{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:08:25Z","timestamp":1742940505816,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687335"},{"type":"electronic","value":"9783030687342"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68734-2_5","type":"book-chapter","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T01:14:09Z","timestamp":1612833249000},"page":"78-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Partial Approach to Intrusion Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4212-0329","authenticated-orcid":false,"given":"John","family":"Sheppard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,7]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"Du, X., et al.: SoK: exploring the state of the art and the future potential of artificial intelligence in digital forensic investigation. In: Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES 2020. ACM, August 2020. https:\/\/doi.org\/10.1145\/3407023.3407068, ISBN: 9781450388337","DOI":"10.1145\/3407023.3407068"},{"key":"5_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-662-56266-6_4","volume-title":"Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVI","author":"N Nguyen Thi","year":"2017","unstructured":"Nguyen Thi, N., Cao, V.L., Le-Khac, N.-A.: One-class collective anomaly detection based on LSTM-RNNs. In: Hameurlain, A., K\u00fcng, J., Wagner, R., Dang, T.K., Thoai, N. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVI. LNCS, vol. 10720, pp. 73\u201385. Springer, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-3-662-56266-6_4"},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jnca.2018.12.006","volume":"128","author":"N Moustafa","year":"2019","unstructured":"Moustafa, N., Hu, J., Slay, J.: A holistic review of network anomaly detection systems: a comprehensive survey. J. Netw. Comput. Appl. 128, 33\u201355 (2019)","journal-title":"J. Netw. Comput. Appl."},{"key":"5_CR4","unstructured":"Othman, S., Alsohybe, N., Ba-Alwi, F., Zahar, A.: Survey on intrusion detection system. Int. J. Cyber-Secur. Digital Forensics (IJCSDF) (2018). ISSN: 2305\u2013001"},{"issue":"2","key":"5_CR5","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"A Buczak","year":"2016","unstructured":"Buczak, A., Guven, E.: A survey of data mining and machine learning methods for cybersecurity intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153\u20131176 (2016). https:\/\/doi.org\/10.1109\/COMST.2015.2494502","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","volume":"60","author":"M Ahmed","year":"2016","unstructured":"Ahmed, M., Naser, A., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19\u201331 (2016). https:\/\/doi.org\/10.1016\/j.jnca.2015.11.016. ISSN: 1084\u20138045","journal-title":"J. Netw. Comput. Appl."},{"issue":"11","key":"5_CR7","first-page":"947","volume":"6","author":"U Modi","year":"2015","unstructured":"Modi, U., Jain, A.: A survey of IDS classification using KDD cup 99 dataset in WEKA. Int. J. Sci. Eng. Res. 6(11), 947\u2013954 (2015). ISSN 2229\u20135518","journal-title":"Int. J. Sci. Eng. Res."},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jocs.2017.03.006","volume":"25","author":"S Aljawarneh","year":"2018","unstructured":"Aljawarneh, S., Aldwairi, M., Yassein, M.: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J. Comput. Sci. 25, 152\u2013160 (2018). https:\/\/doi.org\/10.1016\/j.jocs.2017.03.006","journal-title":"J. Comput. Sci."},{"issue":"10","key":"5_CR9","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TC.2016.2519914","volume":"65","author":"M Ambusaidi","year":"2016","unstructured":"Ambusaidi, M., He, X., Nanda, P., Tan, Z.: Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans. Comput. 65(10), 2986\u20132998 (2016). https:\/\/doi.org\/10.1109\/TC.2016.2519914","journal-title":"IEEE Trans. Comput."},{"key":"5_CR10","first-page":"129","volume":"7","author":"M Hasan","year":"2016","unstructured":"Hasan, M., Nasser, S., Ahmad, M., Molla, K.: Feature selection for intrusion detection using random forest. J. Inf. Secur. 7, 129\u2013140 (2016)","journal-title":"J. Inf. Secur."},{"key":"5_CR11","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-319-91341-4_9","volume-title":"Evolutionary and Swarm Intelligence Algorithms","author":"S Elhag","year":"2019","unstructured":"Elhag, S., Fern\u00e1ndez, A., Alshomrani, S., Herrera, F.: Evolutionary fuzzy systems: a case study for intrusion detection systems. In: Bansal, J.C., Singh, P.K., Pal, N.R. (eds.) Evolutionary and Swarm Intelligence Algorithms. SCI, vol. 779, pp. 169\u2013190. