{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:31:21Z","timestamp":1753601481758,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"CUREX: seCUre and pRivate hEalth data eXchange (H2020)","award":["826404"],"award-info":[{"award-number":["826404"]}]},{"name":"RAINBOW (H2020)","award":["871403"],"award-info":[{"award-number":["871403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,6]]},"DOI":"10.1145\/3538712.3538736","type":"proceedings-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T10:14:41Z","timestamp":1661249681000},"page":"1-4","source":"Crossref","is-referenced-by-count":1,"title":["Facilitating DoS Attack Detection using Unsupervised Anomaly Detection"],"prefix":"10.1145","author":[{"given":"Christos","family":"Bellas","sequence":"first","affiliation":[{"name":"Aristotle University of Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgia","family":"Kougka","sequence":"additional","affiliation":[{"name":"Aristotle University of Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athanasios","family":"Naskos","sequence":"additional","affiliation":[{"name":"Atlantis Engineering SA, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anastasios","family":"Gounaris","sequence":"additional","affiliation":[{"name":"Aristotle University of Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athena","family":"Vakali","sequence":"additional","affiliation":[{"name":"Aristotle University of Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos","family":"Xenakis","sequence":"additional","affiliation":[{"name":"University of Piraeus, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apostolos","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"Aristotle University of Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"volume-title":"Data Mining - The Textbook","author":"Aggarwal C.","key":"e_1_3_2_1_1_1","unstructured":"Charu\u00a0 C. Aggarwal . 2015. Data Mining - The Textbook . Springer . Charu\u00a0C. Aggarwal. 2015. Data Mining - The Textbook. Springer."},{"volume-title":"Outlier Analysis, 2ed","author":"Aggarwal C.","key":"e_1_3_2_1_2_1","unstructured":"Charu\u00a0 C. Aggarwal . 2017. Outlier Analysis, 2ed . Springer . Charu\u00a0C. Aggarwal. 2017. Outlier Analysis, 2ed. Springer."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Stefan Asanger and Andrew Hutchison. 2013. Experiences and Challenges in Enhancing Security Information and Event Management Capability Using Unsupervised Anomaly Detection. In ARES.  Stefan Asanger and Andrew Hutchison. 2013. Experiences and Challenges in Enhancing Security Information and Event Management Capability Using Unsupervised Anomaly Detection. In ARES.","DOI":"10.1109\/ARES.2013.86"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Christos Bellas Athanasios Naskos Georgia Kougka George Vlahavas Anastasios Gounaris Athena Vakali Apostolos Papadopoulos Evmorfia Biliri Nefeli Bountouni and Gustavo Gonzalez Granadillo. 2020. A Methodology for Runtime Detection and Extraction of Threat Patterns. SN Comput. Sci. (2020).  Christos Bellas Athanasios Naskos Georgia Kougka George Vlahavas Anastasios Gounaris Athena Vakali Apostolos Papadopoulos Evmorfia Biliri Nefeli Bountouni and Gustavo Gonzalez Granadillo. 2020. A Methodology for Runtime Detection and Extraction of Threat Patterns. SN Comput. Sci. (2020).","DOI":"10.1007\/s42979-020-00226-8"},{"volume-title":"Lightweight DDoS flooding attack detection using NOX\/OpenFlow","author":"Braga Rodrigo","key":"e_1_3_2_1_5_1","unstructured":"Rodrigo Braga , Edjard Mota , and Alexandre Passito . 2010. Lightweight DDoS flooding attack detection using NOX\/OpenFlow . In IEEE LCN. Rodrigo Braga, Edjard Mota, and Alexandre Passito. 2010. Lightweight DDoS flooding attack detection using NOX\/OpenFlow. In IEEE LCN."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Pedro Casas Johan Mazel and Philippe Owezarski. 2012. Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge. Comput. Commun. (2012).  Pedro Casas Johan Mazel and Philippe Owezarski. 2012. Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge. Comput. Commun. (2012).","DOI":"10.1016\/j.comcom.2012.01.016"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Witcha Chimphlee Abdul\u00a0Hanan Abdullah Mohd\u00a0Noor Md\u00a0Sap Surat Srinoy and Siriporn Chimphlee. 