Adopting genetic algorithm to develop a neural network for recognition of network intrusion

Shashank, K and Balachandra, Mamatha (2019) Adopting genetic algorithm to develop a neural network for recognition of network intrusion. International Journal of Engineering and Technology(UAE), 7 (4). pp. 5869-5873. ISSN 2227-524X

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Abstract

Protecting the network from unapproved access and exposure is called network security. Machine learning has been significantly useful in detecting the attack patterns. Deep learning being one among them is the standard tool for feature extraction and transformation. Building a deep learning model becomes tedious due to the substantial computation involved. Ultimately it can be optimized by suitably selecting the hyper parameters to evolve a neural network that gives the best results. The genetic algorithm is one such algorithm that can be employed to optimize the training phase of a deep learning model by selection of the best parameters. In the paper, a genetic algorithm based deep learning model is proposed to build a network intrusion detection system. NSL KDD dataset with 41 feature attributes is used to train and test the model

Item Type: Article
Uncontrolled Keywords: Deep Learning; Neural Network; Genetic Algorithm; Network Intrusion; Network Security.
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 16 May 2019 05:29
Last Modified: 16 May 2019 05:29
URI: http://eprints.manipal.edu/id/eprint/153842

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