Detecting Simulated Attacks in Computer Networks Using Resilient Propagation Artificial Neural Networks

 

Mario A. Garcia and Tung Trinh

 

The authors are with Texas A & M University-Corpus Christi, Computer Science, 6300 Ocean Dr., Corpus Christi, TX, USA. (e-mail: mario.garcia@tamucc.edu, tung.trinh@tamucc.edu).

 

Manuscript received on January 15, 2015,
Accepted for publication on May 10, 2015,
Published on June 15, 2015.

 

Abstract

In a large network, it is extremely difficult for an administrator or security personnel to detect which computers are being attacked and from where intrusions come. Intrusion detection systems using neural networks have been deemed a promising solution to detect such attacks. The reason is that neural networks have some advantages such as learning from training and being able to categorize data. Many studies have been done on applying neural networks in intrusion detection systems. This work presents a study of applying resilient propagation neural networks to detect simulated attacks. The approach includes two main components: the Data Preprocessing module and the Neural Network. The Data Preprocessing module performs normalizing data function while the Neural Network processes and categorizes each connection to find out attacks. The results produced by this approach are compared with present approaches.

Key words: Computer security, artificial neural network, resilient propagation.

 

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