PPT-Application of the Complex Event Processing system for anomaly detection and network monitoring

Author : smith | Published Date : 2023-12-30

Marek Pawłowski Gerard Frankowski Marcin Jerzak Maciej Miłostan Tomasz Nowak Poznań Supercomputing and Networking Center Agenda Introduction System

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Application of the Complex Event Processing system for anomaly detection and network monitoring: Transcript

Marek Pawłowski Gerard Frankowski Marcin Jerzak Maciej Miłostan Tomasz Nowak Poznań Supercomputing and Networking Center Agenda Introduction System Architecture . John Plummer, Jeff Johnson. Introduction. What is CEP ?. Typical Application and Architecture. Event Query Languages. Event Processing Examples. NEsper. BizTalk RFID. Demo. Agenda. “Complex Event Processing (CEP) is a set of techniques and tools to help understand and control event-driven Information Systems”. -. Traffic Video Surveillance. Ziming. Zhang, . Yucheng. Zhao and . Yiwen. Wan. Outline. Introduction. &Motivation. Problem Statement. Paper Summeries. Discussion and Conclusions. What are . Anomalies?. Introduction and Use Cases. Derick . Winkworth. , Ed Henry and David Meyer. Agenda. Introduction and a Bit of History. So What Are Anomalies?. Anomaly Detection Schemes. Use Cases. Current Events. Q&A. 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. Intruders. WenZhan. Song. Cryptography and Network Security. 1. Intruders. Three classes of intruders:. Examples of Intrusion. Performing a remote root compromise of an e-mail server. Defacing a Web server. Paper by: T. Bowen. Presented by: Tiyseer Al Homaiyd. 1. Introduction: . Intrusions: show observable events that deviate from the . norm.. Survivable system usually focus on detecting intrusions rather than preventing or containing damage. . Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. Craig Buchanan. University of Illinois at Urbana-Champaign. CS 598 MCC. 4/30/13. Outline. K-Nearest Neighbor. Neural Networks. Support Vector Machines. Lightweight Network Intrusion Detection (LNID). 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. Fourth Edition. By: William Stallings and Lawrie Brown. Chapter . 8. Intrusion Detection. Classes of Intruders –. Cyber Criminals. Individuals or members of an organized crime group with a goal of financial reward. 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. Project Lead: . Farokh. . Bastani. , I-Ling Yen, . Latifur. Khan. Date: April 7, 2011. 2010/Current Project Overview. Self-Detection of Abnormal Event Sequences. 2. Tasks:. Prepare Cisco event sequence data for analysis tools.. 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.

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