A Machine Learning-based Approach for Detecting Malicious Activities in Cloud Computing Environments
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Abstract
With the rapid proliferation of cloud computing technologies, the digital realm faces an increasing threat from cyber-attacks and malicious activities. The core essence of this research revolves around leveraging machine learning techniques to bolster the security measures in cloud environments. Our primary objectives are twofold: firstly, to detect potential threats at their nascent stages, ensuring timely mitigation; and secondly, to minimize the occurrence of false positives, which can lead to unnecessary resources consumption and potential downtimes. Historically, existing security systems in cloud environments have been plagued with a multitude of challenges. Delayed threat detections often result in increased vulnerabilities, while a higher rate of false positives can lead to resource inefficiencies and potential mistrust in security protocols. Moreover, as cloud infrastructures continue to expand, ensuring that security measures scale effectively is of paramount importance. To surmount these challenges, our methodology embarks on a comprehensive journey, dissecting threat vectors in a structured manner. The process commences with a rigorous phase of data collection, ensuring a diverse and representative dataset. This data undergoes a meticulous preprocessing phase, ensuring its quality and relevance. Subsequently, our approach employs advanced feature extraction mechanisms, utilizing Principal Component Analysis (PCA) to distill the most pertinent features from the vast array of data. The heart of our approach is a specialized machine learning algorithm, fine-tuned to optimize metrics such as accuracy, sensitivity, and specificity. Preliminary results have been encouraging, with our model boasting an impressive accuracy rate of 95%, coupled with a sensitivity of 94% and a precision of 93%. However, in the spirit of rigorous research, we also analyzed models that did not meet our benchmarks. An illustrative model, for instance, achieved an accuracy of 80% and precision of 73%, highlighting potential areas of refinement and the iterative nature of developing machine learning solutions.In encapsulation, this research underscores the potential of machine learning as a formidable tool in the arsenal against cyber threats in cloud computing.
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