Convolutional Neural Network based Intelligent Network Intrusion Detection System
DOI:
https://doi.org/10.24113/ijoscience.v6i9.317Keywords:
Intrusion Detection System, Deep Learning, Convolution neural network, Random Forest, NSL-KDD, UNSW-NB 15.Abstract
Today cyberspace is developing tremendously, and the Intrusion Detection System (IDS) plays a key role in information security. The IDS, which operates at the network and host levels, should be able to identify various malicious attacks. The job of network-based IDSs is to distinguish between normal and malicious traffic data and trigger an alert in the event of an attack. In addition to traditional signature-based and anomaly-based approaches, many researchers have used various deep learning (DL) techniques to detect intruders, as DL models are capable of automatically extracting salient features from the input data packets. The application of the Convolutional Neural Network (CNN), which is often used to solve research problems in the visual and visual fields, is not much explored for IDS. In this research work the proposed model for intrusion detection is based on feature selection and reduction using CNN and classification using random forest. As compared to some existing work the proposed algorithm proves its efficiency in terms of high accuracy and high detection rate.
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Copyright (c) 2020 Levina Bisen, Sumit Sharma
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