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The effectiveness of network intrusion detection systems, predominantly based on machine learning, is 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 paramount for creating IDS 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 …