A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest

<p>The evolving area of cybersecurity presents a dynamic battlefield for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date.</p> <p>A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented problems of low accuracy, but the growth in the area of machine learning over the last decade has led to vast improvements in machine learning algorithms and their requirements.</p> <p>This research applies k nearest neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature selection improve the increase the detection accuracy using the K-fold cross validation algorithm on the random forest algorithm on approximately 126,000 samples of the NSL-KDD dataset.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2019

Author dc:creator

• Atawodi, Ilemona S

Contributors dc:contributor

• Zhaoxian Zhou
• Chaoyang Zhang
• Kuo Lane Chen

• Computational Engineering
• Computer Engineering
• Engineering

Repository record dc:identifier

https://aquila.usm.edu/masters_theses/651

OAI identifier oai:identifier

oai:aquila.usm.edu:masters_theses-1707