A Metaheuristic Machine Learning Approach for Darknet Traffic Classification
Keywords:
Darknet Traffic Classification, Deep Learning, Recurrent Neural Networks (RNNs), Harris Hawks Optimization (HHO), Feature Selection, Classification AccuracyAbstract
The expansion of darknet traffic categorization necessitates recognizing and resolving its intricate nature. Conventional categorization methods often prove inadequate when confronted with the complexities inherent in darknet network data. This study presents a novel methodology that combines Recurrent Neural Networks (RNNs) with the Harris Hawks Optimization (HHO) method. This research thoroughly evaluated the classifier to enhance classification accuracy and address the prevailing classier in darknet datasets. The study results indicate that incorporating HHO led to a significant increase in precision, with a spike of 99.90%. Additionally, the recall metric showed notable improvement, reaching a value of 0.9998. Moreover, the balanced accuracy measure also shows a substantial enhancement. The usefulness of the combination of Recurrent Neural Networks (RNN) and Hybrid Harmony Optimization (HHO) is shown by this significant advancement. This innovation offers a possible answer to the issue of categorizing darknet data.
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