Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm
Kinam Park, Youngrok Song, Yun-Gyung Cheong · 2018
In this paper, we present the results of our experiments to evaluate the performance of detecting different types of attacks (e.g., IDS, Malware, and Shellcode). We analyze the recognition performance by applying the Random Forest algorithm to the various datasets that are constructed from the Kyoto 2006+ dataset, which is the latest network packet data collected for developing Intrusion Detection Systems. We conclude with discussions and future research projects.