{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:20:13Z","timestamp":1787066413564,"version":"build-2736575974"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2019,3,26]],"date-time":"2019-03-26T00:00:00Z","timestamp":1553558400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s00521-019-04152-6","type":"journal-article","created":{"date-parts":[[2019,3,26]],"date-time":"2019-03-26T14:03:38Z","timestamp":1553609018000},"page":"3135-3147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Intrusion detection using deep sparse auto-encoder and self-taught learning"],"prefix":"10.1007","volume":"32","author":[{"given":"Aqsa Saeed","family":"Qureshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2039-5305","authenticated-orcid":false,"given":"Asifullah","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nauman","family":"Shamim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Hanif","family":"Durad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,3,26]]},"reference":[{"key":"4152_CR1","first-page":"1","volume":"2","author":"AS Ashoor","year":"2011","unstructured":"Ashoor AS, Gore S (2011) Importance of intrusion detection system (ids). Int J Sci Eng Res 2:1\u20134","journal-title":"Int J Sci Eng Res"},{"key":"4152_CR2","first-page":"94","volume":"800","author":"K Scarfone","year":"2007","unstructured":"Scarfone K, Mell P (2007) Guide to intrusion detection and prevention systems (idps). NIST Spec Publ 800:94","journal-title":"NIST Spec Publ"},{"key":"4152_CR3","doi-asserted-by":"publisher","first-page":"975","DOI":"10.5120\/12136-8419","volume":"69","author":"K Letou","year":"2015","unstructured":"Letou K, Devi D (2015) Host-based intrusion detection and prevention system (HIDPS). Int J Comput Appl 69:975\u2013988. \nhttps:\/\/doi.org\/10.5120\/12136-8419","journal-title":"Int J Comput Appl"},{"key":"4152_CR4","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/65.283931","volume":"83","author":"B Mukherjee","year":"1994","unstructured":"Mukherjee B, Heberlein LT (1994) Network intrusion detection. IEEE Netw 83:26\u201341","journal-title":"IEEE Netw"},{"key":"4152_CR5","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cose.2008.06.002","volume":"27","author":"W Masri","year":"2008","unstructured":"Masri W, Podgurski A (2008) Application-based anomaly intrusion detection with dynamic information flow analysis 5. Comput Secur 27:176\u2013187. \nhttps:\/\/doi.org\/10.1016\/j.cose.2008.06.002","journal-title":"Comput Secur"},{"key":"4152_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.cose.2008.08.003","volume":"28","author":"J D\u0131","year":"2009","unstructured":"D\u0131 J (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28:18\u201328. \nhttps:\/\/doi.org\/10.1016\/j.cose.2008.08.003","journal-title":"Comput Secur"},{"key":"4152_CR7","unstructured":"Diaz-Gomez PA, Hougen DF (2007) Misuse detection: an iterative process vs. a genetic algorithm approach. In: ICEIS (2), pp 455\u2013458"},{"key":"4152_CR8","doi-asserted-by":"crossref","unstructured":"Malhotra S, Bali V, Paliwal KK (2017) Genetic programming and K-nearest neighbour classifier based intrusion detection model. In: 7th International conference on cloud computing data science and engineering 2017, IEEE, pp 42\u201346","DOI":"10.1109\/CONFLUENCE.2017.7943121"},{"key":"4152_CR9","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.patrec.2004.09.045","volume":"26","author":"C Zhang","year":"2005","unstructured":"Zhang C, Jiang J, Kamel M (2005) Intrusion detection using hierarchical neural networks. Pattern Recognit Lett 26:779\u2013791. \nhttps:\/\/doi.org\/10.1016\/j.patrec.2004.09.045","journal-title":"Pattern Recognit Lett"},{"key":"4152_CR10","first-page":"258","volume":"7","author":"M Panda","year":"2007","unstructured":"Panda M, Patra MR (2007) Network intrusion detection using naive bayes. Int J Comput Sci Netw Secur 7:258\u2013263","journal-title":"Int J Comput Sci Netw Secur"},{"key":"4152_CR11","doi-asserted-by":"crossref","unstructured":"Zhang J, Zulkernine M (2005) Network intrusion detection using random forests. PST, Citeseer","DOI":"10.1109\/ARES.2006.7"},{"key":"4152_CR12","unstructured":"Portnoy L, Eskin E, Stolfo S (2001) Intrusion detection with unlabeled data using clustering. In: Proceedings of the ACM CSS workshop on data mining applied to security (DMSA-2001, Citeseer)"},{"key":"4152_CR13","unstructured":"Leung K, Leckie C (2005) Unsupervised anomaly detection in network intrusion detection using clusters. In: Proceedings of twenty-eighth Australasian computer science conference, vol 38, pp 333\u2013342"},{"key":"4152_CR14","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s00521-015-1964-2","volume":"27","author":"BMARRM Chizari","year":"2016","unstructured":"Chizari BMARRM, Eslami AMM (2016) A hybrid method consisting of GA and SVM for intrusion detection