{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:41:28Z","timestamp":1769906488691,"version":"3.49.0"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030009786","type":"print"},{"value":"9783030009793","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,9,28]],"date-time":"2018-09-28T00:00:00Z","timestamp":1538092800000},"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":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-00979-3_15","type":"book-chapter","created":{"date-parts":[[2018,9,27]],"date-time":"2018-09-27T12:55:41Z","timestamp":1538052941000},"page":"146-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System"],"prefix":"10.1007","author":[{"given":"Mohammed Hasan","family":"Ali","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Fadli","family":"Zolkipli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,28]]},"reference":[{"key":"15_CR1","unstructured":"Buczak, A., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutorials, vol. PP, no. 99, p. 1, 2015"},{"issue":"1","key":"15_CR2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jnca.2012.08.007","volume":"36","author":"Ahmed Patel","year":"2013","unstructured":"Patel, A., Taghavi, M., Bakhtiyari, K., Celestino Jr J.: An intrusion detection and prevention system in cloud computing: a systematic review. J. Netw. Comput. Appl. 36(1), 25\u201341 (2013)","journal-title":"Journal of Network and Computer Applications"},{"issue":"1","key":"15_CR3","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.jnca.2012.09.004","volume":"36","author":"Hung-Jen Liao","year":"2013","unstructured":"Liao, H.-J., Lin, C.-H.R., Lin, Y.-C.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16\u201324 (2012)","journal-title":"Journal of Network and Computer Applications"},{"issue":"1","key":"15_CR4","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.jnca.2012.09.004","volume":"36","author":"HJ Liao","year":"2013","unstructured":"Liao, H.J., Richard Lin, C.H., Lin, Y.C., Tung, K.Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16\u201324 (2013)","journal-title":"J. Netw. Comput. Appl."},{"issue":"10","key":"15_CR5","doi-asserted-by":"publisher","first-page":"11994","DOI":"10.1016\/j.eswa.2009.05.029","volume":"36","author":"C Tsai","year":"2009","unstructured":"Tsai, C., Hsu, Y., Lin, C., Lin, W.: Expert systems with applications intrusion detection by machine learning: a review. Expert Syst. Appl. 36(10), 11994\u201312000 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"15_CR6","doi-asserted-by":"publisher","first-page":"4062","DOI":"10.1016\/j.eswa.2014.12.040","volume":"42","author":"JM Fossaceca","year":"2015","unstructured":"Fossaceca, J.M., Mazzuchi, T.A., Sarkani, S.: MARK-ELM: Application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection. Expert Syst. Appl. 42(8), 4062\u20134080 (2015)","journal-title":"Expert Syst. Appl."},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jnca.2016.10.015","volume":"77","author":"Preeti Mishra","year":"2017","unstructured":"Mishra, P., Pilli, E.S., Varadharajan, V., Tupakula, U.: Intrusion detection techniques in cloud environment: a survey. J. Netw. Comput. Appl. 77, pp. 18\u201347, October 2016","journal-title":"Journal of Network and Computer Applications"},{"issue":"14","key":"15_CR8","first-page":"38","volume":"54","author":"V Jaiganesh","year":"2012","unstructured":"Jaiganesh, V., Sumathi, P.: Kernelized extreme learning machine with levenberg-marquardt learning approach towards intrusion detection. Int. J. Comput. Appl. 54(14), 38\u201344 (2012)","journal-title":"Int. J. Comput. Appl."},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Udaya Sampath, X.W., Perera Miriya Thanthrige, K., Samarabandu, J.: Machine learning techniques for intrusion detection. IEEE Can. Conf. Electr. Comput. Eng. 1\u201310 (2016)","DOI":"10.1109\/CCECE.2016.7726677"},{"issue":"6","key":"15_CR10","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s00521-015-1964-2","volume":"27","author":"BM Aslahi-Shahri","year":"2016","unstructured":"Aslahi-Shahri, B.M., et al.: A hybrid method consisting of GA and SVM for intrusion detection system. Neural Comput. Appl. 27(6), 1669\u20131676 (2016)","journal-title":"Neural Comput. Appl."