Intrusion Detection System Using Back Propagation Algorithm And Compare Its Performance With Self Organizing Map
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Pulchowk Campus
Abstract
Abstract- In recent years, internet and computers have been utilized by many people
all over the world in several fields. On the other hand, network intrusion and
information safety problems are ramifications of using internet. The growing network
intrusions have put companies and organizations at a much greater risk of loss. This
thesis proposes a new learning methodology towards developing a novel Intrusion
Detection System (IDS) by Back Propagation Neural Networks (BPN) and Self
Organizing Map (SOM) and compares the performance between them. The main
function of Intrusion Detection System is to protect the resources from threats. It
analyzes and predicts the behaviors of users and then these behaviors resemble either
an attack or the normal behavior. There are several existing techniques that provide
more security to the network, but most of these techniques are static. This thesis tests
the proposed method by a benchmark intrusion dataset to verify its feasibility and
effectiveness. Results show that choosing good hidden layers network and input data
will not only have impact on the performance, but also on the overall execution
efficiency. The proposed method can significantly reduce the training time and epoch
time. It provides a powerful tool to help supervisors analyze, model and understand
the complex attack behavior of electronic crime.The proposed methodology
implemented in sampled data from KddCup99 data set so that intrusion detection
attacks database is standard for the evaluation of intrusion detection systems.
Description
Abstract- In recent years, internet and computers have been utilized by many people
all over the world in several fields.
Citation
MASTER OF SCIENCE IN COMPUTER SYSTEM AND KNOWLWDGE ENGINEERING