Abstract:
An Intrusion Detection System (IDS) acts as a cyber security system which
monitors and detects any security threats for software and hardware running on the
network. Although there have many existing IDS but still face challenges in
improving accuracy in detecting security vulnerabilities, not enough methods to
reduce the level of alertness and detecting intrusion attacks. Machine learning
methods can detect data from past experience and differentiate normal and abnormal
data. In this system, the Convolutional Neural Network (CNN) in deep learning
method is used for solving the problem of identifying intrusion in a network. NSL –
KDD dataset is used to train the data with the CNN algorithm. The system
implementation is performed for balanced and unbalanced nature of NSL – KDD
dataset. The analysis of evaluation results describes the achievement of the proposed
system with the accuracy of 83% in balanced dataset and 80% in unbalanced dataset.
The proposed system is implemented by Python programming language on
Tensorflow platform.
