{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T23:37:07Z","timestamp":1775173027715,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030308582","type":"print"},{"value":"9783030308599","type":"electronic"}],"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\/978-3-030-30859-9_6","type":"book-chapter","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T12:03:26Z","timestamp":1568203406000},"page":"65-76","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Malware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualisation"],"prefix":"10.1007","author":[{"given":"Robert","family":"Shire","sequence":"first","affiliation":[]},{"given":"Stavros","family":"Shiaeles","sequence":"additional","affiliation":[]},{"given":"Keltoum","family":"Bendiab","sequence":"additional","affiliation":[]},{"given":"Bogdan","family":"Ghita","sequence":"additional","affiliation":[]},{"given":"Nicholas","family":"Kolokotronis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"key":"6_CR1","unstructured":"IOT Analytics. https:\/\/iot-analytics.com\/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b\/ . Accessed 02 Apr 2019"},{"key":"6_CR2","unstructured":"Anthony, O., John, O., Siman, E.: Intrusion detection in Internet of Things (IoT). Int. J. Adv. Res. Comput. 9(1) (2018)"},{"key":"6_CR3","unstructured":"Schneier on Security. https:\/\/www.schneier.com\/blog\/archives\/2018\/06\/e-mail_vulnerab.html . Accessed 02 Apr 2019"},{"key":"6_CR4","unstructured":"Symantec. https:\/\/www.symantec.com\/content\/dam\/symantec\/docs\/reports\/istr-23-2018-en.pdf . Accessed 02 Apr 2019"},{"key":"6_CR5","unstructured":"McAfee. https:\/\/securingtomorrow.mcafee.com\/consumer\/mobile-and-iot-security\/top-trending-iot-malware-attacks-of-2018\/ . Accessed 10 Mar 2019"},{"issue":"02","key":"6_CR6","first-page":"56","volume":"5","author":"E Gandotra","year":"2014","unstructured":"Gandotra, E., Bansal, D., Sofat, S.: Malware analysis and classification: a survey. J. Inf. Secur. 5(02), 56\u201364 (2014)","journal-title":"J. Inf. Secur."},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-642-19934-9_53","volume-title":"International Symposium on Distributed Computing and Artificial Intelligence","author":"I Santos","year":"2011","unstructured":"Santos, I., Nieves, J., Bringas, P.G.: Semi-supervised learning for unknown malware detection. In: Abraham, A., Corchado, J.M., Gonz\u00e1lez, S.R., De Paz Santana, J.F. (eds.) International Symposium on Distributed Computing and Artificial Intelligence, pp. 415\u2013422. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-19934-9_53"},{"issue":"1\u20132","key":"6_CR8","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.cose.2008.08.003","volume":"28","author":"P Garcia-Teodoro","year":"2009","unstructured":"Garcia-Teodoro, P., Diaz-Verdejo, J., Maci\u00e1-Fern\u00e1ndez, G., V\u00e1zquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1\u20132), 18\u201328 (2009)","journal-title":"Comput. Secur."},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Gao, N., Gao, L., Gao, Q., Wang, H.: An intrusion detection model based on deep belief networks. In: 2014 Second International Conference on Advanced Cloud and Big Data, IEEE, Huangshan, China, pp. 247\u2013252 (2014)","DOI":"10.1109\/CBD.2014.41"},{"issue":"1","key":"6_CR10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41\u201350 (2018)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Torres, P., Catania, C., Garcia, S., Garino, C.G.: An analysis of recurrent neural networks for botnet detection behavior. In: 2016 IEEE biennial congress of Argentina (ARGENCON), IEEE, pp. 1\u20136 (2016)","DOI":"10.1109\/ARGENCON.2016.7585247"},{"key":"6_CR12","unstructured":"Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN), IEEE, Da Nang, Vietnam, pp. 712\u2013717 (2017)"},{"key":"6_CR13","unstructured":"Bezerra, V.H., da Costa, V.G.T., Martins, R.A., Junior, S.B., Miani, R.S., Zarpelao, B.B.: Providing IoT host-based datasets for intrusion detection research. In: SBSeg 2018, SBC, pp. 15\u201328 (2018)"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Baptista, I., Shiaeles, S., Kolokotronis, N.: A Novel Malware Detection System Based On Machine Learning and Binary Visualization. arXiv preprint arXiv:1904.00859 (2019)","DOI":"10.1109\/ICCW.2019.8757060"},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jnca.2018.05.004","volume":"116","author":"D Zhou","year":"2018","unstructured":"Zhou, D., Yan, Z., Fu, Y., Yao, Z.: A survey on network data collection. J. Network Comput. Appl. 116, 9\u201323 (2018)","journal-title":"J. Network Comput. Appl."