Intrusion Detection with Machine Learning Using Open-Sourced Datasets
This addresses the data availability issue for independent researchers in network security, but it appears incremental as it focuses on using existing open-sourced datasets rather than introducing new methods.
The paper tackled the problem of intrusion detection by investigating whether open-sourced simulated network traffic data can be used to develop a robust model for recognizing denial of service or infiltration attacks, but no concrete results or numbers are provided in the abstract.
No significant research has been conducted so far on Intrusion detection due to data availability since, network traffic within companies is private information and no available logs can be found on the Internet for independent research. This paper aims to answer the question whether open-sourced data, that is usually simulated network traffic can assist in developing a robust model that will effectively recognize and deter possible denial of service or infiltration attacks.