{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:34:06Z","timestamp":1786980846274,"version":"3.56.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T00:00:00Z","timestamp":1588723200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T00:00:00Z","timestamp":1588723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672338 and 61873160"],"award-info":[{"award-number":["61672338 and 61873160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s10489-020-01694-4","type":"journal-article","created":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T14:03:41Z","timestamp":1588773821000},"page":"3162-3178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["An intrusion detection approach based on improved deep belief network"],"prefix":"10.1007","volume":"50","author":[{"given":"Qiuting","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhi","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-4364","authenticated-orcid":false,"given":"Kuan-Ching","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Letian","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arcangelo","family":"Castiglione","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,5,6]]},"reference":[{"key":"1694_CR1","unstructured":"Cyber-Attack Against Ukrainian Critical Infrastructure. [Online]. Available: https:\/\/www.us-cert.gov\/ics\/alerts\/IR-ALERT-H-16-056-01 (Accessed Aug 6, 2019)"},{"key":"1694_CR2","unstructured":"No municipality paid ransoms in 'coordinated ransomware attack' that hit Texas. [Online]. Available: https:\/\/www.zdnet.com\/article\/no-municipality-paid-ransoms-in-coordinated-ransomware-attack-that-hit-texas\/ (Accessed Aug 6, 2019)"},{"issue":"3","key":"1694_CR3","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.3233\/JIFS-169457","volume":"34","author":"Y Dai","year":"2018","unstructured":"Dai Y, Wang G, Li KC (2018) Conceptual alignment deep neural networks[J]. Journal of Intelligent & Fuzzy Systems 34(3):1631\u20131642","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"1694_CR4","doi-asserted-by":"crossref","unstructured":"Song H, Jiang Z, Men A, et al. A hybrid semi-supervised anomaly detection model for high-dimensional data[J]. Computational intelligence and neuroscience, 2017, 2017","DOI":"10.1155\/2017\/8501683"},{"key":"1694_CR5","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.patcog.2016.05.028","volume":"61","author":"Z Zhao","year":"2017","unstructured":"Zhao Z, Jiao L, Zhao J et al (2017) Discriminant deep belief network for high-resolution SAR image classification[J]. Pattern Recogn 61:686\u2013701","journal-title":"Pattern Recogn"},{"issue":"3","key":"1694_CR6","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s11063-016-9556-4","volume":"45","author":"S Basu","year":"2017","unstructured":"Basu S, Karki M, Ganguly S et al (2017) Learning sparse feature representations using probabilistic quadtrees and deep belief nets[J]. Neural Process Lett 45(3):855\u2013867","journal-title":"Neural Process Lett"},{"key":"1694_CR7","doi-asserted-by":"crossref","unstructured":"Ding Y, Chen S, Xu J. Application of deep belief networks for opcode based malware detection[C]\/\/2016 international joint conference on neural networks (IJCNN). IEEE, 2016: 3901\u20133908","DOI":"10.1109\/IJCNN.2016.7727705"},{"key":"1694_CR8","doi-asserted-by":"crossref","unstructured":"Zhao G, Zhang C, Zheng L. Intrusion detection using deep belief network and probabilistic neural network[C]\/\/2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC). IEEE, 2017, 1: 639\u2013642","DOI":"10.1109\/CSE-EUC.2017.119"},{"key":"1694_CR9","doi-asserted-by":"crossref","unstructured":"Kaiser J, Zimmerer D, Tieck J C V, et al. Spiking convolutional deep belief networks[C]\/\/international conference on artificial neural networks. Springer, Cham, 2017: 3\u201311","DOI":"10.1007\/978-3-319-68612-7_1"},{"key":"1694_CR10","unstructured":"Koo J, Klabjan D. Improved Classification Based on Deep Belief Networks[J]. arXiv preprint arXiv:1804.09812, 2018"},{"key":"1694_CR11","doi-asserted-by":"publisher","unstructured":"Robert W. Harrison. Continuous restricted Boltzmann machines[J]. Wireless Networks, https:\/\/doi.org\/10.1007\/s11276-018-01903-6. (online: 2018. 