PPT-Applied Anomaly Based IDS

Author : alexa-scheidler | Published Date : 2016-09-08

Craig Buchanan University of Illinois at UrbanaChampaign CS 598 MCC 43013 Outline KNearest Neighbor Neural Networks Support Vector Machines Lightweight Network Intrusion

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Applied Anomaly Based IDS: Transcript

Craig Buchanan University of Illinois at UrbanaChampaign CS 598 MCC 43013 Outline KNearest Neighbor Neural Networks Support Vector Machines Lightweight Network Intrusion Detection LNID. a. nomaly. -. based. . E. vent . D. etection. and . T. racking. in . T. witter. Adrien . Guille. & Cécile Favre. ERIC . Lab. , . University. of Lyon 2, France. IEEE/ACM ASONAM 2014, Beijing, China. Anomaly Detection for. Cyber Security. Presentation by Mike Calder . Anomaly Detection. Used for cyber security. Detecting threats using network data. Detecting threats using host-based data. In some domains, anomalies are detected so that they can be removed/corrected. Anomaly-based . Network Intrusion . Detection (A-NIDS). by Nitish Bahadur, Gulsher Kooner, . Caitlin Kuhlman. 1. PALANTIR CYBER An End-to-End Cyber Intelligence Platform for Analysis & Knowledge Management [Online]. Available: . &. Intrusion . Detection Systems. 1. Intruders. Three classes of intruders:. Examples of Intrusion. Performing a remote root compromise of an e-mail server. Defacing a Web server. Guessing and cracking passwords. Detection. Carolina . Ruiz. Department of Computer Science. WPI. Slides based on . Chapter 10 of. “Introduction to Data Mining”. textbook . by Tan, Steinbach, Kumar. (all figures and some slides taken from this chapter. Benjamin I.P. Rubinstein, Blaine Nelson, Anthony . D. . Joseph, . Shing-hon. Lau, . NinaTaft. , J. D. . Tygar. RAID 2008. Presented by: Dong-Jae Shin. Outline. Background. Machine Learning. PCA (Principal Component Analysis. On-Orbit Anomaly Research. NASA IV&V Facility. Fairmont, WV, USA. 2013 Annual Workshop on Independent Verification & Validation of Software. Fairmont, WV, USA. September 10-12, 2013. Agenda. September 10, 2013. 9. Introduction to Data Mining, . 2. nd. Edition. by. Tan. , Steinbach, Karpatne, . Kumar. With additional slides and modifications by Carolina Ruiz, WPI. 11/20/2018. Introduction to Data Mining, 2nd Edition. Yasin. Yilmaz, . Mahsa. Mozaffari. Secure and Intelligent Systems Lab. sis.eng.usf.edu. Department of Electrical Engineering. University of South Florida, Tampa, FL. S. u. leyman. . Uluda. g. Department of . see in the data they learn continuously so new patterns replace old patterns in the same way you remember recent events better than old events And if a new pattern is different but similar to previous “Anomaly Detection: A Tutorial”. Arindam. . Banerjee. , . Varun. . Chandola. , . Vipin. Kumar, Jaideep . Srivastava. , . University of Minnesota. Aleksandar. . Lazarevic. , . United Technology Research Center. 14. . World-Leading Research with Real-World Impact!. CS 5323. Outline. Anomaly detection. Facts and figures. Application. Challenges. Classification. Anomaly in Wireless. . 2. Recent News. Hacking of Government Computers Exposed 21.5 Million People. Authors. Bo Sun, Fei Yu, Kui Wu, Yang Xiao, and Victor C. M. Leung.. . Presented by . Aniruddha Barapatre. Introduction. Importance of Cellular phones.. Due to the open radio transmission environment and the physical vulnerability of mobile devices , . Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly.

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