{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:04:15Z","timestamp":1767373455197,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031342035"},{"type":"electronic","value":"9783031342042"}],"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\/978-3-031-34204-2_7","type":"book-chapter","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T23:04:18Z","timestamp":1686092658000},"page":"71-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["BotDroid: Permission-Based Android Botnet Detection Using Neural Networks"],"prefix":"10.1007","author":[{"given":"Saeed","family":"Seraj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elias","family":"Pimenidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michalis","family":"Pavlidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stelios","family":"Kapetanakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcello","family":"Trovati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolaos","family":"Polatidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"7_CR1","volume":"58","author":"JF Alqatawna","year":"2021","unstructured":"Alqatawna, J.F., Ala\u2019M, A. Z., Hassonah, M. A., & Faris, H.: Android botnet detection using machine learning models based on a comprehensive static analysis approach. Journal of Information Security and Applications 58, 102735 (2021)","journal-title":"Journal of Information Security and Applications"},{"doi-asserted-by":"crossref","unstructured":"Alothman, B.,  Rattadilok, P.: Android botnet detection: An integrated source code mining approach. In:\u00a02017 12th International Conference for Internet Technology and Secured Transactions (ICITST),\u00a0(pp. 111\u2013115) (2017, December). IEEE","key":"7_CR2","DOI":"10.23919\/ICITST.2017.8356358"},{"doi-asserted-by":"publisher","unstructured":"Hosseini, S., Nezhad, A.E., Seilani, H.: Botnet detection using negative selection algorithm, convolution neural network and classification methods. Evol. Syst. 13, 1\u201315 (2021). https:\/\/doi.org\/10.1007\/s12530-020-09362-1","key":"7_CR3","DOI":"10.1007\/s12530-020-09362-1"},{"doi-asserted-by":"crossref","unstructured":"Yusof, M., Saudi, M. M.,  Ridzuan, F.: Mobile botnet classification by using hybrid analysis.\u00a0In: International Journal of Engineering and Technology (UAE) (2018)","key":"7_CR4","DOI":"10.14419\/ijet.v7i4.15.21429"},{"issue":"2","key":"7_CR5","first-page":"32","volume":"3","author":"S Balasunthar","year":"2022","unstructured":"Balasunthar, S., Abdullah, Z.: Comparison of Convolutional Neural Network and Artificial Neural Network for Android Botnet Attack Detection. Applied Information Technology And Computer Science 3(2), 32\u201349 (2022)","journal-title":"Applied Information Technology And Computer Science"},{"doi-asserted-by":"crossref","unstructured":"Kothari, S.,  Joshi, S.: Analysis of Android Applications to Detect Botnet Attacks. In: 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC)\u00a0(pp. 144\u2013150) (2020, October). IEEE","key":"7_CR6","DOI":"10.1109\/ICSIDEMPC49020.2020.9299580"},{"doi-asserted-by":"crossref","unstructured":"Yusof, M., Saudi, M.M., Ridzuan, F.: A new mobile botnet classification based on permission and API calls. In: 2017 Seventh International Conference on Emerging Security Technologies (EST)\u00a0(pp. 122\u2013127) (2017, September). IEEE","key":"7_CR7","DOI":"10.1109\/EST.2017.8090410"},{"doi-asserted-by":"crossref","unstructured":"Anwar, S., Zain, J.M., Inayat, Z., Haq, R. U., Karim, A.,  Jabir, A.N.: A static approach towards mobile botnet detection. In: 2016 3rd International Conference on Electronic Design (ICED),\u00a0(pp. 563\u2013567) (2016, August). IEEE","key":"7_CR8","DOI":"10.1109\/ICED.2016.7804708"},{"doi-asserted-by":"crossref","unstructured":"Hojjatinia, S., Hamzenejadi, S.,  Mohseni, H.: Android botnet detection using convolutional neural networks. In: 2020 28th Iranian Conference on Electrical Engineering (ICEE),\u00a0(pp. 1\u20136)  (2020, August). IEEE","key":"7_CR9","DOI":"10.1109\/ICEE50131.2020.9260674"},{"issue":"3","key":"7_CR10","doi-asserted-by":"crossref","first-page":"486","DOI":"10.3390\/electronics11030486","volume":"11","author":"SY Yerima","year":"2022","unstructured":"Yerima, S.Y., Bashar, A.: A novel Android botnet detection system using image-based and manifest file features. Electronics 11(3), 486 (2022)","journal-title":"Electronics"},{"doi-asserted-by":"crossref","unstructured":"Yerima, S.Y.,  Bashar, A.:  Bot-IMG: A framework for image-based detection of Android botnets using machine learning. In: 2021 IEEE\/ACS 18th International Conference on Computer Systems and Applications (AICCSA),\u00a0(pp. 1\u20137), (2021, November). IEEE","key":"7_CR11","DOI":"10.1109\/AICCSA53542.2021.9686850"},{"unstructured":"Yusof, M., Saudi, M.M.,  Ridzuan, F.: Android Botnet Detection Using Risk Assessment","key":"7_CR12"},{"issue":"4","key":"7_CR13","doi-asserted-by":"crossref","first-page":"519","DOI":"10.3390\/electronics10040519","volume":"10","author":"SY Yerima","year":"2021","unstructured":"Yerima, S.Y., Alzaylaee, M.K., Shajan, A.: Deep learning techniques for android botnet detection. Electronics 10(4), 519 (2021)","journal-title":"Electronics"},{"doi-asserted-by":"crossref","unstructured":"Pieterse, H.,  