Friday 29th of March 2024
 

Effective Anomaly Intrusion Detection System based on Neural Network with Indicator Variable and Rough set Reduction


Rowayda Abd El-Hamid Sadek, Mahmoud Sami Soliman and Hagar Saad El-Din El-Sayed

Intrusion detection system (IDS) is an important tool for the defense of a network against attacks. It monitors the activities occurring in a computer system or network and analyzes them for recognizing intrusions to protect the computer network. Most of the existing IDSs use all of the 41 features available in the network packet to analyze and look for intrusive pattern, while some of these features are redundant and irrelevant. The weakness of this approach is the time-consuming during detection process and degrading the performance of IDSs. A well-defined feature selection algorithm makes the classification process more effective and efficient. In this paper a new hybrid algorithm NNIV-RS (Neural Network with Indicator Variable using Rough Set for attribute reduction) algorithm is used to reduce the amount of computer resources like memory and CPU time required to detect attack. Rough Set Theory is used to select out feature reducts. Indicator Variable is used to represent dataset in more efficient way. Neural network is used for network traffic packet classification. Tests and comparison were done on NSL-KDD dataset which is the improved version of KDD99 data set. The experiments results showed that the proposed algorithm gives better and robust representation of data as it was able to select features resulting in 80.4% data reduction, select significant attributes from the selected features and achieve detection accuracy about 96.7% with a false alarm rate of 3%.

Keywords: intrusion detection, feature selection, indicator variable, neural network, NSL-KDD.

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ABOUT THE AUTHORS

Rowayda Abd El-Hamid Sadek
Associate Prof. Rowayda A. Sadek received her PhD. in 2005 from Alexandria University, Alexandria, Egypt in Communication and Electronics Engineering. She is currently working as Associate Prof. in Information Technology Department, Faculty of Computers and Information, Helwan University, Cairo, Egypt. She worked as Assoc. Prof. in Computer Engineering Department, in the College of Engineering & Technology in AASTMT. Her research interests include Computer Networking and Security, Multimedia Processing for image, audio, video, etc. also Multimedia Networking, and Security as interdisciplinary research.

Mahmoud Sami Soliman
has received his B.Sc of Computer Engineering and Automatic control from Tanta University, Egypt in 2001. Master of Computer Engineering and science from Menofia University , Egypt in 2007 and PhD of Computer Engineering from Central South university ,China in 2010. he is currently an expert in Saudi National Center for earthquakes and volcano.

Hagar Saad El-Din El-Sayed
has received her B.Sc of computer engineering from october 6 university, Egypt. Currently working for master degree in Arab Academy for Science and Technology & Maritime Transport.


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