},{"key":"15_CR11","unstructured":"Atefi, K., Yahya, S., Dak, A.Y., Atefi, A.: A Hybrid Intrusion detection system based on differen machine learning algorithms. In: Proceedings of the 4th International Conference Computing Informatics, no. 22, pp. 312\u2013320 (2013)"},{"issue":"3\u20134","key":"15_CR12","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s00521-013-1522-8","volume":"25","author":"S Ding","year":"2014","unstructured":"Ding, S., Xu, X., Nie, R.: Extreme learning machine and its applications. Neural Comput. Appl. 25(3\u20134), 549\u2013556 (2014)","journal-title":"Neural Comput. Appl."},{"issue":"22","key":"15_CR13","doi-asserted-by":"publisher","first-page":"8609","DOI":"10.1016\/j.eswa.2015.07.015","volume":"42","author":"R Singh","year":"2015","unstructured":"Singh, R., Kumar, H., Singla, R.K.: An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Syst. Appl. 42(22), 8609\u20138624 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"16","key":"15_CR14","first-page":"4180","volume":"12","author":"MH Ali","year":"2017","unstructured":"Ali, M.H., Zolkipli, M.F., Mohammed, M.A., Jaber, M.M.: Enhance of extreme learning machine-genetic algorithm hybrid based on intrusion detection system. J. Eng. Appl. Sci. 12(16), 4180\u20134185 (2017)","journal-title":"J. Eng. Appl. Sci."},{"issue":"2","key":"15_CR15","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s12293-016-0182-5","volume":"9","author":"H Lu","year":"2017","unstructured":"Lu, H., Du, B., Liu, J., Xia, H., Yeap, W.K.: A kernel extreme learning machine algorithm based on improved particle swam optimization. Memetic Comput. 9(2), 121\u2013128 (2017)","journal-title":"Memetic Comput."},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. man, Cybern. Part B, Cybern. 42, (2), 513\u2013529 (2012)","DOI":"10.1109\/TSMCB.2011.2168604"},{"issue":"9","key":"15_CR17","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","volume":"4","author":"M Pal","year":"2013","unstructured":"Pal, M., Maxwell, A.E., Warner, T.A.: Kernel-based extreme learning machine for remote-sensing image classification. Remote Sens. Lett. 4(9), 853\u2013862 (2013)","journal-title":"Remote Sens. Lett."},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.neucom.2012.11.053","volume":"128","author":"B Liu","year":"2014","unstructured":"Liu, B., Tang, L., Wang, J., Li, A., Hao, Y.: 2-D defect profile reconstruction from ultrasonic guided wave signals based on QGA-kernelized ELM. Neurocomputing 128, 217\u2013223 (2014)","journal-title":"Neurocomputing"},{"key":"15_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2014.01.008","volume":"53","author":"WY Deng","year":"2014","unstructured":"Deng, W.Y., Zheng, Q.H., Wang, Z.M.: Cross-person activity recognition using reduced kernel extreme learning machine. Neural Netw. 53, 1\u20137 (2014)","journal-title":"Neural Netw."},{"key":"15_CR20","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.neucom.2015.07.138","volume":"184","author":"Hui-Ling Chen","year":"2016","unstructured":"Chen, H.L., Wang, G., Ma, C., Cai, Z.N., Bin Liu, W., Wang, S. J.: An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson\u2019s disease. Neurocomputing 184, 131\u2013144 (2016)","journal-title":"Neurocomputing"},{"issue":"6","key":"15_CR21","doi-asserted-by":"publisher","first-page":"5795","DOI":"10.3390\/rs6065795","volume":"6","author":"C Chen","year":"2014","unstructured":"Chen, C., Li, W., Su, H., Liu, K.: Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sens. 6(6), 5795\u20135814 (2014)","journal-title":"Remote Sens."},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Fu, H., Vong, C.-M., Wong, P.