The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on. Ensuring an accurate reflection of the multifaceted nature of benign and malicious traffic in these datasets is essential for creating models capable of recognizing and responding to a wide array of intrusion patterns. However, existing datasets often fall short, lacking the necessary diversity and alignment with the contemporary network environment, thereby limiting the effectiveness of intrusion detection. This paper introduces TII-SSRC-23, a novel and comprehensive dataset designed to overcome these challenges. Comprising a diverse range of traffic types and subtypes, our dataset is a robust and versatile tool for the research community. Additionally, we conduct a feature importance analysis, providing vital insights into critical features for intrusion detection tasks. Through extensive experimentation, we also establish firm baselines for supervised and unsupervised intrusion detection methodologies using our dataset, further contributing to the advancement and adaptability of intrusion detection models in the rapidly changing landscape of network security. Our dataset is available at https://kaggle.com/datasets/daniaherzalla/tii-ssrc-23.

该研究介绍了一个新颖且全面的数据集TII-SSRC-23，旨在解决现有数据集在多样性和与当代网络环境的一致性方面的挑战。通过广泛实验，为监督和无监督入侵检测方法建立了坚实的基准，并提供了对入侵检测任务关键特征的重要见解，从而进一步推动入侵检测模型在网络安全快速变化的领域中的进展和适应性。

TII-SSRC-23 数据集：用于入侵检测的多样化流量模式的类型学探索