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Testing a rich sample of cybercrimes dataset  by using powerful classifiers’ competences
  • Elrasheed Ismail Mohommoud ZAYID,
  • Abdulmalik A. HUMAYED,
  • Yagoub Abbker ADAM
Elrasheed Ismail Mohommoud ZAYID

Corresponding Author:[email protected]

Author Profile
Abdulmalik A. HUMAYED
Yagoub Abbker ADAM


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.  
04 Jan 2024Submitted to TechRxiv
10 Jan 2024Published in TechRxiv