-K., Yang, Z.: Fast detection of impact location using kernel extreme learning machine. Neural Comput. Appl. 1\u201310 (2014)","DOI":"10.1007\/s00521-014-1568-2"},{"key":"15_CR23","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.1016\/j.neucom.2017.09.004","volume":"275","author":"Lu Li","year":"2018","unstructured":"Li, L., Wang, C., Li, W., Chen, J.: Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines. Neurocomputing 275, 1725\u20131733 (2018)","journal-title":"Neurocomputing"},{"key":"15_CR24","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.bspc.2017.06.015","volume":"38","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Wang, A.N., Ai, Q., Sun, H.J.: An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson\u2019s disease. Biomed. Signal Process. Control 38, 400\u2013410 (2017)","journal-title":"Biomed. Signal Process. Control"},{"key":"15_CR25","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2017.04.060","volume":"267","author":"M Wang","year":"2017","unstructured":"Wang, M., et al.: Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267, 69\u201384 (2017)","journal-title":"Neurocomputing"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Niu, P., Li, G.: An adaptive extreme learning machine for modeling NOx emission of a 300\u00a0MW circulating fluidized bed boiler (2017)","DOI":"10.1007\/s11063-017-9611-9"},{"issue":"1","key":"15_CR27","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s00500-014-1486-3","volume":"20","author":"Guoqiang Li","year":"2014","unstructured":"Li, G., Niu, P.: Combustion optimization of a coal-fired boiler with double linear fast learning network (2014)","journal-title":"Soft Computing"},{"issue":"6","key":"15_CR28","doi-asserted-by":"publisher","first-page":"7067","DOI":"10.1016\/j.eswa.2010.12.006","volume":"38","author":"MS Abadeh","year":"2011","unstructured":"Abadeh, M.S., Mohamadi, H., Habibi, J.: Design and analysis of genetic fuzzy systems for intrusion detection in computer networks. Expert Syst. Appl. 38(6), 7067\u20137075 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"13","key":"15_CR29","doi-asserted-by":"publisher","first-page":"5972","DOI":"10.1016\/j.eswa.2014.04.009","volume":"41","author":"T Chen","year":"2014","unstructured":"Chen, T., Zhang, X., Jin, S., Kim, O.: Efficient classification using parallel and scalable compressed model and its application on intrusion detection. Expert Syst. Appl. 41(13), 5972\u20135983 (2014)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"15_CR30","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.1016\/j.eswa.2010.11.028","volume":"38","author":"V Bol\u00f3n-Canedo","year":"2011","unstructured":"Bol\u00f3n-Canedo, V., S\u00e1nchez-Maro\u00f1o, N., Alonso-Betanzos, A.: Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset. Expert Syst. Appl. 38(5), 5947\u20135957 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"15_CR31","first-page":"405","volume":"12","author":"R Mitchell","year":"2014","unstructured":"Mitchell, R., Chen, I.-R.: A survey of intrusion detection techniques. Comput. Secur. 12(4), 405\u2013418 (2014)","journal-title":"Comput. Secur."},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications CISDA 2009 (June 2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"2","key":"15_CR33","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3233\/IDA-2010-0466","volume":"15","author":"V Engen","year":"2011","unstructured":"Engen, V., Vincent, J., Phalp, K.: Exploring discrepancies in findings obtained with the KDD Cup\u201999 data set. Intell. Data Anal. 15(2), 251\u2013276 (2011)","journal-title":"Intell. Data Anal."},{"issue":"1","key":"15_CR34","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/TCYB.2013.2247592","volume":"44","author":"W Hu","year":"2014","unstructured":"Hu, W., Gao, J., Wang, Y., Wu, O., Maybank, S.: Online adaboost-based parameterized methods for dynamic distributed network intrusion detection. IEEE Trans. Cybern. 44(1), 66\u201382 (2014)","journal-title":"IEEE Trans. Cybern."},{"key":"15_CR35","unstructured":"Weller-Fahy, D.J.: Network intrusion dataset assessment, p. 114 (2013)"},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"Chou, T.-S., Fan, J., Fan, S., Makki, K.: Ensemble of machine learning algorithms for intrusion detection. In: 2009 IEEE International Conference System Man and Cybernetics, pp. 3976\u20133980 (2009)","DOI":"10.1109\/ICSMC.2009.5346669"},{"issue":"7\u20138","key":"15_CR37","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1007\/s00521-013-1398-7","volume":"24","author":"G Li","year":"2014","unstructured":"Li, G., Niu, P., Duan, X., Zhang, X.: Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput. Appl. 24(7\u20138), 1683\u20131695 (2014)","journal-title":"Neural Comput. Appl."