1. Anomaly Detection brief review of my prospectus Ziba Rostamian CS590 – Winter 2008

2. What I am planning to accomplish Study Learning Finite Automaton. Focusing of CSSR algorithm. Choose an application of desire and test the performance of the CSSR algorithm. (Once I implement the algorithm I can try it for different application and find out where it performs better). Study CSSR and its extensions and use it for detecting anomaly of moving object. Apply some modification in to the algorithm (it depends on how I proceed).

3. Why this is academically interesting Finite automaton inference has several "real world" applications. Electrical engineering DFA’s have been proposed as a model of players. Model the problem of robot trying to learn its envirounment. The application of PFAs (Probabilistic Finite automaton), of which Hidden Markov Models (HMMs) are special case, are much more extensive. Speech recognition and handwriting recognition recognizing patterns in biological sequences such a DNA and proteins

4. Anomaly Detection What are anomalies/outliers? The set of data points that are considerably different than the remainder of the data Variants of Anomaly/Outlier Detection Problems Given a database D, find all the data points x  D with anomaly scores greater than some threshold t Given a database D, find all the data points x  D having the top-n largest anomaly scores f(x) Applications: Credit card fraud detection, telecommunication fraud detection, network intrusion detection, fault detection

5. Importance of Anomaly Detection Ozone Depletion History In 1985 three researchers (Farman, Gardinar and Shanklin) were puzzled by data gathered by the British Antarctic Survey showing that ozone levels for Antarctica had dropped 10% below normal levels Why did the Nimbus 7 satellite, which had instruments aboard for recording ozone levels, not record similarly low ozone concentrations? NASA discovered that the spring-time ''ozone hole'' had been covered up by a computer- program desinged to discard sudden, large drops in ozone concentrations as ''errors''.

6. Anomaly detection in moving object Example: There are a large number of massive vessels sailing near American coasts. It’s unrealistic to manually trace such a enormous number of moving objects and identify the suspicious ones. Therefore, it’s highly desirable to develop automated tools that can evaluate the behavior of all maritime vessels and flag the suspicious ones. This will allow human agent to focus their monitoring more efficiently and accurantely.

7. Mechanisms for Anomaly detection Classification, which relies on training data set. Normal Outliers Clustering, which performs automated grouping without using training set.

8. Anticipated Challenges Tracking moving object can generate an enormous amount of complex data. Example: the time and the location of a vessel might be recorded every few seconds, and non-spatial information such a vessel’s weight, speed, shape and color may be included in this recording There exists substantial complexities of possible abnormal behavior.