Abstract
The risk and severity of network intrusion has clearly received great attention in the last decade. Meanwhile, machine learning methods have been widely employed in the area of cybersecurity. This paper introduces the network intrusion attacks and detection systems and gives an overview of literature on various machine learning models to achieve network intrusion detection, including logistic regression, k-nearest neighbors, neural networks, random forest, decision tree, and k-means clustering. We find that as the dataset gets larger, the machine learning methods yield better performance significantly. Furthermore, we discuss the prospects mentioned in the literature and put forward some key prominent future research directions in network intrusion detection systems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