},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"Guang-Bin, H., Qin-Yu, Z., Chee-Kheong, S.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference, vol. 2, pp. 985\u2013990. August 2004","DOI":"10.1109\/IJCNN.2004.1380068"},{"key":"15_CR39","doi-asserted-by":"crossref","unstructured":"Smola, A.J., Sch\u00f6lkopf, B.: Learning with Kernels. February 2002","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"15_CR40","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/978-3-319-00969-8_80","volume-title":"Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013","author":"Helen Flynn","year":"2013","unstructured":"Flynn, H., Cameron, S.: Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, vol. 226 (2013)"},{"issue":"6","key":"15_CR41","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/S0020-0190(02)00447-7","volume":"85","author":"IC Trelea","year":"2003","unstructured":"Trelea, I.C.: The particle swarm optimization algorithm: Convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317\u2013325 (2003)","journal-title":"Inf. Process. Lett."},{"key":"15_CR42","unstructured":"J. Blondin, \u201cParticle swarm optimization: A tutorial,\u201d \u2026 Site Http\/\/Cs. Armstrong. Edu\/Saad\/Csci8100\/Pso Tutor. \u2026, pp. 1\u20135, 2009"},{"issue":"4","key":"15_CR43","first-page":"660","volume":"36","author":"A Sengupta","year":"2017","unstructured":"Sengupta, A., Bhadauria, S., Mohanty, S.P.: TL-HLS: Methodology for Low Cost Hardware Trojan Security Aware Scheduling with Optimal Loop Unrolling Factor during High Level Synthesis. IEEE Trans. Comput. Des. Integr. Circuits Syst. 36(4), 660\u2013673 (2017)","journal-title":"IEEE Trans. Comput. Des. Integr. Circuits Syst."},{"issue":"2","key":"15_CR44","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1049\/el.2014.3507","volume":"51","author":"V.K. Mishra","year":"2015","unstructured":"Mishra, V.K., Sengupta, A.: Swarm-inspired exploration of architecture and unrolling factors for nested-loop-based application in architectural synthesis. Electron. Lett. 51(2), 157\u2013159 (2015)","journal-title":"Electronics Letters"},{"key":"15_CR45","doi-asserted-by":"crossref","unstructured":"Sengupta, A., Bhadauria, S.: User power-delay budget driven PSO based design space exploration of optimal k-cycle transient fault secured datapath during high level synthesis. In: Proceedings of the International Symposium Quality Electronic Design ISQED, vol. 2015, no. 6, pp. 289\u2013292 (2015)","DOI":"10.1109\/ISQED.2015.7085441"},{"key":"15_CR46","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.advengsoft.2013.09.001","volume":"67","author":"VK Mishra","year":"2014","unstructured":"Mishra, V.K., Sengupta, A.: MO-PSE: Adaptive multi-objective particle swarm optimization based design space exploration in architectural synthesis for application specific processor design. Adv. Eng. Softw. 67, 111\u2013124 (2014)","journal-title":"Adv. Eng. Softw."},{"key":"15_CR47","unstructured":"Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp. 69\u201373 (1998)"}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Computing &amp; Optimization"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00979-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T20:43:34Z","timestamp":1720644214000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-00979-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,28]]},"ISBN":["9783030009786","9783030009793"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00979-3_15","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,28]]},"assertion":[{"value":"ICO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing & Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pattaya","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ico2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icico.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}