2006. Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering. In ICHIT.  Witcha Chimphlee Abdul\u00a0Hanan Abdullah Mohd\u00a0Noor Md\u00a0Sap Surat Srinoy and Siriporn Chimphlee. 2006. Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering. In ICHIT.","DOI":"10.1109\/ICHIT.2006.253508"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Dylan Chou and Meng Jiang. 2022. A Survey on Data-driven Network Intrusion Detection. ACM Comput. Surv. (2022).  Dylan Chou and Meng Jiang. 2022. A Survey on Data-driven Network Intrusion Detection. ACM Comput. Surv. (2022).","DOI":"10.1145\/3472753"},{"key":"e_1_3_2_1_9_1","volume-title":"Meet Parekh, Vaibhav Savla, Rudra Mishra, and Mahesh Shirole.","author":"Divekar Abhishek","year":"2018","unstructured":"Abhishek Divekar , Meet Parekh, Vaibhav Savla, Rudra Mishra, and Mahesh Shirole. 2018 . Benchmarking datasets for Anomaly-based Network Intrusion Detection : KDD CUP 99 alternatives. CoRR abs\/1811.05372(2018). Abhishek Divekar, Meet Parekh, Vaibhav Savla, Rudra Mishra, and Mahesh Shirole. 2018. Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives. CoRR abs\/1811.05372(2018)."},{"key":"e_1_3_2_1_10_1","unstructured":"Markus Goldstein Stefan Asanger Matthias Reif and Andrew Hutchison. 2013. Enhancing Security Event Management Systems with Unsupervised Anomaly Detection.. In ICPRAM.  Markus Goldstein Stefan Asanger Matthias Reif and Andrew Hutchison. 2013. Enhancing Security Event Management Systems with Unsupervised Anomaly Detection.. In ICPRAM."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Meng Jianliang Shang Haikun and Bian Ling. 2009. The Application on Intrusion Detection Based on K-means Cluster Algorithm. In 2009 International Forum on Information Technology and Applications.  Meng Jianliang Shang Haikun and Bian Ling. 2009. The Application on Intrusion Detection Based on K-means Cluster Algorithm. In 2009 International Forum on Information Technology and Applications.","DOI":"10.1109\/IFITA.2009.34"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2920326"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Maria Kontaki Anastasios Gounaris Apostolos\u00a0N. Papadopoulos Kostas Tsichlas and Yannis Manolopoulos. 2016. Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Inf. Syst. (2016).  Maria Kontaki Anastasios Gounaris Apostolos\u00a0N. Papadopoulos Kostas Tsichlas and Yannis Manolopoulos. 2016. Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Inf. Syst. (2016).","DOI":"10.1016\/j.is.2015.07.006"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Luk\u00e1s Kotlaba Simona Buchoveck\u00e1 and R\u00f3bert L\u00f3rencz. 2021. Active Directory Kerberoasting Attack: Detection using Machine Learning Techniques. In ICISSP.  Luk\u00e1s Kotlaba Simona Buchoveck\u00e1 and R\u00f3bert L\u00f3rencz. 2021. Active Directory Kerberoasting Attack: Detection using Machine Learning Techniques. In ICISSP.","DOI":"10.5220\/0010202803760383"},{"volume-title":"ICCST","author":"Lashkari Arash\u00a0Habibi","key":"e_1_3_2_1_15_1","unstructured":"Arash\u00a0Habibi Lashkari , Amy Seo , Gerard\u00a0Drapper Gil , and Ali Ghorbani . 2017. CIC-AB: Online ad blocker for browsers . In ICCST , IEEE. Arash\u00a0Habibi Lashkari, Amy Seo, Gerard\u00a0Drapper Gil, and Ali Ghorbani. 2017. CIC-AB: Online ad blocker for browsers. In ICCST, IEEE."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Rafath Samrin and D. Vasumathi. 2017. Review on anomaly based network intrusion detection system. ICEECCOT (2017).  Rafath Samrin and D. Vasumathi. 2017. Review on anomaly based network intrusion detection system. ICEECCOT (2017).","DOI":"10.1109\/ICEECCOT.2017.8284655"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Andrey Sapegin Marian Gawron David Jaeger Feng Cheng and Christoph Meinel. 2017. Evaluation of in-memory storage engine for machine learning analysis of security events. Concurr. Comput. Pract. Exp.(2017).  Andrey Sapegin Marian Gawron David Jaeger Feng Cheng and Christoph Meinel. 2017. Evaluation of in-memory storage engine for machine learning analysis of security events. Concurr. Comput. Pract. Exp.(2017).","DOI":"10.1002\/cpe.3800"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Andrey Sapegin David Jaeger Feng Cheng and Christoph Meinel. 