A Comprehensive Analysis of Intrusion Detection Datasets: Evaluation, Challenges, and Insights

Intrusion detection is an important part of cybersecurity, to protect against unauthorized access and malicious activities within computer networks. This paper offers all-inclusive analysis of intrusion detection datasets, that aims to focusing on evaluation, challenges, and the valuable insights they offer to the ground of cybersecurity. Reviewing notable intrusion detection datasets, the paper evaluates each dataset’s dimensions, variation, and data distribution. Additionally, issues with intrusion detection dataset adequacy are examined at, including data imbalance, a lack of diversity, and a lack of real-world representation. We learn important things about the positive aspects and negative aspects of employing the datasets that already exist for to this investigation. These observations help us comprehend how dataset quality impacts intrusion detection model performance. This paper accomplishes by offering recommendations and potential directions for the dataset generation, improvement, and sharing of intrusion detection datasets to address these challenges The insights presented in this study aim to direct researchers and practitioners towards better dataset exercise and improved intrusion detection capabilities as the cybersecurity landscape advances.