Intrusion detection using machine learning algorithms

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Deepthi Hassan Lakshminarayana (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: With the growing rate of cyber-attacks , there is a significant need for intrusion detection systems (IDS) in networked environments. As intrusion tactics become more sophisticated and more challenging to detect , this necessitates improved intrusion detection technology to retain user trust and preserve network security. Over the last decade , several detection methodologies have been designed to provide users with reliability , privacy , and information security. The first half of this thesis surveys the literature on intrusion detection techniques based on machine learning , deep learning , and blockchain technology from 2009 to 2018. The survey identifies applications , drawbacks , and challenges of these three intrusion detection methodologies that identify threats in computer network environments. The second half of this thesis proposes a new machine learning Model for intrusion detection that employs random forest , naive Bayes , and decision tree algorithms. We evaluate its performance on a standard dataset of simulated network attacks used in the literature , NSL-KDD. We discuss preprocessing of the dataset and feature selection for training our hybrid model and report its performance using standard metrics such as accuracy , precision , recall , and f-measure. In the final part of the thesis , we evaluate our intrusion model against the performance of existing machine learning models for intrusion detection reported in the literature. Our model predicts the Denial of Service (DOS) attack using a random forest classifier with 99.81% accuracy , Probe attack with 97.89% accuracy , and R2L attack with 97.92% accuracy achieving equivalent or superior performance in comparison with the existing models.

Additional Information

Publication

Thesis

Language: English

Date: 2019

Keywords

intrusion detection, IDS, security, network, machine learning, deep learning, blockchain, NSL KDD, algorithms, classifiers

Subjects

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