},{"key":"6_CR16","unstructured":"Python. Python.org , https:\/\/docs.python.org\/3\/library\/socket.html . Accessed 03 Jan 2019"},{"key":"6_CR17","unstructured":"binvis.io. http:\/\/binvis.io\/#\/ . Accessed 12 Mar 2019"},{"issue":"1","key":"6_CR18","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S0020-0190(97)00014-8","volume":"62","author":"HV Jagadish","year":"1997","unstructured":"Jagadish, H.V.: Analysis of the Hilbert curve for representing two-dimensional space. Inf. Process. Lett. 62(1), 17\u201322 (1997)","journal-title":"Inf. Process. Lett."},{"key":"6_CR19","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 265\u2013283 (2016)"},{"key":"6_CR20","unstructured":"G\u00e9ron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O\u2019Reilly Media, Inc. (2017)"},{"key":"6_CR21","unstructured":"MobileNet. https:\/\/ai.googleblog.com\/2017\/06\/mobilenets-open-source-models-for.html . Accessed 23 Feb 2019"},{"key":"6_CR22","unstructured":"Abdellatif. A.: Image Classification using Deep Neural Networks\u2014A beginner friendly approach using TensorFlow. https:\/\/medium.com\/@tifa2up\/image-classification-using-deep-neural-networks-a-beginner-friendly-approach-using-tensorflow-94b0a090ccd4 . Accessed 23 Feb 2019"},{"key":"6_CR23","unstructured":"McAfee. https:\/\/securingtomorrow.mcafee.com\/consumer\/consumer-threat-notices\/casinos-high-roller-database-iot-thermometer\/ . Accessed 15 May 2019"},{"key":"6_CR24","unstructured":"Huseby, S.H.: Common security problems in the code of dynamic web applications. Web Application Security Consortium (2005). www.webappsec.org"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Afianian, A., Niksefat, S., Sadeghiyan, B., Baptiste, D.: Malware Dynamic Analysis Evasion Techniques: A Survey. arXiv preprint arXiv:1811.01190 (2018)","DOI":"10.1145\/3365001"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"B\u00fcschkes, R., Laskov, P.: Detection of intrusions and malware and vulnerability assessment. In: Proceedings of Third International Conference DIMVA, pp. 13\u201314, July 2006","DOI":"10.1007\/11790754"},{"key":"6_CR27","unstructured":"Snort-IDS. https:\/\/www.snort.org\/ . Accessed 10 Mar 2019"},{"key":"6_CR28","unstructured":"Suricata. https:\/\/suricata-ids.org\/ . Accessed 10 Mar 2019"},{"key":"6_CR29","unstructured":"Roesch, M.: Lightweight intrusion detection for networks. In: Proceedings of LISA, vol. 99 (2005)"},{"key":"6_CR30","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.future.2017.10.016","volume":"80","author":"SAR Shah","year":"2018","unstructured":"Shah, S.A.R., Issac, B.: Performance comparison of intrusion detection systems and application of machine learning to Snort system. Future Gener. Comput. Syst. 80, 157\u2013170 (2018)","journal-title":"Future Gener. Comput. Syst."}],"container-title":["Lecture Notes in Computer Science","Internet of Things, Smart Spaces, and Next Generation Networks and Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30859-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,7]],"date-time":"2019-12-07T01:12:41Z","timestamp":1575681161000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30859-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030308582","9783030308599"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30859-9_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ruSMART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Internet of Things and Smart Spaces","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"St. Petersburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","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":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2019","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":"rusmart2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rusmart.e-werest.org\/2019.html","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":"50","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":"34% - 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":"6","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)"}}]}}