12)","DOI":"10.1007\/s11276-018-01903-6"},{"key":"1694_CR12","doi-asserted-by":"publisher","unstructured":"Liang W, Li K C, Long J, et al. An industrial network intrusion detection algorithm based on multi-feature data clustering optimization model[J], IEEE transactions on industrial informatics, IEEE. DOI: https:\/\/doi.org\/10.1109\/TII.2019.2946791","DOI":"10.1109\/TII.2019.2946791"},{"issue":"1","key":"1694_CR13","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s10846-015-0213-3","volume":"80","author":"Z Cui","year":"2015","unstructured":"Cui Z, Ge SS, Cao Z et al (2015) Analysis of different sparsity methods in constrained RBM for sparse representation in cognitive robotic perception[J]. Journal of Intelligent & Robotic Systems 80(1):121\u2013132","journal-title":"Journal of Intelligent & Robotic Systems"},{"issue":"9","key":"1694_CR14","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1016\/j.patcog.2014.03.025","volume":"47","author":"NN Ji","year":"2014","unstructured":"Ji NN, Zhang JS, Zhang CX (2014) A sparse-response deep belief network based on rate distortion theory[J]. Pattern Recogn 47(9):3179\u20133191","journal-title":"Pattern Recogn"},{"key":"1694_CR15","first-page":"11","volume-title":"Training deep belief network with sparse hidden units[C]\/\/Chinese conference on pattern recognition","author":"Z Hu","year":"2014","unstructured":"Hu Z, Hu W, Zhang C (2014) Training deep belief network with sparse hidden units[C]\/\/Chinese conference on pattern recognition. Springer, Berlin, Heidelberg, pp 11\u201320"},{"issue":"6","key":"1694_CR16","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1109\/TNNLS.2017.2692773","volume":"29","author":"D Chen","year":"2018","unstructured":"Chen D, Lv J, Yi Z (2018) Graph regularized restricted Boltzmann machine [J]. IEEE Transactions on Neural Networks & Learning Systems 29(6):2651\u20132659","journal-title":"IEEE Transactions on Neural Networks & Learning Systems"},{"key":"1694_CR17","doi-asserted-by":"crossref","unstructured":"Alom M Z, Bontupalli V R, Taha T M. Intrusion detection using deep belief networks[C]\/\/2015 National Aerospace and electronics conference (NAECON). IEEE, 2015: 339\u2013344","DOI":"10.1109\/NAECON.2015.7443094"},{"key":"1694_CR18","unstructured":"Adil S H, Ali S S A, Raza K, et al. An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief networks[C]\/\/SoMeT, 2014: 94\u2013102"},{"issue":"05","key":"1694_CR19","doi-asserted-by":"crossref","first-page":"687","DOI":"10.12677\/CSA.2018.85077","volume":"08","author":"L Yu","year":"2018","unstructured":"Yu L (2018) Research on intrusion detection based on deep confidence network[J]. Computer Science and Application 08(05):687\u2013701","journal-title":"Computer Science and Application"},{"key":"1694_CR20","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.cose.2018.11.005","volume":"81","author":"B Selvakumar","year":"2019","unstructured":"Selvakumar B, Muneeswaran K (2019) Firefly algorithm based feature selection for network intrusion detection[J]. Computers & Security 81:148\u2013155","journal-title":"Computers & Security"},{"issue":"6","key":"1694_CR21","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1109\/TFUZZ.2015.2406889","volume":"23","author":"CLP Chen","year":"2015","unstructured":"Chen CLP, Zhang CY, Chen L et al (2015) Fuzzy restricted Boltzmann machine for the enhancement of deep learning[J]. IEEE Trans Fuzzy Syst 23(6):2163\u20132173","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"7","key":"1694_CR22","first-page":"2695","volume":"29","author":"ER Merino","year":"2017","unstructured":"Merino ER, Castrillejo FM, Pin JD (2017) Neighborhood-based stopping criterion for contrastive divergence[J]. IEEE transactions on neural networks and learning systems 