Authors - Elrasheed Ismail Mohommoud Zayid, Abdulmalik A. Humayed, Yagoub Abbker Adam Abstract - This study aims to test machine learning (ML) classification analysis across a rich network traffic dataset sample to figure out the patterns of Saudi cyberspace and alert the community for cybersecurity risks. The network topology used for generating a sample dataset was a kind of heterogeneous simultaneous photonic multiprocessor exchange bus architecture (SOME-Bus). First, the dirty and noisy dataset was cleaned using essential cleaning procedures. Dimensions of 22 characteristics and 1048575 datapoints were considered for the model/data evaluation procedures. Second, the top-ranked learning model candidates were nominated by using the LazyPredict technique. Third, the Saudi cyber domain's terra-Byte payload traffic is shaped and visualized using a potent supervised computing algorithm. Powerful performance measure criteria were employed to calculate the model’s final accuracy and error rates. The findings conclude that Saudi was in the ninth rank with respect to cybercrime source; and, decision tree (DT) was the highest-performing algorithm with respect to destination address.
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