Abstract:
This paper addresses the new challenge of develop a novel hybrid ANN model that will be effective
in detecting the real time network anomalies. The entire system will be designed, developed, and
evaluated within this paper. The main goal is to develop a Artificial neural network based algorithm
and use the hyperparameter tuning features to fine tune the model and get the best model as the
output to use in in a flask application.
A live network capturing module will be implemented and the flask application is designed to get
the lively captured data into the ANN model perform the predictions and passes to the Web
application to showcase to the end user. To train the data model, a cleaned, preprocessed dataset
was used to achieve the higher accuracy, performance, and efficiency rates.
The experimental results demonstrate the capability of the end Intrusion detection system to
identify the true network attacks as network anomaly within a very short time. The author explored
the integration of RFE mechanism to use the feature selection for the model training as a extra
achievement as well. With the increasing volume of the network attacks started against the
complex networks within the cybersecurity industry, author wanted to deliver this system and
address the issue even for some distance. At the end comprehensive evaluations were carried out
by the experts as well as the author to prove the capability of the proposed system.