29(7):2695\u20132704","journal-title":"IEEE transactions on neural networks and learning systems"},{"issue":"11","key":"1694_CR23","doi-asserted-by":"crossref","first-page":"2814","DOI":"10.1109\/TSP.2017.2675866","volume":"65","author":"M Fatemi","year":"2017","unstructured":"Fatemi M, Granstr\u00f6m K, Svensson L et al (2017) Poisson multi-Bernoulli mapping using Gibbs sampling[J]. IEEE Trans Signal Process 65(11):2814\u20132827","journal-title":"IEEE Trans Signal Process"},{"key":"1694_CR24","doi-asserted-by":"crossref","unstructured":"Wang L, Ye P, Xiang J. A modified algorithm based on smoothed L0 norm in compressive sensing signal reconstruction[C]\/\/2018 25th IEEE international conference on image processing (ICIP). IEEE, 2018: 1812\u20131816","DOI":"10.1109\/ICIP.2018.8451799"},{"key":"1694_CR25","doi-asserted-by":"crossref","unstructured":"Keyvanrad M A, Homayounpour M M. Normal sparse deep belief network[C]\/\/2015 international joint conference on neural networks (IJCNN). IEEE, 2015: 1\u20137","DOI":"10.1109\/IJCNN.2015.7280688"},{"key":"1694_CR26","doi-asserted-by":"crossref","unstructured":"Alom M Z, Bontupalli V R, Taha T M. Intrusion detection using deep belief networks[C]\/\/2015 National Aerospace and electronics conference (NAECON). IEEE, 2015: 339\u2013344","DOI":"10.1109\/NAECON.2015.7443094"},{"key":"1694_CR27","doi-asserted-by":"crossref","unstructured":"Niyaz Q, Sun W, Javaid A Y. A deep learning based DDoS detection system in software-defined networking (SDN) [J]. arXiv preprint arXiv:1611.07400, 2016","DOI":"10.4108\/eai.28-12-2017.153515"},{"key":"1694_CR28","doi-asserted-by":"publisher","unstructured":"Chen P, Han D, Tan F, et al. Reinforcement-based robust variable pitch control of wind turbines[J]. IEEE access, IEEE. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2020.2968853","DOI":"10.1109\/ACCESS.2020.2968853"},{"key":"1694_CR29","unstructured":"Rathore S, Saxena A, Manoria M. Intrusion detection system on KDDCup99 dataset: a survey[J]. Int J Comput Sci Inf Tech, 2015"},{"issue":"6","key":"1694_CR30","first-page":"20","volume":"7","author":"MR Parsaei","year":"2016","unstructured":"Parsaei MR, Rostami SM, Javidan R (2016) A hybrid data mining approach for intrusion detection on imbalanced NSL-KDD dataset[J]. Int J Adv Comput Sci Appl 7(6):20\u201325","journal-title":"Int J Adv Comput Sci Appl"},{"key":"1694_CR31","doi-asserted-by":"crossref","first-page":"30373","DOI":"10.1109\/ACCESS.2019.2899721","volume":"7","author":"FA Khan","year":"2019","unstructured":"Khan FA, Gumaei A, Derhab A et al (2019) A novel two-stage deep learning model for efficient network intrusion detection[J]. IEEE Access 7:30373\u201330385","journal-title":"IEEE Access"},{"key":"1694_CR32","doi-asserted-by":"crossref","unstructured":"Moustafa N, Slay J. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)[C]\/\/2015 military communications and information systems conference (MilCIS). IEEE, 2015: 1\u20136","DOI":"10.1109\/MilCIS.2015.7348942"},{"issue":"1\u20133","key":"1694_CR33","first-page":"18","volume":"25","author":"N Moustafa","year":"2016","unstructured":"Moustafa N, Slay J (2016) The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set[J]. Information Security Journal: A Global Perspective 25(1\u20133):18\u201331","journal-title":"Information Security Journal: A Global Perspective"},{"key":"1694_CR34","doi-asserted-by":"publisher","unstructured":"Liang W, Fan Y, Li K C, et al. Secure data storage and recovery in industrial Blockchain network environments[J]. IEEE transactions on industrial informatics, IEEE. DOI:https:\/\/doi.org\/10.1109\/TII.2020.2966069","DOI":"10.1109\/TII.2020.2966069"},{"issue":"1","key":"1694_CR35","first-page":"62","volume":"7","author":"Z Dewa","year":"2016","unstructured":"Dewa Z, Maglaras LA (2016) Data mining and intrusion detection systems[J]. Int J Adv Comput Sci Appl 7(1):62\u201371","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"6","key":"1694_CR36","doi-asserted-by":"crossref","first-page":"3582","DOI":"10.1109\/TII.2019.2907092","volume":"15","author":"W