{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:06:33Z","timestamp":1743073593528,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031299261"},{"type":"electronic","value":"9783031299278"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-29927-8_19","type":"book-chapter","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T12:02:50Z","timestamp":1680868970000},"page":"238-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Network Intrusions with Resilient Approaches Based on Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Fatin Neamah Ridha","family":"Al-Sarray","sequence":"first","affiliation":[]},{"given":"Maslina","family":"Zolkepli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,8]]},"reference":[{"issue":"8","key":"19_CR1","doi-asserted-by":"publisher","first-page":"356","DOI":"10.26483\/ijarcs.v8i8.4703","volume":"8","author":"DA Kumar","year":"2017","unstructured":"Kumar, D.A.: Intrusion detection systems: a review. Int. J. Adv. Res. Comput. Sci. 8(8), 356\u2013370 (2017)","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"115524","DOI":"10.1016\/j.eswa.2021.115524","volume":"185","author":"Y Imrana","year":"2021","unstructured":"Imrana, Y., Xiang, Y., Ali, L., Abdul-Rauf, Z.: A bidirectional LSTM deep learning approach for intrusion detection. Expert Syst. Appl. 185, 115524 (2021)","journal-title":"Expert Syst. Appl."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"102448","DOI":"10.1016\/j.cose.2021.102448","volume":"110","author":"Z Halim","year":"2021","unstructured":"Halim, Z., et al.: An effective genetic algorithm-based feature selection method for intrusion detection systems. Comput. Secur. 110, 102448 (2021)","journal-title":"Comput. Secur."},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.comcom.2021.08.026","volume":"180","author":"KN Rao","year":"2021","unstructured":"Rao, K.N., Rao, K.V., Prasad, P.R.: A hybrid intrusion detection system based on sparse autoencoder and deep neural network. Comput. Commun. 180, 77\u201388 (2021)","journal-title":"Comput. Commun."},{"key":"19_CR5","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.procs.2020.03.330","volume":"167","author":"A Thakkar","year":"2020","unstructured":"Thakkar, A., Lohiya, R.: A review of the advancement in intrusion detection datasets. Procedia Comput. Sci. 167, 636\u2013645 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"19_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.future.2021.11.030","volume":"130","author":"P Maniriho","year":"2022","unstructured":"Maniriho, P., Mahmood, A.N., Chowdhury, M.J.M.: A study on malicious software behaviour analysis and detection techniques: taxonomy, current trends and challenges. Futur. Gener. Comput. Syst. 130, 1\u201318 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"19_CR7","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-030-44289-7_23","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)","author":"ME El-Telbany","year":"2020","unstructured":"El-Telbany, M.E.: Prediction of the electrical load for Egyptian energy management systems: deep learning approach. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. (eds.) AICV 2020. AISC, vol. 1153, pp. 237\u2013246. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-44289-7_23"},{"key":"19_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/3806459","volume":"2021","author":"H Alkahtani","year":"2021","unstructured":"Alkahtani, H., Aldhyani, T.H.H.: Botnet attack detection by using CNN-LSTM model for internet of things applications. Secur. Commun. Netw. 2021, 1\u201323 (2021)","journal-title":"Secur. Commun. Netw."},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"5762","DOI":"10.1016\/j.egyr.2021.09.001","volume":"7","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Feng, J., Li, Y.: Non-intrusive load decomposition based on CNN\u2013LSTM hybrid deep learning model. Energy Rep. 7, 5762\u20135771 (2021)","journal-title":"Energy Rep."},{"issue":"4","key":"19_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.33851\/JMIS.2019.6.4.165","volume":"6","author":"J Kim","year":"2019","unstructured":"Kim, J., Shin, Y., Choi, E.: An intrusion detection model based on a convolutional neural network. J. Multimed. Inf. Syst. 6(4), 165\u2013172 (2019)","journal-title":"J. Multimed. Inf. Syst."},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.cose.2019.04.015","volume":"85","author":"C Xu","year":"2019","unstructured":"Xu, C., Shen, J., Du, X.: Detection method of domain names generated by DGAs based on semantic representation and deep neural network. Comp. Sec. 85, 77\u201388 (2019)","journal-title":"Comp. Sec."},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"163660","DOI":"10.1109\/ACCESS.2020.3019931","volume":"8","author":"M Haggag","year":"2020","unstructured":"Haggag, M., Tantawy, M.M., El-Soudani, M.M.S.: Implementing a deep learning model for intrusion detection on apache spark platform. IEEE Access 8, 163660\u2013163672 (2020)","journal-title":"IEEE Access"},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"102151","DOI":"10.1016\/j.cose.2020.102151","volume":"102","author":"SM Sohi","year":"2021","unstructured":"Sohi, S.M., Seifert, J.P., Ganji, F.: RNNIDS: enhancing network intrusion detection systems through deep learning. Comput. Secur. 102, 102151 (2021)","journal-title":"Comput. Secur."},{"issue":"3","key":"19_CR14","first-page":"1362","volume":"10","author":"P Kottapalle","year":"2020","unstructured":"Kottapalle, P.: A CNN-LSTM model for intrusion detection system from high dimensional data. J. Inf. Comput. Sci. 10(3), 1362\u20131370 (2020)","journal-title":"J. Inf. Comput. Sci."},{"key":"19_CR15","doi-asserted-by":"publisher","first-page":"99837","DOI":"10.1109\/ACCESS.2022.3206425","volume":"10","author":"A Halbouni","year":"2022","unstructured":"Halbouni, A., Gunawan, T.S., Habaebi, M.H., Halbouni, M., Kartiwi, M., Ahmad, R.: CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE Access 10, 99837\u201399849 (2022)","journal-title":"IEEE Access"},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"108498","DOI":"10.1016\/j.comnet.2021.108498","volume":"200","author":"RK Batchu","year":"2021","unstructured":"Batchu, R.K., Seetha, H.: A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning. Comput. Netw. 200, 108498 (2021)","journal-title":"Comput. Netw."},{"key":"19_CR17","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":"19_CR18","first-page":"e2","volume":"3","author":"Q Niyaz","year":"2015","unstructured":"Niyaz, Q., Sun, W., Javaid, A.Y., Alam, M.: A deep learning approach for network intrusion detection system. EAI Endorsed Tran. Secur. Saf. 3, e2 (2015)","journal-title":"EAI Endorsed Tran. Secur. Saf."}],"container-title":["Lecture Notes in Computer Science","Parallel and Distributed Computing, Applications and Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-29927-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T12:06:13Z","timestamp":1680869173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-29927-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031299261","9783031299278"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-29927-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PDCAT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel and Distributed Computing: Applications and Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sendai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pdcat2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.hpc.is.tohoku.ac.jp\/pdcat2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","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":"95","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":"24","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":"16","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":"25% - 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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}