system. Neural Comput Appl 27:1669\u20131676. \nhttps:\/\/doi.org\/10.1007\/s00521-015-1964-2","journal-title":"Neural Comput Appl"},{"key":"4152_CR15","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1007\/s40815-016-0160-6","volume":"19","author":"SRS Devaraju","year":"2017","unstructured":"Devaraju SRS (2017) Attack\u2019 s feature selection-based network intrusion detection system using fuzzy control language. Int J Fuzzy Syst 19:316\u2013328. \nhttps:\/\/doi.org\/10.1007\/s40815-016-0160-6","journal-title":"Int J Fuzzy Syst"},{"key":"4152_CR16","doi-asserted-by":"publisher","unstructured":"Srinivasan T, Vijaykumar V, Chandrasekar R (2006) A self-organized agent-based architecture for power-aware intrusion detection in wireless ad-hoc networks. In: 2006 International conference on computing and informatics, 2006, pp 1\u20136. \nhttps:\/\/doi.org\/10.1109\/icoci.2006.5276609","DOI":"10.1109\/icoci.2006.5276609"},{"key":"4152_CR17","first-page":"155","volume":"5","author":"A Puri","year":"2017","unstructured":"Puri A, Sharma N (2017) A novel technique for intrusion detection system for network security using hybrid svm-cart. Int J Eng Dev Res 5:155\u2013161","journal-title":"Int J Eng Dev Res"},{"key":"4152_CR18","doi-asserted-by":"crossref","unstructured":"Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. In: Proceedings of the 2002 international joint conference on neural networks, IJCNN\u201902, 2002, pp 1702\u20131707","DOI":"10.1109\/IJCNN.2002.1007774"},{"key":"4152_CR19","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.ins.2016.04.019","volume":"378","author":"R Aamir","year":"2017","unstructured":"Aamir R, Ashfaq R, Wang X, Zhexue J, Abbas H, He Y (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484\u2013497. \nhttps:\/\/doi.org\/10.1016\/j.ins.2016.04.019","journal-title":"Inf Sci"},{"key":"4152_CR20","unstructured":"Kim J, Shin N, Jo SY, Kim SH. Method of intrusion detection using deep neural network. In: 2017 IEEE International conference on big data and smart computing (BigComp), IEEE; 2017, pp 313\u2013316"},{"key":"4152_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2418-1","author":"J Kevric","year":"2016","unstructured":"Kevric J, Jukic S, Subasi A (2016) An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-016-2418-1","journal-title":"Neural Comput Appl"},{"key":"4152_CR22","unstructured":"Cup KDD (2007) Data: available on \nhttps:\/\/archive.ics.uci.edu\/ml\/datasets\/kdd+cup+1999+data\n\n. Accessed 25 Mar 2019"},{"key":"4152_CR23","doi-asserted-by":"crossref","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: Computational intelligence in security and defence application, 2009, pp 1\u20136","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"4152_CR24","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.jvcir.2012.05.003","volume":"24","author":"C Lu","year":"2013","unstructured":"Lu C, Min H, Gui J, Zhu L, Lei Y (2013) Face recognition via weighted sparse representation. J Vis Commun Image Represent 24:111\u2013116. \nhttps:\/\/doi.org\/10.1016\/j.jvcir.2012.05.003","journal-title":"J Vis Commun Image Represent"},{"key":"4152_CR25","doi-asserted-by":"publisher","first-page":"6786","DOI":"10.1016\/j.ijleo.2013.05.099","volume":"124","author":"J Mi","year":"2013","unstructured":"Mi J, Lei D, Gui J (2013) Optik a novel method for recognizing face with partial occlusion via sparse representation. Opt Int J Light Electron Opt 124:6786\u20136789. \nhttps:\/\/doi.org\/10.1016\/j.ijleo.2013.05.099","journal-title":"Opt Int J Light Electron Opt"},{"key":"4152_CR26","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1109\/TCYB.2015.2457234","volume":"46","author":"J Gui","year":"2016","unstructured":"Gui J, Liu T, Tao D, Sun Z, Tan T (2016) Representative vector machines: a unified framework for classical classifiers. IEEE Trans Cybern 46:1877\u20131888","journal-title":"IEEE Trans Cybern"},{"key":"4152_CR27","doi-asserted-by":"publisher","first-page":"3126","DOI":"10.1109\/TIP.2014.2326001","volume":"23","author":"J Gui","year":"2014","unstructured":"Gui J, Tao D, Sun Z, Luo Y, You X, Tang YY (2014) Group sparse multiview patch alignment framework with view consistency for image classification. IEEE Trans Image Process 23:3126\u20133137","journal-title":"IEEE Trans Image Process"},{"key":"4152_CR28","doi-asserted-by":"crossref","unstructured":"Gui J, Sun Z, Hou G, Tan T. An optimal set of code words and correntropy for rotated least squares regression. In: IEEE international joint conference on biometrics (IJCB), 2014, pp 1\u20136","DOI":"10.1109\/BTAS.2014.6996222"},{"key":"4152_CR29","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.asoc.2017.05.031","volume":"58","author":"AS Qureshi","year":"2017","unstructured":"Qureshi AS, Khan A, Zameer A, Usman A (2017) Wind power prediction using deep neural network based meta regression and