Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P106Keywords:
Cloud Security, DDoS Detection, Random Forest, Machine Learning, CICDDoS2019 Dataset, Threat Detection, CybersecurityAbstract
The high growth rate of cloud computing where protection of digital assets due to the nature of the threat is of paramount importance especially in reducing cyber threats such as Distributed Denial-of-Service (DDoS) attacks. In the paper, I have suggested an intelligent framework of threat detection based on the Random Forest (RF) model to improve cloud security. This model was trained and also tested on the CIC-DDoS2019 dataset, with the capabilities of ensemble learning used to attain high levels of classification. In the experiment, the RF model attained an 99.97% accuracy with precision, recall of 99.97%, and an F1-score of 99.98 %. The better performance of the proposed approach is proven by making a comparative analysis with other models, Gradient Boosting (96.7%), Logistic Regression (95.0%), Support Vector Machine (94.32%). The model robustness is also confirmed by ROC curves, confusion matrix analysis and training-validation trends. Such results define Random Forest as an exceptionally efficient and predictable framework when it comes to countering changing cyber risks in cloud environments, with promise of both scalability and real-time effectiveness when implemented into practice
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