{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:08:19Z","timestamp":1743030499172,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030373511"},{"type":"electronic","value":"9783030373528"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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\/978-3-030-37352-8_12","type":"book-chapter","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T20:03:00Z","timestamp":1577995380000},"page":"137-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Intrusion Detection Based on Semantic Re-encoding and Multi-space Projection"],"prefix":"10.1007","author":[{"given":"Jingjing","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zhendong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Zhang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2016.03.011","volume":"66","author":"G Folino","year":"2016","unstructured":"Folino, G., Sabatino, P.: Ensemble based collaborative and distributed intrusion detection systems: a survey. J. Netw. Comput. Appl. 66, 1\u201316 (2016)","journal-title":"J. Netw. Comput. Appl."},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jnca.2018.12.006","volume":"128","author":"N Moustafa","year":"2019","unstructured":"Moustafa, N., Hu, J., Slay, J.: A holistic review of network anomaly detection systems: a comprehensive survey. J. Netw. Comput. Appl. 128, 33\u201355 (2019)","journal-title":"J. Netw. Comput. Appl."},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/SURV.2013.052213.00046","volume":"16","author":"MH Bhuyan","year":"2013","unstructured":"Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutor. 16, 303\u2013336 (2013)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"10","key":"12_CR4","first-page":"1","volume":"2","author":"B Prabhu Kavin","year":"2017","unstructured":"Prabhu Kavin, B.: Data mining techniques for providing network security through intrusion detection systems: a survey. Int. J. Adv. Comput. Electron. Eng. 2(10), 1\u20136 (2017)","journal-title":"Int. J. Adv. Comput. Electron. Eng."},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.cose.2016.11.004","volume":"65","author":"AA Aburomman","year":"2017","unstructured":"Aburomman, A.A., Reaz, M.B.I.: A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput. Secur. 65, 135\u2013152 (2017)","journal-title":"Comput. Secur."},{"key":"12_CR6","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.jnca.2018.02.004","volume":"108","author":"JF Colom","year":"2018","unstructured":"Colom, J.F., Gil, D., Mora, H., Volckaert, B., Jimeno, A.M.: Scheduling framework for distributed intrusion detection systems over heterogeneous network architectures. J. Netw. Comput. Appl. 108, 76\u201386 (2018)","journal-title":"J. Netw. Comput. Appl."},{"key":"12_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jnca.2015.12.004","volume":"62","author":"S-Y Ji","year":"2016","unstructured":"Ji, S.-Y., Jeong, B.-K., Choi, S., Jeong, D.H.: A multi-level intrusion detection method for abnormal network behaviors. J. Netw. Comput. Appl. 62, 9\u201317 (2016)","journal-title":"J. Netw. Comput. Appl."},{"key":"12_CR8","doi-asserted-by":"publisher","first-page":"12060","DOI":"10.1109\/ACCESS.2017.2787719","volume":"6","author":"L Li","year":"2018","unstructured":"Li, L., Yu, Y., Bai, S., Hou, Y., Chen, X.: An effective two-step intrusion detection approach based on binary classification and k-NN. IEEE Access 6, 12060\u201312073 (2018)","journal-title":"IEEE Access"},{"key":"12_CR9","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.future.2017.01.029","volume":"79, Part 1","author":"E Kabir","year":"2018","unstructured":"Kabir, E., Hu, J., Wang, H., Zhuo, G.: A novel statistical technique for intrusion detection systems. Futur. Gener. Comput. Syst. 79, Part 1, 303\u2013318 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Blanco, R., Malagon, P., Cilla, J.J., Moya, J.M.: Multiclass network attack classifier using CNN tuned with genetic algorithms. In: 2018 28th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), pp. 177\u2013182. IEEE, Platja d\u2019Aro (2018)","DOI":"10.1109\/PATMOS.2018.8463997"},{"key":"12_CR11","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-06158-6_9","volume-title":"Wireless Internet","author":"C-M Hsu","year":"2019","unstructured":"Hsu, C.-M., Hsieh, H.-Y., Prakosa, S.W., Azhari, M.Z., Leu, J.-S.: Using long-short-term memory based convolutional neural networks for network intrusion detection. In: Chen, J.-L., Pang, A.-C., Deng, D.-J., Lin, C.-C. (eds.) WICON 2018. LNICST, vol. 264, pp. 86\u201394. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-06158-6_9"},{"key":"12_CR12","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/j.future.2017.08.043","volume":"82","author":"AA Diro","year":"2018","unstructured":"Diro, A.A., Chilamkurti, N.: Distributed attack detection scheme using deep learning approach for Internet of Things. Futur. Gener. Comput. Syst. 82, 761\u2013768 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"12_CR13","doi-asserted-by":"publisher","first-page":"48231","DOI":"10.1109\/ACCESS.2018.2863036","volume":"6","author":"S Naseer","year":"2018","unstructured":"Naseer, S., et al.: Enhanced network anomaly detection based on deep neural networks. IEEE Access 6, 48231\u201348246 (2018)","journal-title":"IEEE Access"},{"key":"12_CR14","doi-asserted-by":"publisher","first-page":"50850","DOI":"10.1109\/ACCESS.2018.2868993","volume":"6","author":"K Wu","year":"2018","unstructured":"Wu, K., Chen, Z., Li, W.: A novel intrusion detection model for a massive network using convolutional neural networks. IEEE Access 6, 50850\u201350859 (2018)","journal-title":"IEEE Access"},{"key":"12_CR15","doi-asserted-by":"publisher","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","volume":"5","author":"C Yin","year":"2017","unstructured":"Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954\u201321961 (2017)","journal-title":"IEEE Access"},{"key":"12_CR16","first-page":"50","volume":"46","author":"L Mohammadpour","year":"2018","unstructured":"Mohammadpour, L., Ling, T.C., Liew, C.S., Chong, C.Y.A.: Convolutional neural network for network intrusion detection system. Proc. Asia-Pacific Adv. Netw. 46, 50\u201355 (2018)","journal-title":"Proc. Asia-Pacific Adv. Netw."},{"key":"12_CR17","doi-asserted-by":"publisher","first-page":"52843","DOI":"10.1109\/ACCESS.2018.2869577","volume":"6","author":"M Al-Qatf","year":"2018","unstructured":"Al-Qatf, M., Lasheng, Y., Al-Habib, M., Al-Sabahi, K.: Deep learning approach combining sparse auto encoder with SVM for network intrusion detection. IEEE Access 6, 52843\u201352856 (2018)","journal-title":"IEEE Access"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Ingre, B., Yadav, A.: Performance analysis of NSL-KDD dataset using ANN. In: 2015 International Conference on Signal Processing and Communication Engineering Systems (SPACES), pp. 92\u201396. IEEE, Guntur (2015)","DOI":"10.1109\/SPACES.2015.7058223"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Lecture Notes in Computer Science","Cyberspace Safety and Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-37352-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T00:36:10Z","timestamp":1578011770000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-37352-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030373511","9783030373528"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-37352-8_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Cyberspace Safety and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"css2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/css2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"235","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}