Authors :
Uwadia Anthony. O
Volume/Issue :
Volume 9 - 2024, Issue 7 - July
Google Scholar :
https://tinyurl.com/bddzykty
Scribd :
https://tinyurl.com/46hmf33j
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUL1158
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
A Convolution Neural Network (CNN)-based
Network Intrusion Detection Model for Cyber-attacks is
of great value in identifying and classifying attacks on
any network. The Knowledge Discovery in Database
Cup '99 dataset containing approximately 4,900,000
single connection vectors was divided into two phases;
75% of the total dataset was used during the learning
process of the machine learning technique, while 25%
was used on a fully trained model to validate and
evaluate its performance. The model's performance
indicated that it can detect and classify different classes
of attacks with an accuracy of 98% with 20 epochs at a
0.001 learning rate using machine learning. The model
loss for the training and validation was 7.48% and
7.98%, respectively, over 20 epochs, which implies that
the model performed better on the training dataset.
This study demonstrated that the convolutional Neural
network-based Network Intrusion Detection and
classification model shows high detection and low false
negative rates. The CNN model offers a high detection
rate and fidelity to unknown attacks, i.e., it can
differentiate between already-seen attacks and new
zero-day attacks. At the end of the experiment, the
proposed approach is suitable in modeling the network
IDS for detecting intrusion attacks on computer networks
thereby enabling a secured environment for the proper
functioning of the system
Keywords :
Component; Network; Intrusion Detection System (IDS); Convolutional Neural Network(CNN); Artificial Neural Network(ANN); Machine Learning (ML)
References :
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A Convolution Neural Network (CNN)-based
Network Intrusion Detection Model for Cyber-attacks is
of great value in identifying and classifying attacks on
any network. The Knowledge Discovery in Database
Cup '99 dataset containing approximately 4,900,000
single connection vectors was divided into two phases;
75% of the total dataset was used during the learning
process of the machine learning technique, while 25%
was used on a fully trained model to validate and
evaluate its performance. The model's performance
indicated that it can detect and classify different classes
of attacks with an accuracy of 98% with 20 epochs at a
0.001 learning rate using machine learning. The model
loss for the training and validation was 7.48% and
7.98%, respectively, over 20 epochs, which implies that
the model performed better on the training dataset.
This study demonstrated that the convolutional Neural
network-based Network Intrusion Detection and
classification model shows high detection and low false
negative rates. The CNN model offers a high detection
rate and fidelity to unknown attacks, i.e., it can
differentiate between already-seen attacks and new
zero-day attacks. At the end of the experiment, the
proposed approach is suitable in modeling the network
IDS for detecting intrusion attacks on computer networks
thereby enabling a secured environment for the proper
functioning of the system
Keywords :
Component; Network; Intrusion Detection System (IDS); Convolutional Neural Network(CNN); Artificial Neural Network(ANN); Machine Learning (ML)