PPT-Applied Anomaly Based IDS

Craig Buchanan University of Illinois at UrbanaChampaign CS 598 MCC 43013 Outline KNearest Neighbor Neural Networks Support Vector Machines Lightweight Network Intrusion Detection LNID. Contents. Overview of IDS/IPS. Components of an IDS/IPS. IDS/IPS classification. By scope of protection. By detection model. 2. /37. Intrusion. A set of actions aimed at compromising the security goals (confidentiality, integrity, availability of a computing/networking resource). What is ids?. IDS is . the data . management system for small and medium sized businesses . looking to . easily, simply, and . cost-effectively . consolidate their data needs.. CONSOLIDATE. . your data. 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: . 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. DETECTION. Scholar: . Andrew . Emmott. Focus: . Machine Learning. Advisors: . Tom . Dietterich. , Prasad . Tadepalli. Donors: . Leslie and Mark Workman. Acknowledgements:. Funding for my research is . Network Security. All materials . are . licensed under a Creative Commons . “. Share Alike. ”. license.. http://creativecommons.org/licenses/by-sa/3.0/. 3. Why Assess. What’s needed. Router and Switch Security. Contents. Overview of IDS/IPS. Components of an IDS/IPS. IDS/IPS classification. By scope of protection. By detection model. 2. /37. Intrusion. A set of actions aimed at compromising the security goals (confidentiality, integrity, availability of a computing/networking resource). 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. ILLiad Conference 2009. IDSProject.org. Mark Sullivan, SUNY Geneseo . Cyril Oberlander, SUNY Geneseo. Dustin Stokes, Atlas Systems, Inc.. Curtis Poston, Atlas Systems, Inc.. Speaker Notes are included so please download the file to your computer before running.. 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 Shilin . He. ,. . Jieming. Zhu, . Pinjia. . He,. and Michael R. . Lyu. Department of Computer Science and Engineering, . The Chinese University of Hong Kong, Hong Kong. 2016/10/26. Background & Motivation. Kai Shen, Christopher Stewart, . Chuanpeng Li, and Xin Li. 6/16/2009. SIGMETRICS 2009. 1. University of Rochester. Performance Anomalies. 6/16/2009. SIGMETRICS 2009. 2. Complex software systems (like operating systems and distributed systems):. Hierarchical Temporal Memory (and LSTM). Jaime Coello de Portugal. Many thanks to . Jochem. . Snuverink. Motivation. Global outlier. Level change. Pattern deviation. Pattern change. Plots from: Ted . 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.