2017. Towards a System for Complex Analysis of Security Events in Large-Scale Networks. Comput. Secur. (2017).  Andrey Sapegin David Jaeger Feng Cheng and Christoph Meinel. 2017. Towards a System for Complex Analysis of Security Events in Large-Scale Networks. Comput. Secur. (2017).","DOI":"10.1016\/j.cose.2017.02.001"},{"key":"e_1_3_2_1_20_1","volume-title":"Toward generating a new intrusion detection dataset and intrusion traffic characterization.ICISSp","author":"Sharafaldin Iman","year":"2018","unstructured":"Iman Sharafaldin , Arash\u00a0Habibi Lashkari , and Ali\u00a0 A Ghorbani . 2018. Toward generating a new intrusion detection dataset and intrusion traffic characterization.ICISSp ( 2018 ). Iman Sharafaldin, Arash\u00a0Habibi Lashkari, and Ali\u00a0A Ghorbani. 2018. Toward generating a new intrusion detection dataset and intrusion traffic characterization.ICISSp (2018)."},{"key":"e_1_3_2_1_21_1","volume-title":"PROUD: PaRallel OUtlier Detection for streams. In SIGMOD.","author":"Toliopoulos Theodoros","year":"2020","unstructured":"Theodoros Toliopoulos , Christos Bellas , Anastasios Gounaris , and Apostolos Papadopoulos . 2020 . PROUD: PaRallel OUtlier Detection for streams. In SIGMOD. Theodoros Toliopoulos, Christos Bellas, Anastasios Gounaris, and Apostolos Papadopoulos. 2020. PROUD: PaRallel OUtlier Detection for streams. In SIGMOD."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Theodoros Toliopoulos Anastasios Gounaris Kostas Tsichlas Apostolos Papadopoulos and Sandra Sampaio. 2020. Continuous outlier mining of streaming data in flink. Inf. Syst. (2020).  Theodoros Toliopoulos Anastasios Gounaris Kostas Tsichlas Apostolos Papadopoulos and Sandra Sampaio. 2020. Continuous outlier mining of streaming data in flink. Inf. Syst. (2020).","DOI":"10.1016\/j.is.2020.101569"},{"key":"e_1_3_2_1_23_1","volume-title":"Distance-based outlier detection in data streams. PVLDB","author":"Tran Luan","year":"2016","unstructured":"Luan Tran , Liyue Fan , and Cyrus Shahabi . 2016. Distance-based outlier detection in data streams. PVLDB ( 2016 ). Luan Tran, Liyue Fan, and Cyrus Shahabi. 2016. Distance-based outlier detection in data streams. PVLDB (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges","author":"Usama Muhammad","year":"2019","unstructured":"Muhammad Usama , Junaid Qadir , Aunn Raza , Hunain Arif , Kok-lim\u00a0 Alvin Yau , Yehia Elkhatib , Amir Hussain , and Ala Al-Fuqaha . 2019. Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges . IEEE Access ( 2019 ). Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-lim\u00a0Alvin Yau, Yehia Elkhatib, Amir Hussain, and Ala Al-Fuqaha. 2019. Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges. IEEE Access (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Stefano Zanero and Sergio\u00a0M. Savaresi. 2004. Unsupervised Learning Techniques for an Intrusion Detection System. In SAC.  Stefano Zanero and Sergio\u00a0M. Savaresi. 2004. Unsupervised Learning Techniques for an Intrusion Detection System. In SAC.","DOI":"10.1145\/967900.967988"},{"volume-title":"SIEDS","author":"Zhang Julina","key":"e_1_3_2_1_26_1","unstructured":"Julina Zhang , Kerry Jones , Tianye Song , Hyojung Kang , and Donald\u00a0 E Brown . 2017. Comparing unsupervised learning approaches to detect network intrusion using NetFlow data . In SIEDS , IEEE. Julina Zhang, Kerry Jones, Tianye Song, Hyojung Kang, and Donald\u00a0E Brown. 2017. Comparing unsupervised learning approaches to detect network intrusion using NetFlow data. In SIEDS, IEEE."}],"event":{"name":"SSDBM 2022: 34th International Conference on Scientific and Statistical Database Management","acronym":"SSDBM 2022","location":"Copenhagen Denmark"},"container-title":["34th International Conference on Scientific and Statistical Database Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3538712.3538736","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3538712.3538736","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:39Z","timestamp":1750183779000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3538712.3538736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":25,"alternative-id":["10.1145\/3538712.3538736","10.1145\/3538712"],"URL":"https:\/\/doi.org\/10.1145\/3538712.3538736","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]}}}