Detection and Classification Of Normal and Anomaly IP Packet | Network intrusion detection through Neural Network Hybrid Learning with Data Transformation Analysis

Saima Munawar

ISBN 10: 3847373161 / ISBN 13: 9783847373162

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

Detection and Classification Of Normal and Anomaly IP Packet | Network intrusion detection through Neural Network Hybrid Learning with Data Transformation Analysis | Saima Munawar | Taschenbuch | 108 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783847373162 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 106481262

Bibliographic Details

• Title: Detection and Classification Of Normal and Anomaly IP Packet | Network intrusion detection through Neural Network Hybrid Learning with Data Transformation Analysis
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3847373161
• ISBN 13: 9783847373162
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 179 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Intrusion detection system is a vital part of computer security system commonly used for precaution and detection. It is built for classifier or descriptive or predictive model to proficient classification of normal behavior from abnormal behavior of IP packets. This book presents the solution regarding proper data transformation methods handling and importance of data analysis of complete data set which is apply on hybrid neural network approaches for used to cluster and classify normal and abnormal behavior to improve the accuracy of network based anomaly detection classifier. Because neural network classes only require the numerical form of data but IP connections or packets of network have some symbolic features which are difficult to handle without the proper data transformation analysis. For this reason, it got non redundant new NSL KDD CUP data set. The experimental results show that indicator variable is more effective as compared to the both conditional probabilities and arbitrary assignment method from measurement of accuracy and balance error rate.

About the Author

Saima Munawar has done MS in Computer Science from Lahore College for Women University, Lahore, Pakistan in 2010. She is currently working as faculty member in Virtual University, Lahore, Pakistan. Her research interests include Computer Network, Neural Network and Software Engineering.