Olivier, M.S.: Android botnets on the rise: Trends and characteristics. In: 2012 information security for South Africa\u00a0(pp. 1\u20135) (2012, August).. IEEE","key":"7_CR14","DOI":"10.1109\/ISSA.2012.6320432"},{"doi-asserted-by":"crossref","unstructured":"Tansettanakorn, C., Thongprasit, S., Thamkongka, S., & Visoottiviseth, V. (2016, May). ABIS: a prototype of android botnet identification system. In:\u00a02016 Fifth ICT International Student Project Conference (ICT-ISPC),\u00a0(pp. 1\u20135). IEEE","key":"7_CR15","DOI":"10.1109\/ICT-ISPC.2016.7519221"},{"key":"7_CR16","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106988","volume":"222","author":"M Moodi","year":"2021","unstructured":"Moodi, M., Ghazvini, M., Moodi, H.: A hybrid intelligent approach to detect android botnet using smart self-adaptive learning-based PSO-SVM. Knowl.-Based Syst. 222, 106988 (2021)","journal-title":"Knowl.-Based Syst."},{"doi-asserted-by":"crossref","unstructured":"da Costa, V.G., Barbon, S., Miani, R.S., Rodrigues, J.J.,  Zarpel\u00e3o, B.B.: Detecting mobile botnets through machine learning and system calls analysis. In: 2017 IEEE International Conference on Communications (ICC)\u00a0(pp. 1\u20136) (2017, May). IEEE","key":"7_CR17","DOI":"10.1109\/ICC.2017.7997390"},{"doi-asserted-by":"crossref","unstructured":"Girei, D.A., Shah, M.A.,  Shahid, M.B.: An enhanced botnet detection technique for mobile devices using log analysis. In: 2016 22nd International Conference on Automation and Computing (ICAC)\u00a0(pp. 450\u2013455)  (2016, September). IEEE","key":"7_CR18","DOI":"10.1109\/IConAC.2016.7604961"},{"issue":"1","key":"7_CR19","doi-asserted-by":"crossref","first-page":"127","DOI":"10.18280\/isi.250117","volume":"25","author":"MM Rasheed","year":"2020","unstructured":"Rasheed, M.M., Faieq, A.K., Hashim, A.A.: Android Botnet Detection Using Machine Learning. Ing\u00e9nierie des Syst\u00e8mes d Inf. 25(1), 127\u2013130 (2020)","journal-title":"Ing\u00e9nierie des Syst\u00e8mes d Inf."},{"doi-asserted-by":"crossref","unstructured":"Jadhav, S., Dutia, S., Calangutkar, K., Oh, T., Kim, Y. H., & Kim, J. N. (2015, July). Cloud-based android botnet malware detection system. In:\u00a02015 17th International Conference on Advanced Communication Technology (ICACT),\u00a0(pp. 347\u2013352). IEEE","key":"7_CR20","DOI":"10.1109\/ICACT.2015.7224817"},{"doi-asserted-by":"publisher","unstructured":"Seraj, S., Khodambashi, S., Pavlidis, M., Polatidis, N.: HamDroid: permission-based harmful android anti-malware detection using neural networks. Neural Comput. Appl. 34, 1 (2021). https:\/\/doi.org\/10.1007\/s00521-021-06755-4","key":"7_CR21","DOI":"10.1007\/s00521-021-06755-4"},{"doi-asserted-by":"crossref","unstructured":"Oh, T., Jadhav, S., Kim, Y.H.: Android botnet categorization and family detection based on behavioural and signature data. In: 2015 International Conference on Information and Communication Technology Convergence (ICTC)\u00a0(pp. 647\u2013652) (2015, October). IEEE","key":"7_CR22","DOI":"10.1109\/ICTC.2015.7354630"},{"doi-asserted-by":"crossref","unstructured":"Abdul Kadir, A.F., Stakhanova, N., &Ghorbani, A.A.:  Android botnets: What urls are telling us. In:\u00a0International Conference on Network and System Security\u00a0(pp. 78\u201391), (2015, November). Springer, Cham","key":"7_CR23","DOI":"10.1007\/978-3-319-25645-0_6"},{"doi-asserted-by":"crossref","unstructured":"Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K.,  Siemens, C.E.R.T.: Drebin: Effective and explainable detection of android malware in your pocket. In:\u00a0Ndss\u00a0(Vol. 14, pp. 23\u201326), (2014, February).","key":"7_CR24","DOI":"10.14722\/ndss.2014.23247"},{"unstructured":"Baruah, S. : Botnet detection: analysis of various techniques.\u00a0In: International Journal of Computational Intelligence & IoT\u00a02(2)","key":"7_CR25"},{"unstructured":"Yerima, S.Y.,  To, Y.: A deep learning-enhanced botnet detection system based on Android manifest text mining","key":"7_CR26"},{"unstructured":"VirusTotal. Free online virus, malware and URL scanner https:\/\/www.virustotal.com\/","key":"7_CR27"},{"unstructured":"https:\/\/www.kaggle.com\/datasets\/saeedseraj\/botdroid-android-botnet-detection\/","key":"7_CR28"}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34204-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T23:08:36Z","timestamp":1686092916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34204-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031342035","9783031342042"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34204-2_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Le\u00f3n","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2023\/","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":"Easyacademia","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","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":"41","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":"8","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":"33% - 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":"2.4","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":"3.2","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)"}}]}}