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-91341-4_9"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Denning, D.: An intrusion-detection model. In IEEE Trans. Softw. Eng., Piscataway, NJ, USA, vol. 13, pp. 222\u2013232. IEEE Press, February 1987. https:\/\/doi.org\/10.1109\/TSE.1987.232894","DOI":"10.1109\/TSE.1987.232894"},{"key":"5_CR13","unstructured":"Scarfone, K., Mell, P.: 800\u201394 rev-1. NIST Guide to Intrusion Detection and Prevention Systems (IDPS) Revision, vol. 1 (2012)"},{"issue":"4","key":"5_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/604264.604267","volume":"30","author":"S Stolfo","year":"2001","unstructured":"Stolfo, S., Lee, W., Chan, P., Fan, W., Eskin, E.: Data mining-based intrusion detectors: an overview of the Columbia ids project. ACM SIGMOD Rec. 30(4), 5\u201314 (2001)","journal-title":"ACM SIGMOD Rec."},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Gharib, A., Sharafaldin, I., Lashkari, A., Ghorbani, A.: An evaluation framework for intrusion detection dataset. In: 2016 International Conference on Information Science and Security (ICISS), pp. 1\u20136, December 2016. https:\/\/doi.org\/10.1109\/ICISSEC.2016.7885840","DOI":"10.1109\/ICISSEC.2016.7885840"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Sharafaldin, I., Lashkari, A., Ghorbani, A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, pp. 108\u2013116. INSTICC, SciTePress (2018). https:\/\/doi.org\/10.5220\/0006639801080116, ISBN: 978-989-758-282-0","DOI":"10.5220\/0006639801080116"},{"key":"5_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-030-23502-4_12","volume-title":"Cloud Computing \u2013 CLOUD 2019","author":"P Lin","year":"2019","unstructured":"Lin, P., Ye, K., Xu, C.-Z.: Dynamic network anomaly detection system by using deep learning techniques. In: Da Silva, D., Wang, Q., Zhang, L.-J. (eds.) CLOUD 2019. LNCS, vol. 11513, pp. 161\u2013176. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-23502-4_12"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Aksu, D., Aydin, M.: Detecting port scan attempts with comparative analysis of deep learning and support vector machine algorithms. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 77\u201380, December 2018. https:\/\/doi.org\/10.1109\/IBIGDELFT.2018.8625370","DOI":"10.1109\/IBIGDELFT.2018.8625370"},{"key":"5_CR19","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-030-00840-6_16","volume-title":"Computer and Information Sciences","author":"D Aksu","year":"2018","unstructured":"Aksu, D., \u00dcstebay, S., Aydin, M.A., Atmaca, T.: Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. In: Czach\u00f3rski, T., Gelenbe, E., Grochla, K., Lent, R. (eds.) ISCIS 2018. CCIS, vol. 935, pp. 141\u2013149. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00840-6_16"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Saber, M., El Farissi, I., Chadli, S., Emharraf, M.,Belkasmi, M.: Performance analysis of an intrusion detection systems based of artificial neural network. In: Europe and MENA Cooperation Advances in Information and Communication Technologies, pp. 511\u2013521. Springer International Publishing, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-46568-5_52, ISBN: 978-3-319-46568-5","DOI":"10.1007\/978-3-319-46568-5_52"},{"key":"5_CR21","unstructured":"Hodo, E., Bellekens, X., Hamilton, A., Tachtatzis, C., Atkinson, R.: Shallow and deep networks intrusion detection system: a taxonomy and survey. CoRR, abs\/1701.02145, 2017. http:\/\/arxiv.org\/abs\/1701.02145"},{"key":"5_CR22","doi-asserted-by":"publisher","first-page":"13546","DOI":"10.1109\/ACCESS.2019.2893871","volume":"7","author":"D Papamartzivanos","year":"2019","unstructured":"Papamartzivanos, D., G\u00f3mez M\u00e1rmol, D., Kambourakis, G.: Introducing deep learning self-adaptive misuse network intrusion detection systems. IEEE Access 7, 13546\u201313560 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2893871. ISSN: 2169\u20133536","journal-title":"IEEE Access"},{"key":"5_CR23","doi-asserted-by":"publisher","unstructured":"Karatas, G., Demir, O., Koray Sahingoz, O.: Deep learning in intrusion detection systems. