What question did this study set out to answer?

This research aims to improve network security through an effective intrusion detection system (IDS) utilizing a feed-forward neural network.

January 1, 2010

Intrusion Detection and Attack Classification Using Feed-Forward Neural Network

Key Points

This research aims to improve network security through an effective intrusion detection system (IDS) utilizing a feed-forward neural network.
Developed a 2-layered feed-forward neural network for intrusion detection.
Employed an early stopping strategy during the training phase to address overfitting.
Utilized the DARPA dataset for evaluation and performed feature preprocessing with a range adjustment to [-1, 1].
The system demonstrated high precision in detecting intrusions compared to similar systems.
Experimental modifications significantly impacted the final detection results.
The proposed approach maintains simplicity while achieving effective performance.

Abstract

Fast Internet growth and increase in number of users make network security essential in recent decades. Lately one of the most hot research topics in network security is intrusion detection systems (IDSs) which try to keep security at the highest level. This paper addresses a IDS using a 2-layered feed-forward neural network. In training phase, “early stopping” strategy is used to overcome the “over-fitting” problem in neural networks. The proposed system is evaluated by DARPA dataset. The connections selected from DARPA is preprocessed and feature range is converted into -1, 1. These modifications affect final detection results notably. Experimental results show that the system, with simplicity in comparison with similar cases, has suitable performance with high precision.

Mark Helpful

Bookmark

Relay