose.2024.104109 – volume: 24 start-page: 16605 year: 2020 ident: 6363_CR46 publication-title: Soft Comput. doi: 10.1007/s00500-020-04963-z – volume: 13 start-page: 142 year: 2019 ident: 6363_CR55 publication-title: Recent. Pat. Eng. doi: 10.2174/1872212112666180402122150 – volume: 3 start-page: 1 year: 2019 ident: 6363_CR3 publication-title: IEEE Sens. Lett. doi: 10.1109/LSENS.2018.2879990 – volume: 36 start-page: 422 year: 2020 ident: 6363_CR75 publication-title: Def. Secur. Anal. doi: 10.1080/14751798.2020.1857911 – volume: 8 start-page: 9463 year: 2021 ident: 6363_CR44 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.2996590 – volume: 13 start-page: 1764 year: 2021 ident: 6363_CR54 publication-title: Symmetry (Basel) doi: 10.3390/sym13101764 – volume: 29 start-page: 56 year: 2014 ident: 6363_CR83 publication-title: IEEE Intell. Syst. doi: 10.1109/MIS.2014.92 – ident: 6363_CR66 doi: 10.1109/ICSSIT48917.2020.9214206 – volume: 54 start-page: 8558 year: 2018 ident: 6363_CR81 publication-title: Water Resour. Res. doi: 10.1029/2018WR022643 – volume: 132 start-page: 108 year: 2020 ident: 6363_CR82 publication-title: Neural Netw. doi: 10.1016/j.neunet.2020.08.001
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Snippet	Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated... Abstract Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly...
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SubjectTerms	639/166 639/705 692/1807 Cyber security Humanities and Social Sciences Internet of things (IoT) Intrusion detection Intrusion detection systems (IDS) multidisciplinary Optimized sequential neural network Optimized XGBoost Science Science (multidisciplinary)
Title	Smart deep learning model for enhanced IoT intrusion detection
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