This dataset is designed to support the training and evaluation of
reinforcement learning models in the context of network traffic
analysis. It is derived from an existing IoT network traffic dataset,
from which packet capture (pcap) files were selected and processed
following a custom methodology explained in [Methodological
Information](methodological-information). The resulting data
representation is based on a windowing approach, where network traffic
is segmented into fixed-size temporal windows.
Each window aggregates traffic instances and is labeled according to its
composition as benign, attack, or mixed (containing both benign and
malicious activity). The final datasets are generated through random
combinations of these windows, enabling the creation of diverse traffic
patterns that better reflect dynamic and random network conditions.
This structure facilitates the use of the dataset in reinforcement
learning scenarios, where agents must learn to identify, classify, or
respond to varying traffic behaviors over time. Additionally, the
evaluation datasets are generated following the same methodology as the
training datasets, but are kept separate and are not used during the
training process, allowing for an independent evaluation of model
performance.
