Open AccessArtificial Neural Network Model for Predicting Fraudulent Attacks
Author(s) -
P. Archana,
P. Divyabharathi,
Y. Camry Joshya,
. Sudha
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1979/1/012016
Subject(s) - artificial neural network , computer science , artificial intelligence , gradient descent , sigmoid function , field (mathematics) , the internet , machine learning , network security , phishing , computer security , world wide web , mathematics , pure mathematics
Now a days Artificial Intelligence is an emerging technology. Neural network concepts used in many applications at present situation. The usage of internet increases day by day as well as the lack of security increases day by day. Mainly phishing scams emerges highly in case of network security. In this paper Neural network concepts, how to train and test the data using Artificial neural network has been discussed which gives an brief idea about usage of Neural net concepts in field of Network security. The properties such as feed forward back propagation network form, gradient descent momentum training purpose, sigmoid transfer function, supervised learning model used to train the model for predicting fraudulent attacks.