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Network Anomaly Detection using Predictive Analysis in Machine Learning

Verma, Ritu (2020) Network Anomaly Detection using Predictive Analysis in Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

With immense growth and rapid rise in detection of intrusion, undoubtedly it plays a key role in the security of existing systems. The present approaches available in the systems for detection of intrusion are somehow adequate and less effective to an extent. Many conventional approaches to accentuate (IDS) Intrusion Detection system claims an artificial neural network to be better in comparison to traditional methods. However, the strategies based on ANN require enhancement, exceptionally for less frequent attacks. In this research, a novel approach based on ANN ( artificial neural network ) and genetic algorithm for feature selection with optimal number of feature value are proposed. This new approach is proposed to achieve better accuracy and resolve the problem by aiming to gain more stability with a less false positive rate for the detection system. Results achieved through experiments on NSL KDD dataset demonstrate that the proposed approach in this paper, outer-performs the existing esteemed methods concerning false -positive rate and accuracy.
Keywords : ANN (Artificial neural network), Back propagation algorithm, genetic algorithm, random forest, recursive feature elimination ,anomalies.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 25 Jan 2021 16:39
Last Modified: 25 Jan 2021 16:39
URI: http://norma.ncirl.ie/id/eprint/4479

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