{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:26:52Z","timestamp":1743046012606,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030727918"},{"type":"electronic","value":"9783030727925"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72792-5_20","type":"book-chapter","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T20:34:22Z","timestamp":1619469262000},"page":"215-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Performance Analysis Approach for Network Intrusion Detection Algorithms"],"prefix":"10.1007","author":[{"given":"Zhihao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Dingde","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yuqing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Junyang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Xie, J., Li, S., Zhang, Y., et al.: A method based on hierarchical spatiotemporal features for trojan traffic detection. In: 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), pp. 1\u20138 (2019)","DOI":"10.1109\/IPCCC47392.2019.8958768"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Li, Z., Batta, P., Trajkovic, L.: Comparison of machine learning algorithms for detection of network intrusions. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4248\u20134253 (2018)","DOI":"10.1109\/SMC.2018.00719"},{"key":"20_CR3","doi-asserted-by":"publisher","unstructured":"Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mob. Netw. Appl. (2019). https:\/\/doi.org\/10.1007\/s11036-019-01424-2, online available","DOI":"10.1007\/s11036-019-01424-2"},{"key":"20_CR4","doi-asserted-by":"publisher","unstructured":"Wang, Y., Jiang, D., Huo, L., Zhao, Y.: A new traffic prediction algorithm to software defined networking. Mob. Netw. Appl. (2019). https:\/\/doi.org\/10.1007\/s11036-019-01423-3. online available","DOI":"10.1007\/s11036-019-01423-3"},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"33789","DOI":"10.1109\/ACCESS.2018.2841987","volume":"6","author":"I Ahmad","year":"2018","unstructured":"Ahmad, I., Basheri, M., Iqbal, M.J., et al.: Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6, 33789\u201333795 (2018)","journal-title":"IEEE Access"},{"issue":"5","key":"20_CR6","first-page":"1","volume":"13","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE 13(5), 1\u201323 (2018)","journal-title":"PLoS ONE"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Cosar, M., Kiran, H.E.: Performance comparison of open source IDSs via Raspberry Pi. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1\u20135 (2018)","DOI":"10.1109\/IDAP.2018.8620784"},{"issue":"1","key":"20_CR8","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/TNSE.2018.2861388","volume":"7","author":"D Jiang","year":"2020","unstructured":"Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 7(1), 80\u201390 (2020)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"70651","DOI":"10.1109\/ACCESS.2020.2986217","volume":"8","author":"S Sarvari","year":"2020","unstructured":"Sarvari, S., Sani, N.F.M., Hanapi, Z.M., et al.: An efficient anomaly intrusion detection method with feature selection and evolutionary neural network. IEEE Access 8, 70651\u201370663 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"20_CR10","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1109\/TNSE.2018.2877597","volume":"7","author":"D Jiang","year":"2020","unstructured":"Jiang, D., Wang, W., Shi, L., Song, H.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. 7(1), 507\u2013519 (2020)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Chiba, Z., Abghour, N., Moussaid, K., et al.: A hybrid optimization framework based on genetic algorithm and simulated annealing algorithm to enhance performance of anomaly network intrusion detection system based on BP neural network. In: 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp. 1\u20136 (2018)","DOI":"10.1109\/ISAECT.2018.8618804"},{"issue":"220","key":"20_CR12","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.neucom.2016.07.056","volume":"2017","author":"D Jiang","year":"2017","unstructured":"Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 2017(220), 160\u2013169 (2017)","journal-title":"Neurocomputing"},{"key":"20_CR13","doi-asserted-by":"publisher","first-page":"64366","DOI":"10.1109\/ACCESS.2019.2917299","volume":"7","author":"H Yang","year":"2019","unstructured":"Yang, H., Wang, F.: Wireless network intrusion detection based on improved convolutional neural network. IEEE Access 7, 64366\u201364374 (2019)","journal-title":"IEEE Access"},{"issue":"6","key":"20_CR14","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/JIOT.2016.2613111","volume":"3","author":"D Jiang","year":"2016","unstructured":"Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things J. 3(6), 1437\u20131447 (2016)","journal-title":"IEEE Internet of Things J."