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 113\u2013116, December 2018. https:\/\/doi.org\/10.1109\/IBIGDELFT.2018.8625278","DOI":"10.1109\/IBIGDELFT.2018.8625278"},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Yang, K., Liu, J., Zhang, C., Fang, Y.: Adversarial examples against the deep learning based network intrusion detection systems. In: MILCOM 2018\u20132018 IEEE Military Communications Conference (MILCOM), pp. 559\u2013564, October 2018. https:\/\/doi.org\/10.1109\/MILCOM.2018.8599759","DOI":"10.1109\/MILCOM.2018.8599759"},{"key":"5_CR25","unstructured":"Gurung, S., Ghose, M., Subedi, A.: Deep learning approach on network intrusion detection system using NSL-KDD dataset. Int. J. Comput. Netw. Inf. Secur. 11(3), 8 (2019). https:\/\/ucd.idm.oclc.org\/login?url=search-proquest-com.ucd.idm.oclc.org\/docview\/2193195455?accountid=14507"},{"issue":"4","key":"5_CR26","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J McHugh","year":"2000","unstructured":"McHugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory. ACM Trans. Inf. Syst. Secur. 3(4), 262\u2013294 (2000)","journal-title":"ACM Trans. Inf. Syst. Secur."},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Al-Nemrat, A., Venkatraman, S.: Deep learning approach for intelligent intrusion detection system. IEEE Access, 41525\u201341550 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2895334, ISSN: 2169\u20133536","DOI":"10.1109\/ACCESS.2019.2895334"},{"key":"5_CR28","doi-asserted-by":"publisher","unstructured":"Ustebay, S., Turgut, Z., Aydin, M.: Intrusion detection system with recursive feature elimination by using random forest and deep learning classifier. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 71\u201376, December 2018. https:\/\/doi.org\/10.1109\/IBIGDELFT.2018.8625318","DOI":"10.1109\/IBIGDELFT.2018.8625318"},{"key":"5_CR29","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2011","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., Burlington (2011). ISBN 0123814790, 9780123814791","edition":"3"},{"key":"5_CR30","volume-title":"Data Mining: Introductory and Advanced Topics","author":"M Dunham","year":"2002","unstructured":"Dunham, M.: Data Mining: Introductory and Advanced Topics. Prentice Hall PTR, Upper Saddle River (2002). ISBN 0130888923"},{"issue":"1","key":"5_CR31","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). https:\/\/doi.org\/10.1109\/TETCI.2017.2772792","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"5_CR32","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"I Witten","year":"2011","unstructured":"Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers Inc., Burlington (2011). ISBN 0123748569"},{"key":"5_CR33","unstructured":"Suh, S.: Practical Applications of Data Mining. Jones & Bartlett Learning, January 2011. ISBN 9780763785871"},{"issue":"4","key":"5_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/COMST.2018.2844742","volume":"20","author":"E Benkhelifa","year":"2018","unstructured":"Benkhelifa, E., Welsh, T., Amouda, W.: A critical review of practices and challenges in intrusion detection systems for IoT: towards universal and resilient systems. IEEE Commun. Surv. Tutor. 20(4), 1\u201315 (2018)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"5_CR35","first-page":"1","volume":"7","author":"H Pajouh","year":"2016","unstructured":"Pajouh, H., Javidan, R., Khayami, R., Ali, D., Choo, K.-K.R.: A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. 7, 1\u201311 (2016)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"5_CR36","doi-asserted-by":"publisher","first-page":"32910","DOI":"10.1109\/ACCESS.2018.2844794","volume":"6","author":"N Moustafa","year":"2018","unstructured":"Moustafa, N., Adi, E., Turnbull, B., Hu, J.: A new threat intelligence scheme for safeguarding industry 4.0 systems. IEEE Access 6, 32910\u201332924 (2018)","journal-title":"IEEE Access"},{"key":"5_CR37","unstructured":"Elrawy, M., Awad, A. and Hamed, H.: Intrusion detection systems for IoT-based smart environments: a survey. J. Cloud Comput. (2018). ISSN 2192\u2013113X 10.1186\/s13677-018-0123-6"},{"issue":"4","key":"5_CR38","doi-asserted-by":"publisher","first-page":"9889","DOI":"10.1007\/s10586-018-1847-2","volume":"22","author":"L Deng","year":"2018","unstructured":"Deng, L., Li, D., Yao, X., Cox, D., Wang, H.: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Comput. 