JOURNAL ARTICLE

Network Intrusion Detection System Using Neural Networks

Abstract

This paper presents a neural network-based intrusion detection method for the internet-based attacks on a computer network. Intrusion detection systems (IDS) have been created to predict and thwart current and future attacks. Neural networks are used to identify and predict unusual activities in the system. In particular, feedforward neural networks with the back propagation training algorithm were employed in this study. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data sets. The experimental results on real-data showed promising results on detection intrusion systems using neural networks.

Keywords:
Intrusion detection system Artificial neural network Computer science Anomaly-based intrusion detection system Feedforward neural network Artificial intelligence Machine learning Data mining The Internet Intrusion Backpropagation Operating system

Metrics

107
Cited By
4.13
FWCI (Field Weighted Citation Impact)
3
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Network Security and Intrusion Detection
Physical Sciences →  Computer Science →  Computer Networks and Communications
Advanced Malware Detection Techniques
Physical Sciences →  Computer Science →  Signal Processing
Anomaly Detection Techniques and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence

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