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Modelling of Intrusion Detection System
Using Artificial Intelligence
—
Evaluation
of Performance Measures
Manojit Chattopadhyay
Abstract In recent years, applications of internet and computers are growing
extremely used by many people all over the globe
so is the susceptibility of the
network. In contrast, network intrusion and information security problems are
consequence of internet application. The increasing network intrusions have placed
people and organizations to a great extent at peril of many kinds of loss. With the aim
to produce effectiveness and state-of-the-art concern, the majority organizations put
their applications and service things on internet. The organizations are even
investing huge money to care for their susceptible data from diverse attacks that they
face. Intrusion detection system is a signi
—
cant constituent to protect such infor-
mation systems. A state-of-the-art review of the applications of neural network to
Intrusion Detection System has been presented that reveals the positive trend
towards applications of arti
cial neural network. Various other parameters have been
selected to explore for a theoretical construct and identifying trends of ANN
applications to IDS. The research also proposed an architecture based on Multi Layer
Perceptron (MLP) neural network to develop IDS applied on KDD99 data set. Based
on the identi
ed patterns, the architecture recognized attacks in the datasets using the
back propagation neural network algorithm. The proposed MLP neural network has
been found to be superior when compared with Recurrent and PCA neural network
based on the common measures of performance. The proposed neural network
approach has resulted with higher detection rate (99.10 %), accuracy rate (98.89 %)
and a reduced amount of execution time (11.969 s) and outperforms the benchmark
results of six approaches from literature. Thus the analysis based on experimental
outcomes of the MLP approach has established the robustness, effectiveness in
detecting intrusion that can further improve the performance by reducing the com-
putational cost without obvious deterioration of detection performances.
Keywords Arti
cial
intelligence
Multilayer perceptron
Intrusion detection
system
Detection rate
Fasle alarm
KDDCUP99
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