Liang","year":"2019","unstructured":"Liang W, Tang M, Long J et al (2019) A secure fabric blockchain-based data transmission technique for industrial internet-of-things[J]. IEEE Transactions on Industrial Informatics 15(6):3582\u20133592","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"5","key":"1694_CR37","first-page":"2076","volume":"8","author":"L Li","year":"2018","unstructured":"Li L, Xie L, Li W et al (2018) Improved deep belief networks (IDBN) dynamic model-based detection and mitigation for targeted attacks on heavy-duty robots[J]. Appl Sci 8(5):2076\u20133417","journal-title":"Appl Sci"},{"key":"1694_CR38","doi-asserted-by":"crossref","first-page":"18207","DOI":"10.1109\/ACCESS.2020.2968492","volume":"8","author":"H Liu","year":"2020","unstructured":"Liu H, Han D, Li D (2020) Fabric-iot: a Blockchain-based access control system in IoT[J]. IEEE Access 8:18207\u201318218","journal-title":"IEEE Access"},{"key":"1694_CR39","doi-asserted-by":"crossref","unstructured":"Gajera V, Gupta R, Jana P K. An effective multi-objective task scheduling algorithm using min-max normalization in cloud computing[C]\/\/2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT). IEEE, 2016: 812\u2013816","DOI":"10.1109\/ICATCCT.2016.7912111"},{"key":"1694_CR40","doi-asserted-by":"crossref","unstructured":"Khemiri H, Petrovska-Delacretaz D. Cohort selection for text-dependent speaker verification score normalization[C]\/\/2016 2nd international conference on advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2016: 689\u2013692","DOI":"10.1109\/ATSIP.2016.7523167"},{"issue":"2","key":"1694_CR41","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1109\/JIOT.2018.2883344","volume":"6","author":"J Li","year":"2018","unstructured":"Li J, Zhao Z, Li R et al (2018) AI-based two-stage intrusion detection for software defined IoT networks[J]. IEEE Internet Things J 6(2):2093\u20132102","journal-title":"IEEE Internet Things J"},{"key":"1694_CR42","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.future.2017.08.043","volume":"82","author":"AA Diro","year":"2018","unstructured":"Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for internet of things[J]. Futur Gener Comput Syst 82:761\u2013768","journal-title":"Futur Gener Comput Syst"},{"issue":"2","key":"1694_CR43","doi-asserted-by":"crossref","first-page":"238","DOI":"10.3390\/app9020238","volume":"9","author":"Y Yang","year":"2019","unstructured":"Yang Y, Zheng K, Wu C et al (2019) Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks[J]. Appl Sci 9(2):238","journal-title":"Appl Sci"},{"key":"1694_CR44","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/ACCESS.2019.2891933","volume":"7","author":"Y Djenouri","year":"2019","unstructured":"Djenouri Y, Belhadi A, Lin JCW et al (2019) Adapted k-nearest neighbors for detecting anomalies on spatio\u2013temporal traffic flow[J]. IEEE Access 7:10015\u201310027","journal-title":"IEEE Access"},{"key":"1694_CR45","doi-asserted-by":"crossref","first-page":"31711","DOI":"10.1109\/ACCESS.2019.2903723","volume":"7","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Li P, Wang X (2019) Intrusion detection for IoT based on improved genetic algorithm and deep belief network[J]. IEEE Access 7:31711\u201331722","journal-title":"IEEE Access"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01694-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01694-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01694-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T21:01:23Z","timestamp":1620248483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01694-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,6]]},"references-count":45,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["1694"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01694-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,6]]},"assertion":[{"value":"6 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}