Key goal for this study was to conduct a real network traffic sample
dataset and did a deep mining to survey for secure the Saudi community
by report how the Saudi cyberspace’s pattern is. A kind of a
heterogenous simultaneous optical multiprocessor exchange bus
architecture used as a backbone network for collecting the network
traffic. First, crucial cleaning processes were performed to clean the
very noisy and dirty dataset. A total of 1048575 datapoints and 22
features were considered for the model/data evaluation processes.
Second, Lazy predict mechanism was recruited to nominate the top-ranking
learning models candidates. Third, a powerful supervised computation
algorithms used to shape and picture the terra-Byte payload traffic
across the Saudi cyber domain. Finally, for choosing the best Saudi
cybercrime classification model, an intense digging processes were
experimented and analyzed. Performance metrics used are accuracy (Acc),
balanced accuracy (BAcc), F1-score, learning time taken, and confusion
matrix. Evaluating the performance of different models based on
“Destination” as target decision tree classifier (DTC) was the first
model (i.e., highest BAcc with low time taken) and Saudi Arabia was the
9th country as a generated source target.