Presentation is loading. Please wait.

Presentation is loading. Please wait.

1 Weekly Progress (MAGGIE) Adnan Iqbal Superviser Dr. Waqar Mahmood 22-11-05.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "1 Weekly Progress (MAGGIE) Adnan Iqbal Superviser Dr. Waqar Mahmood 22-11-05."— Presentation transcript:

1 1 Weekly Progress (MAGGIE) Adnan Iqbal Superviser Dr. Waqar Mahmood 22-11-05

2 2 Tasks completed Discover a scheme that can be used to get relationship between network wide anomalies and single route anomalies Implement the scheme Perform Regularization of Data Apply the scheme to suitable routes Analyze Results Study of MIT Lincoln Lab intrusion detection data Study of new data set provided by SLAC Reading Time Analysis (SLAC data) Analysis of MIT Lincoln Lab intrusion detection data Trimming (removal of outliers)

3 3 Trimming Improvement in the results presented last time Output format has been changed Date and Time are included 4/3/20050:00731720.16769.2 4/3/20050:10739754.88465.938 4/3/20050:20640638.37378.469 4/3/20050:30782695.19971.956

4 4 Normalization During multivariate data analysis it is possible that variables have different ranges which might differ significantly and introduce a bias in the analysis. For example Var 1 : 0-1 Var 2: 0-100 Var 3: 100-10000 Normalization is the process of converting data into smaller ranges such that behavior of data does not change

5 5 Normalization Different Methods Min-Max Normalization Z- Score Normalization Decimal Scaling Many Others

6 6 Min-Max Normalization 731720.16769.2 739754.88465.938 640638.37378.469 782695.19971.956 797789.24462.702 726729.64168.235 731787.45962.67 764784.19162.918 760756.24565.77 802780.4563.803 786785.14663.174 691724.61768.724 0.8579810.8679570.278442 0.8673710.9097980.265316 0.7511740.7693770.315738 0.917840.8378650.289531 0.9354460.9512090.252296 0.8521130.8793750.274559 0.8579810.9490580.252167 0.8967140.9451190.253165 0.8920190.9114380.26464 0.9413150.9406110.256726 0.9225350.946270.254195 0.8110330.873320.276526

7 7 Z-Score Normalization 731720.16769.2 739754.88465.938 640638.37378.469 782695.19971.956 797789.24462.702 726729.64168.235 731787.45962.67 764784.19162.918 760756.24565.77 802780.4563.803 786785.14663.174 691724.61768.724 1.4713951.23488-0.06021 1.5010731.367752-0.12523 1.13380.921830.124545 1.6605961.13932-0.00528 1.7162441.499258-0.18974 1.4528461.271139-0.07945 1.4713951.492426-0.19037 1.5938191.479918-0.18543 1.578981.372961-0.12858 1.7347931.465601-0.16779 1.6754361.483574-0.18033 1.3230011.251911-0.0697

8 8 Future Task Regularization Code

Download ppt "1 Weekly Progress (MAGGIE) Adnan Iqbal Superviser Dr. Waqar Mahmood 22-11-05."

Similar presentations

SOP and POS Forms Notation.

SOP and POS Forms Notation.
By Veronika Movagharianpour and Adam Brakel. Software Developers face challenges:  Producing high-quality software  with low-defect levels  while doing.

By Veronika Movagharianpour and Adam Brakel. Software Developers face challenges:  Producing high-quality software  with low-defect levels  while doing.
Log-linear and logistic models Generalised linear model ANOVA revisited Log-linear model: Poisson distribution logistic model: Binomial distribution Deviances.

Log-linear and logistic models Generalised linear model ANOVA revisited Log-linear model: Poisson distribution logistic model: Binomial distribution Deviances.
Module 36: Correlation Pitfalls Effect Size and Correlations Larger sample sizes require a smaller correlation coefficient to reach statistical significance.

Module 36: Correlation Pitfalls Effect Size and Correlations Larger sample sizes require a smaller correlation coefficient to reach statistical significance.
COMSATS Institute of Information Technology Virtual campus Islamabad

COMSATS Institute of Information Technology Virtual campus Islamabad
Www.company.com Lab4 CPIT 440 Data Mining and Warehouse.

Lab4 CPIT 440 Data Mining and Warehouse.
Progress Presentation. Tasks Completed The tasks that were completed in the last week are: The tasks that were completed in the last week are: The implementation.

Progress Presentation. Tasks Completed The tasks that were completed in the last week are: The tasks that were completed in the last week are: The implementation.
SAMPA digital status Arild Velure 03.06.15. Tasks done previous week Changes in data formating unit -> ringbuffer -> memory completed and tested – Saves.

SAMPA digital status Arild Velure Tasks done previous week Changes in data formating unit -> ringbuffer -> memory completed and tested – Saves.
Multi-Route Anomaly detection using Principal Component Analysis Adnan Iqbal Superviser Dr. Waqar Mahmood 21-06-05.

Multi-Route Anomaly detection using Principal Component Analysis Adnan Iqbal Superviser Dr. Waqar Mahmood
Anomaly Detection using GAs M. Umer Khan 22-Nov-2005.

Anomaly Detection using GAs M. Umer Khan 22-Nov-2005.
Relational Data Mining in Finance Haonan Zhang CFWin03-37 03/04/2003.

Relational Data Mining in Finance Haonan Zhang CFWin /04/2003.
Multi-Route Anomaly detection using Principal Component Analysis Adnan Iqbal Superviser Dr. Waqar Mahmood.

Multi-Route Anomaly detection using Principal Component Analysis Adnan Iqbal Superviser Dr. Waqar Mahmood.
131.123.247.253 -> 131.123.246.52 -> 131.123.11.254 131.123.8.1 131.123.11.254 131.123.247.253 131.123.246.52.

> >
Anomaly Detection. Anomaly/Outlier Detection  What are anomalies/outliers? The set of data points that are considerably different than the remainder.

Anomaly Detection. Anomaly/Outlier Detection  What are anomalies/outliers? The set of data points that are considerably different than the remainder.
Chapter 3, Part 1 Descriptive Statistics II: Numerical Methods

Chapter 3, Part 1 Descriptive Statistics II: Numerical Methods
Total Quality Management BUS 3 – 142 Statistics for Variables Week of Mar 14, 2011.

Total Quality Management BUS 3 – 142 Statistics for Variables Week of Mar 14, 2011.
Data Mining: A Closer Look

Data Mining: A Closer Look
Slide 1 Detecting Outliers Outliers are cases that have an atypical score either for a single variable (univariate outliers) or for a combination of variables.

Slide 1 Detecting Outliers Outliers are cases that have an atypical score either for a single variable (univariate outliers) or for a combination of variables.
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data Authors: Eleazar Eskin, Andrew Arnold, Michael Prerau,

A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data Authors: Eleazar Eskin, Andrew Arnold, Michael Prerau,
CS490D: Introduction to Data Mining Prof. Chris Clifton April 14, 2004 Fraud and Misuse Detection.

CS490D: Introduction to Data Mining Prof. Chris Clifton April 14, 2004 Fraud and Misuse Detection.

Similar presentations

Ads by Google