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.

物联网和分布式异构设备的广泛使用为有效和可靠的入侵检测系统的实施提供了启示。这些系统应该能够有效地保护数据和物理设备免受网络攻击。但是，具有不同维度和安全特征的大量数据会影响检测精度并增加这些系统的计算复杂性。最近，人工智能引起了极大的兴趣，现在正被集成到这些系统中，以智能地检测和防御网络攻击。本文旨在提出一种智能入侵检测模型，以预测和检测网络空间中的攻击。该模型是根据决策树的概念设计的，并考虑了安全功能的等级。该模型被应用于网络入侵检测系统的真实数据集。此外，它是基于预定义的性能评估指标（即准确性，准确性，召回率和Fscore）进行验证的。同时，实验结果表明，与其他传统的机器学习技术相比，我们的基于树的入侵检测模型可以有效地检测和预测网络攻击，并降低计算过程的复杂性。