Volume 15 • Issue 1 • PP: 225-232 • 2025
A Hybrid GA-GWO Method for Cyber Attack Detection Using RF Model
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Currently, building a high-performance attack detector for cyber threat should be an essential and challenging task to secure cloud system from malicious activities. Traditional methodologies have become subject to the challenge of overfitting, distributive and intricate system layout, comprehensibility and more extended time particles. Therefore, the proposed contribution can be an efficient solution to design and develop a secure system, which is able to recognize cyber threats from cloud systems. It includes preprocessing and normalization, feature extraction, optimization as well prediction modules. Normalization with the relevant per batch fast Independent Component Analysis (ICA) model. A Genetic Algorithm (GA) - Gray Wolf Optimization (GWO) is then used to select the discriminatory features for training and testing phases. In the end, GAGWO- Random Forest (RF) is employed to classify the flow of data as insider or outsider. The detection system is implemented by taking popular and publicly available datasets like BoT-IoT, KDD Cup’99 etc. The various percentage indicators of feasibility are used as a validation purpose like detection accuracy measuring and comparing with the suggested GAGWO-RF system. Overall Accuracy: The proposed GAGWO-RF system achieved an average accuracy rate at 99.8% on all datasets the used. From the performance study, we have noted that GAGWO-RF security model performs better than other models.
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References
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