Outlier detection for high dimensional data
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This document outlines a project for outlier detection in high dimensional data. It will analyze techniques for finding outliers by studying projections from datasets, as existing methods make assumptions of low dimensionality that do not apply to very high dimensional data. The system architecture is divided into modules for high dimensional outlier detection, lower dimensional projection, and post processing. Implementation plans include literature review, studying Java, developing the detection system and projections, testing, and documentation. A Gantt chart and cost model are provided.
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Abstract
Internet Technology is growing at exponential rate day by day, making data security of computer systems more complex and critical. There has been multiple methodology implemented for the same in recent time as detailed in [1], [3]. Availability of larger bandwidth has made the multiple large computer server network connected worldwide and thus increasing the load on the necessity to secure data and Intrusion detection system (IDS) is one of the most efficient technique to maintain security of computer system. The proposed system is designed in such a way that are helpful in identifying malicious behavior and improper use of computer system. In this report we proposed a hybrid technique for intrusion detection using data mining algorithms. Our main objective is to do complete analysis of intrusion detection Dataset to test the implemented system.In This report we will propose a new methodology in which Modified k-mean is used for clustering whereas Naïve Bayes for the classification. These two data mining techniques will be used for Intrusion detection in large horizontally distributed database.
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Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
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Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Outlier detection for high dimensional data
- 1. Outlier Detection for High Dimensional Data Presented by
- 2. Outline • Problem definition • Literature survey • System features • System Architecture • Analysis Models • UML diagrams • System Implementation Plans • Grantt chart, Cost implementation model
- 3. Problem definition The outlier detection technique finds applications in credit card fraud, network intrusion detection, financial applications and marketing. This problem typically arises in the context of very high dimensional data sets. Much of the recent work on find-ing outliers use methods which make implicit assumptions of relatively low dimensionality of the data. Thus, we discuss new techniques for outlier detection which find the outliers by studying the behavior of projections from the data set.
- 4. Literature survey Many algorithms have been proposed in recent years for out-lier detection, but they are not methods which are specifically designed in order to deal with the curse of high dimensionality. Two interesting algorithms define outliers by using the full dimensional distances of the points from one another. This measure is naturally sus-ceptible to the curse of high dimensionality. According to Knorr and Ng, A point p in a data set is an outlier with respect to the parameters k and A, if no more than k points in the data set are at a distance A or less from p. As pointed out , this method is sensitive to the use of the parameter A which is hard to figure out a-priori. In addition, when the dimensionality increases, it becomes in-creasingly difficult to pick.
- 5. System features Hardware & Software Requirements Hard disk 80 GB RAM 1GB Technology Java Tools Net-beans IDE Processor Intel Pentium IV or above Operating System Windows XP
- 6. System features continued.. Quality Attributes • Usability : The application seem to user friendly since the GUI is interactive. • Maintainability : This application is maintained for long period of time since it will be implemented under java platform . • Reusability : The application can be reusable by expanding it to the new modules • Portability: The application is purely a portable mobile application since it can only be operated on android Operating system.
- 7. System architecture The system architecture is divided into three modules: • High dimensional outlier detection • Lower dimensional projection • Post processing
- 8. Use case
- 9. Activity
- 10. System Implementation Plan SR No Task Name Start Duration 1 Project topic finalization 10 days 2 Literature 10 days 3 Studying Core java,J2SE 30 days 4 Implementation of High Dimensional Outlier detection system 7 days 5 Implementation of Lower projections 10 days
- 11. Grant chart & cost implementation model 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Training System installation Studying Core java Implementation of Modules Testing Documentation Cost(in RS) Time(in days)