},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Singh, K., Mathai, K.J.: Performance comparison of intrusion detection system between deep belief network (DBN) algorithm and state preserving extreme learning machine (SPELM) algorithm. In: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1\u20137 (2019)","DOI":"10.1109\/ICECCT.2019.8869492"},{"issue":"5","key":"20_CR16","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/JSAC.2020.2980919","volume":"38","author":"D Jiang","year":"2020","unstructured":"Jiang, D., Wang, Y., Lv, Z., Wang, W., Wang, H.: An energy-efficient networking approach in cloud services for IIoT networks. IEEE J. Sel. Areas Commun. 38(5), 928\u2013941 (2020)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"20_CR17","doi-asserted-by":"publisher","first-page":"31711","DOI":"10.1109\/ACCESS.2019.2903723","volume":"7","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Li, P., Wang, X.: Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access 7, 31711\u201331722 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"20_CR18","first-page":"196","volume":"7","author":"F Wang","year":"2019","unstructured":"Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. China Commun. 7(1), 196\u2013207 (2019)","journal-title":"China Commun."},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Liu, W., Liu, X., Di, X., et al.: A novel network intrusion detection algorithm based on fast fourier transformation. In: 2019 1st International Conference on Industrial Artificial Intelligence (IAI), pp. 1\u20136 (2019)","DOI":"10.1109\/ICIAI.2019.8850770"},{"issue":"2","key":"20_CR20","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TII.2019.2930226","volume":"16","author":"D Jiang","year":"2020","unstructured":"Jiang, D., Wang, Y., Lv, Z., Qi, S., Singh, S.: Big data analysis based network behavior insight of cellular networks for Industry 4.0 applications. IEEE Trans. Ind. Inf. 16(2), 1310\u20131320 (2020)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"3","key":"20_CR21","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1109\/TII.2019.2946791","volume":"16","author":"W Liang","year":"2020","unstructured":"Liang, W., Li, K., Long, J., et al.: An industrial network intrusion detection algorithm based on multifeature data clustering optimization model. IEEE Trans. Industr. Inf. 16(3), 2063\u20132071 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"10","key":"20_CR22","doi-asserted-by":"publisher","first-page":"3305","DOI":"10.1109\/TITS.2017.2778939","volume":"19","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Lv, Z., Song, H., Qin, W.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305\u20133319 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Khan, R.U., Zhang, X., Alazab, M., et al.: An improved convolutional neural network model for intrusion detection in networks. In: 2019 Cybersecurity and Cyberforensics Conference (CCC), pp. 74\u201377 (2019)","DOI":"10.1109\/CCC.2019.000-6"},{"key":"20_CR24","doi-asserted-by":"publisher","unstructured":"Huo, L., Jiang, D., Qi, S., et al.: An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mob. Netw. Appl. (2019). https:\/\/doi.org\/10.1007\/s11036-019-01419-z. online available","DOI":"10.1007\/s11036-019-01419-z"},{"issue":"10","key":"20_CR25","doi-asserted-by":"publisher","first-page":"2490","DOI":"10.1109\/TIFS.2018.2819967","volume":"13","author":"E Miehling","year":"2018","unstructured":"Miehling, E., Rasouli, M., Teneketzis, D.: A POMDP approach to the dynamic defense of large-scale cyber networks. IEEE Trans. Inf. Forensics Secur. 13(10), 2490\u20132505 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Simulation Tools and Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72792-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T22:03:44Z","timestamp":1619474624000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72792-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030727918","9783030727925"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72792-5_20","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMUtools","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Simulation Tools and Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guiyang","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simutools2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/simutools.eai-conferences.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"354","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":"125","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":"0","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":"35% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID 19 pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}