transfer learning. Appl Soft Comput J 58:742\u2013755. \nhttps:\/\/doi.org\/10.1016\/j.asoc.2017.05.031","journal-title":"Appl Soft Comput J"},{"key":"4152_CR30","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1109\/TNNLS.2016.2551724","volume":"28","author":"J Gui","year":"2017","unstructured":"Gui J, Sun Z, Ji S, Member S, Tao D, Tan T (2017) Feature selection based on structured sparsity: a comprehensive study. IEEE Trans Neural Netw Learn Syst 28:1490\u20131507","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4152_CR31","doi-asserted-by":"crossref","unstructured":"Raina R, Battle A, Lee H, Packer B, Ng AY (2007) Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th international conference on machine learning, ACM, 2007, pp 759\u201366","DOI":"10.1145\/1273496.1273592"},{"key":"4152_CR32","unstructured":"Maurer A, Pontil M, Romera-Paredes B. Sparse coding for multitask and transfer learning. In: International conference on machine learning, 2013, pp 343\u2013351"},{"key":"4152_CR33","unstructured":"Lim JJ, Salakhutdinov RR, Torralba A (2011) Transfer learning by borrowing examples for multiclass object detection. In: Advances in neural information processing systems, 2011, pp 118\u2013126"},{"key":"4152_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.renene.2015.06.034","volume":"85","author":"Q Hu","year":"2016","unstructured":"Hu Q, Zhang R, Zhou Y (2016) Transfer learning for short-term wind speed prediction with deep neural networks. Renew Energy 85:83\u201395. \nhttps:\/\/doi.org\/10.1016\/j.renene.2015.06.034","journal-title":"Renew Energy"},{"key":"4152_CR35","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neucom.2012.08.056","volume":"120","author":"B Du","year":"2013","unstructured":"Du B, Zhang L, Tao D, Zhang D (2013) Neurocomputing unsupervised transfer learning for target detection from hyperspectral images. Neurocomput J 120:72\u201382. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2012.08.056","journal-title":"Neurocomput J"},{"key":"4152_CR36","doi-asserted-by":"publisher","unstructured":"Cao X (2013) A practical transfer learning algorithm for face verification. In: IEEE international conference on computer vision (ICCV), 2013, pp 3208\u20133215. \nhttps:\/\/doi.org\/10.1109\/iccv.2013.398","DOI":"10.1109\/iccv.2013.398"},{"key":"4152_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-012-1084-1","author":"S Yang","year":"2012","unstructured":"Yang S, Lin M, Hou C (2012) A general framework for transfer sparse subspace learning. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-012-1084-1","journal-title":"Neural Comput Appl"},{"key":"4152_CR38","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s00521-012-1297-3","volume":"24","author":"L La","year":"2014","unstructured":"La L, Guo Q, Cao Q, Wang Y (2014) Transfer learning with reasonable boosting strategy. Neural Comput Appl 24:807\u2013816. \nhttps:\/\/doi.org\/10.1007\/s00521-012-1297-3","journal-title":"Neural Comput Appl"},{"key":"4152_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-013-1371-5","author":"S Yang","year":"2013","unstructured":"Yang S, Hou C, Zhang C (2013) Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-013-1371-5","journal-title":"Neural Comput Appl"},{"key":"4152_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-013-1517-5","author":"M Seera","year":"2014","unstructured":"Seera M, Peng C (2014) Transfer learning using the online fuzzy min\u2013max neural network. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-013-1517-5","journal-title":"Neural Comput Appl"},{"key":"4152_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2325-5","author":"M Silva","year":"2017","unstructured":"Silva M, Cardoso JS (2017) Multi-source deep transfer learning for cross-sensor biometrics. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-016-2325-5","journal-title":"Neural Comput Appl"},{"key":"4152_CR42","unstructured":"Khan A, Sohail A, Ali A (2018) A new channel boosted convolution neural network using transfer learning. \narXiv:180408528"},{"key":"4152_CR43","unstructured":"Khan A et al (2019) A survey of the recent architectures of deep convolutional neural networks. \narXiv:1901.06032"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04152-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04152-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04152-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,6]],"date-time":"2020-04-06T04:25:00Z","timestamp":1586147100000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04152-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,26]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["4152"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04152-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,26]]},"assertion":[{"value":"17 November 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}