Gold Rush Optimization-Driven Random Forest Approach for Intrusion Detection System in Edge and Fog Computing Settings
Keywords:
Edge computing, fog computing, Gold Rush Optimization-Driven Random Forest (GRO-RF), Intrusion Detection Systems (IDS), Security.Abstract
Although both edge and fog computing architectures improve latency minimization and real-time data processing, their scattered nature presents significant security problems, particularly in intrusion detection. In this study, we provided an innovative gold rush optimization-driven random forest (GRO-RF) technique for effective intrusion detection systems (IDS). To examine the performance, the UNSW-NB15 public dataset is utilized to train the suggested GRO-RF technique. The z-score normalization approach is used to preprocess the raw samples to rearrange the data without noise and duplicates. To extract important features, the cleaned data is subjected to additional processing throughout the feature extraction process using linear discriminate analysis (LDA). The RF technique is used to identify intrusions in edge and fog computing environments using the retrieved data. GRO is designed to enhance the misclassification of RF by increasing accuracy and reducing the error rate in categorization. The suggested method is implemented using a Python program and its efficacy in detecting intrusions is evaluated against other current approaches using various metrics such as precision (98.45%), accuracy (99%), F-measure (96.89%) and recall (97.25%). We show that, in edge and fog computing scenarios, the GRO-RF technique has the highest intrusion detection accuracy compared to the other methods, based on the results of the experiment.
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