{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:56:44Z","timestamp":1742968604082,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031294181"},{"type":"electronic","value":"9783031294198"}],"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\/978-3-031-29419-8_19","type":"book-chapter","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T05:45:49Z","timestamp":1680500749000},"page":"253-266","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On Feature Selection Algorithms for\u00a0Effective Botnet Detection"],"prefix":"10.1007","author":[{"given":"Meher","family":"Afroz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muntaka","family":"Ibnath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashikur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jakia","family":"Sultana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raqeebir","family":"Rab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,2]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"3457","DOI":"10.1007\/s12652-020-01848-9","volume":"13","author":"AA Ahmed","year":"2020","unstructured":"Ahmed, A.A., et al.: Deep learning-based classification model for botnet attack detection. J. Ambient Intell. Human. Comput. 13, 3457\u20133466 (2020). https:\/\/doi.org\/10.1007\/s12652-020-01848-9","journal-title":"J. Ambient Intell. Human. Comput."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Biglar Beigi, E., Hadian Jazi, H., Stakhanova, N., Ghorbani, A.A.: Towards effective feature selection in machine learning-based botnet detection approaches. In: IEEE Conference on Communications and Net, Security, pp. 247\u2013255 (2014)","DOI":"10.1109\/CNS.2014.6997492"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Chaudhary, P., Sherya, S., Vanshika, V.: Detection of botnet using flow analysis and clustering algorithm. Int. J. Mod. Edu. Comp. Sci. 11 (2019)","DOI":"10.5815\/ijmecs.2019.05.04"},{"issue":"1","key":"19_CR4","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.comnet.2011.07.018","volume":"56","author":"H Choi","year":"2012","unstructured":"Choi, H., Lee, H.: Identifying botnets by capturing group activities in DNS traffic. Comput. Netw. 56(1), 20\u201333 (2012)","journal-title":"Comput. Netw."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Faek, R., Al-Fawa\u2019reh, M., Al-Fayoumi, M.: Exposing bot attacks using machine learning and flow level analysis. In: International Conference on Data Science, E-learning and Information Systems (2021)","DOI":"10.1145\/3460620.3460739"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Garant, D., Lu, W.: Mining botnet behaviors on the large-scale web application community. In: 27th International Conference on Advanced Information Networking and Applications Workshops (2013)","DOI":"10.1109\/WAINA.2013.235"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Hossain, M.I., Eshrak, S., Auvik, M.J., Nasim, S.F., Rab, R., Rahman, A.: Efficient feature selection for detecting botnets based on network traffic and behavior analysis. In: 7th IEEE NSysS, 2020, pp. 56\u201362 (2020)","DOI":"10.1145\/3428363.3428378"},{"key":"19_CR8","first-page":"1","volume":"10","author":"TS Hyslip","year":"2015","unstructured":"Hyslip, T.S., Pittman, J.M.: A survey of botnet detection techniques by command and control infrastructure. J. Digit. Foren. Sec. Law 10, 1 (2015)","journal-title":"J. Digit. Foren. Sec. Law"},{"key":"19_CR9","unstructured":"John, W., Tafvelin, S.: Differences between in-and outbound internet backbone traffic. In: TERENA Networking Conference (TNC) (2007)"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Liao, W.H., Chang, C.C.: Peer to peer botnet detection using data mining scheme. In: International Conference on Internet Technology and Applications, pp. 1\u20134 (2010)","DOI":"10.1109\/ITAPP.2010.5566407"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Livadas, C., Walsh, R., Lapsley, D., Strayer, W.T.: Using machine learning techniques to identify botnet traffic. In: In IEEE LCN, pp. 967\u2013974 (2006)","DOI":"10.1109\/LCN.2006.322210"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Miller, S., Busby-Earle, C.: The role of machine learning in botnet detection. In: 11th International Conference for Internet Technology and Secured Transactions (ICITST), December 2016","DOI":"10.1109\/ICITST.2016.7856730"},{"key":"19_CR13","unstructured":"Morgan, S.: Cybercrime To Cost The World \\$10.5 Trillion Annually By 2025 (2020). https:\/\/cybersecurityventures.com\/hackerpocalypse-cybercrime-report-2016\/"},{"key":"19_CR14","unstructured":"Nivargi, V., Bhaowal, M., Lee, T.: Machine learning based botnet detection. CS 229 Final Proj. Report, Comput. Sci. Dep. Stanford Univ (2006)"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Saad, S., et al.: Detecting P2P botnets through network behavior analysis and machine learning. In: IEEE PST, pp. 174\u2013180 (2011)","DOI":"10.1109\/PST.2011.5971980"},{"key":"19_CR16","unstructured":"Stevanovic, M., Pedersen, J.M.: Machine learning for identifying botnet network traffic (2013)"},{"key":"19_CR17","first-page":"1","volume":"8","author":"E Stinson","year":"2008","unstructured":"Stinson, E., Mitchell, J.C.: Towards systematic evaluation of the evadability of bot\/botnet detection methods. WOOT 8, 1\u20139 (2008)","journal-title":"WOOT"},{"key":"19_CR18","series-title":"Advances in Information Security","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-0-387-68768-1_1","volume-title":"Botnet Detection","author":"WT Strayer","year":"2008","unstructured":"Strayer, W.T., Lapsely, D., Walsh, R., Livadas, C.: Botnet detection based on network behavior. In: Lee, W., Wang, C., Dagon, D. (eds.) Botnet Detection. Advances in Information Security, vol. 36, pp. 1\u201324. Springer, Boston (2008). https:\/\/doi.org\/10.1007\/978-0-387-68768-1_1"},{"issue":"11","key":"19_CR19","first-page":"2017","volume":"11","author":"F Tariq","year":"2017","unstructured":"Tariq, F., Baig, S.: Machine learning based botnet detection in software defined networks. Int. J. Secur. Appl 11(11), 2017 (2017)","journal-title":"Int. J. Secur. Appl"},{"key":"19_CR20","unstructured":"UNB: Iscx botnet dataset (2014). https:\/\/www.unb.ca\/cic\/datasets\/botnet.html"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Yu, X., Dong, X., Yu, G., Qin, Y., Yue, D.: Data-adaptive clustering analysis for online botnet detection. In: 2010, vol. 1 (2010)","DOI":"10.1109\/CSO.2010.214"},{"key":"19_CR22","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-642-30436-1_8","volume-title":"Information Security and Privacy Research","author":"D Zhao","year":"2012","unstructured":"Zhao, D., Traore, I., Ghorbani, A., Sayed, B., Saad, S., Lu, W.: Peer to peer botnet detection based on flow intervals. In: Gritzalis, D., Furnell, S., Theoharidou, M. (eds.) SEC 2012. IAICT, vol. 376, pp. 87\u2013102. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-30436-1_8"},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.cose.2013.04.007","volume":"39","author":"D Zhao","year":"2013","unstructured":"Zhao, D., et al.: Botnet detection based on traffic behavior analysis and flow intervals. Comput. Secur. 39, 2\u201316 (2013)","journal-title":"Comput. Secur."}],"container-title":["Lecture Notes in Computer Science","Ubiquitous Networking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-29419-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T05:49:11Z","timestamp":1680500951000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-29419-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031294181","9783031294198"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-29419-8_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":"2 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UNet","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Ubiquitous Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"25 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unet2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/unet-conf.org\/","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":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43","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":"17","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":"40% - 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":"2","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)"}},{"value":"4 invited papers","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)"}}]}}