DEVELOPMENT OF INTRUSION DETECTION SYSTEM IN NEURAL NETWORK

DEVELOPMENT OF INTRUSION DETECTION SYSTEM IN NEURAL NETWORK
Author(s):
P. Hemalatha
Keywords:
Intrusion Detection System, KNN, GBC, CBC, Random forest Classifier.
Abstract
A device or software programme known as an intrusion detection system (IDS) analyses network or system activity to look for signs of hostile activity. The construction of IDS in a neural network is suggested in this research. The IDS classification is separated using a larger dataset. The idea of transforming unclean data into clean data is known as data pre-processing. Before running the method, the dataset is pre-processed to look for missing values, noisy data, and other abnormalities. Chi-square-based feature extraction is used during the extraction process. The Chi-Square approach is used to process the extraction areas in order to extract various characteristics and choose the essential features in order to enhance classification. The efficient Chi-square method is employed in this project to determine feature extraction and feature selection. The chosen characteristics are then used to accurately classify data using the gradient boosting classifier (GBC), cat boosting classifier (CBC), K-Nearest neighbour (KNN), and random forest classifier. Python software is used in the execution of this project.
Article Details
Unique Paper ID: 161581 Publication Volume & Issue: Volume 10, Issue 5 Page(s): 151 - 156
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