Artificial Intelligence | Design and Implementation of Entropy Based Artificially Immune Malware Detection System

Muhammad Ali (u. a.)

ISBN 10: 3845429992 / ISBN 13: 9783845429991

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

Artificial Intelligence | Design and Implementation of Entropy Based Artificially Immune Malware Detection System | Muhammad Ali (u. a.) | Taschenbuch | 76 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845429991 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106864461

Bibliographic Details

• Title: Artificial Intelligence | Design and Implementation of Entropy Based Artificially Immune Malware Detection System
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3845429992
• ISBN 13: 9783845429991
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 131 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Many Malware detection systems these days are using signature based techniques to detect malwares and viruses. The zero day or new infected files are not detected by these signature based Anti Viruses and their signature is generated only after they have done their damage. Hence it becomes very important for a user to constantly update the antivirus software. To overcome these problems, we have proposed a solution based on Artificial Intelligence techniques. So the clients will not require frequent updates and probability of detecting zero day infections will rise abruptly. This project is based on implementing data mining algorithms mainly C4.5 Decision Tree learner. We have generated a dataset on the basis of already known malicious executable files. A C4.5 decision tree is generated based on the generated dataset and the unknown executables are passed through the tree to classify the executable as a malicious or a benign file. The purpose is to get rid of the manual signature based Malware detection systems that require constant updated signatures and making systems artificially immune to unknown and zero day malicious executables.

About the Author

Graduated as a Computer Engineer from National University of Sciences and Technology, Pakistan in 2010. This research based project was my final year project which I completed with Abdul Haseeb and Muhammad Bilal Bhatti. Since graduating, I have been doing research in the field of Artificial Intelligence(AI) and also plan to do Masters in AI.