22(4), 9889\u20139904 (2018). https:\/\/doi.org\/10.1007\/s10586-018-1847-2","journal-title":"Cluster Comput."},{"key":"5_CR39","doi-asserted-by":"publisher","unstructured":"Amouri, A., Alaparthy, V., and Morgera, S.: Cross layer-based intrusion detection based on network behavior for IoT. In 2018 IEEE 19th Wireless and Microwave Technology Conference (WAMICON), pp. 1\u20134, April 2018. https:\/\/doi.org\/10.1109\/WAMICON.2018.8363921","DOI":"10.1109\/WAMICON.2018.8363921"},{"issue":"1","key":"5_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-018-1128-z","volume":"2018","author":"L Liu","year":"2018","unstructured":"Liu, L., Xu, B., Zhang, X., Wu, X.: An intrusion detection method for internet of things based on suppressed fuzzy clustering. EURASIP J. Wirel. Commun. Netw. 2018(1), 1\u20137 (2018). https:\/\/doi.org\/10.1186\/s13638-018-1128-z","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"5_CR41","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.jnca.2018.02.004","volume":"108","author":"J Colom","year":"2018","unstructured":"Colom, J., Gil, D., Mora, H., Volckaert, B., Jimeno, A.: Scheduling framework for distributed intrusion detection systems over heterogeneous network architectures. J. Netw. Comput. Appl. 108, 76\u201386 (2018). https:\/\/doi.org\/10.1016\/j.jnca.2018.02.004. ISSN 1084-8045","journal-title":"J. Netw. Comput. Appl."},{"key":"5_CR42","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.future.2016.11.009","volume":"78","author":"R Roman","year":"2018","unstructured":"Roman, R., Lopez, J., Mambo, M., et al.: Mobile edge computing, fog: a survey and analysis of security threats and challenges. Future Gener. Comput. Syst. 78, 680\u2013698 (2018). https:\/\/doi.org\/10.1016\/j.future.2016.11.009. ISSN 0167-739X","journal-title":"Future Gener. Comput. Syst."},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Fernandez, G.: Deep Learning Approaches for Network Intrusion Detection, MSc Thesis Presented to the Graduate Faculty of The University of Texas at San Antonio, May 2019","DOI":"10.1109\/MILCOM47813.2019.9020824"},{"key":"5_CR44","doi-asserted-by":"publisher","first-page":"77396","DOI":"10.1109\/ACCESS.2020.2986013","volume":"8","author":"S Manimurugan","year":"2020","unstructured":"Manimurugan, S., Al-Mutairi, S., Aborokbah, M., Chilamkurti, N., Ganesan, S., Patan, R.: Effective attack detection in internet of medical things smart environment using a deep belief neural network. IEEE Access 8, 77396\u201377404 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2986013","journal-title":"IEEE Access"},{"issue":"2","key":"5_CR45","first-page":"187","volume":"5","author":"Z Pelletier","year":"2020","unstructured":"Pelletier, Z., Abualkibash, M.: Evaluating the CIC IDS-2017 dataset using machine learning methods and creating multiple predictive models in the statistical computing language R. Int. Res. J. Adv. Eng. Sci. 5(2), 187\u2013191 (2020)","journal-title":"Int. Res. J. Adv. Eng. Sci."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Digital Forensics and Cyber Crime"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68734-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T18:47:54Z","timestamp":1697827674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-68734-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687335","9783030687342"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68734-2_5","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":"7 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDF2C","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Digital Forensics and Cyber Crime","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Boston, MA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"15 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdf2c2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/d-forensics.org\/","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":"Confyplus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","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":"11","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":"4","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":"31% - 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":"4.7","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":"No","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 teh 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)"}}]}}