An Intelligent Tree-Based Intrusion Detection Model for Cyber Security

The widespread use of the Internet of Things and distributed heterogeneous devices has shed light on the implementation of efficient and reliable intrusion detection systems. These systems should be able to efficiently protect data and physical devices from cyber-attacks. However, the huge amount of data with different dimensions and security features can affect the detection accuracy and increase the computation complexity of these systems. Lately, Artificial Intelligence has received significant interest and is now being integrated into these systems to intelligently detect and protect against cyber-attacks. This paper aims to propose an intelligent intrusion detection model to predict and detect attacks in cyberspace. The model is designed based on the concept of Decision Trees, taking into consideration the ranking of the security features. The model is applied to a real dataset for network intrusion detection systems. Moreover, it is validated based on predefined performance evaluation metrics, namely accuracy, precision, recall and Fscore. Meanwhile, the experimental results reveal that our tree-based intrusion detection model can detect and predict cyber-attacks efficiently and reduce the complexity of computation process compared to other traditional machine learning techniques.

Automated summary

This paper proposes an intelligent intrusion detection model based on Decision Trees to efficiently predict and detect cyber-attacks while reducing computation complexity. The model is applied to real datasets and validated using predefined performance evaluation metrics, showing efficient prediction of attacks.