TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns
  for Intrusion Detection
Dania Herzalla Willian T. Lunardi Martin Andreoni Lopez
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.