18_anomaly_detection.pdf - Anomaly Detection Some slides taken or adapted from “Anomaly Detection A Tutorial” Arindam Banerjee Varun Chandola Vipin

# 18_anomaly_detection.pdf - Anomaly Detection Some slides...

• Notes
• 50

This preview shows page 1 - 11 out of 50 pages.

Jeff Howbert Introduction to Machine Learning Winter 2014 1 Anomaly Detection Some slides taken or adapted from: “Anomaly Detection: A Tutorial” Arindam Banerjee, Varun Chandola, Vipin Kumar, Jaideep Srivastava, University of Minnesota Aleksandar Lazarevic, United Technology Research Center

Subscribe to view the full document.

Jeff Howbert Introduction to Machine Learning Winter 2014 2 Anomalies and outliers are essentially the same thing: objects that are different from most other objects The techniques used for detection are the same. Anomaly detection
Jeff Howbert Introduction to Machine Learning Winter 2014 3 circle6 Historically, the field of statistics tried to find and remove outliers as a way to improve analyses. circle6 There are now many fields where the outliers / anomalies are the objects of greatest interest. The rare events may be the ones with the greatest impact, and often in a negative way. Anomaly detection

Subscribe to view the full document.

Jeff Howbert Introduction to Machine Learning Winter 2014 4 circle6 Data from different class of object or underlying mechanism disease vs. non-disease fraud vs. not fraud circle6 Natural variation tails on a Gaussian distribution circle6 Data measurement and collection errors Causes of anomalies
Jeff Howbert Introduction to Machine Learning Winter 2014 5 Structure of anomalies circle6 Point anomalies circle6 Contextual anomalies circle6 Collective anomalies

Subscribe to view the full document.

Jeff Howbert Introduction to Machine Learning Winter 2014 6 circle6 An individual data instance is anomalous with respect to the data Point anomalies X Y N 1 N 2 o 1 o 2 O 3
Jeff Howbert Introduction to Machine Learning Winter 2014 7 Contextual anomalies circle6 An individual data instance is anomalous within a context circle6 Requires a notion of context circle6 Also referred to as conditional anomalies * * Song, et al, “Conditional Anomaly Detection”, IEEE Transactions on Data and Knowledge Engineering, 2006. Normal Anomaly

Subscribe to view the full document.

Jeff Howbert Introduction to Machine Learning Winter 2014 8 Collective anomalies circle6 A collection of related data instances is anomalous circle6 Requires a relationship among data instances Sequential data Spatial data Graph data circle6 The individual instances within a collective anomaly are not anomalous by themselves anomalous subsequence
Jeff Howbert Introduction to Machine Learning Winter 2014 9 Applications of anomaly detection circle6 Network intrusion circle6 Insurance / credit card fraud circle6 Healthcare informatics / medical diagnostics circle6 Industrial damage detection circle6 Image processing / video surveillance circle6 Novel topic detection in text mining circle6

Subscribe to view the full document.

Jeff Howbert Introduction to Machine Learning Winter 2014 10 Intrusion detection circle6 Intrusion detection Monitor events occurring in a computer system or network and analyze them for intrusions Intrusions defined as attempts to bypass the security mechanisms of a computer or network circle6 Challenges Traditional intrusion detection systems are based on signatures of known attacks and
• Fall '19
• ÖZLEM ŞENVAR

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern