Hybrid Intrusion Detection System using K-Means and Random Tree Algorithms
Yi Yi Aung, Myat Myat Min · 2018
Intrusion detection is the process called intrusion identification. The action of entering a system without authorization is called intrusion. With the improved technology of advanced mobile devices such as smartphones, tablets, smart devices, and other computing devices, the number of network users is growing more and more. Therefore, network security is very important for all net consumers. IDSs are an essential part of the security boundary. As a result, they are now considered a mandatory security mechanism for critical networks. There are many traditional intrusion detection techniques. Research in the traditional analysis of intrusion detection technology, the statistical model for the establishment of the base, the management capacity and the battery and so on there are still disadvantages and disabilities, because the actual results of the tests cannot meet the requirements. The current methods used in IDS are numerous. Each method has advantages and disadvantages. Intrusion detection can also be considered a classification problem. In this research, we use K-means and Random Tree algorithms. The purpose of this paper is to show the accuracy of intrusion detection with the complexity of time when comparing the hybrid detection method and the only detection method. This model is verified using the KDD'99 dataset.