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This study delves into the intricate dynamics of cybersecurity adoption within small and medium enterprises (SMEs), with a specific focus on the transformative potential of AI-driven solutions from a machine learning perspective. Leveraging a rich and diverse dataset of real-world cybersecurity incidents tailored to the SME context, the research meticulously investigates the efficacy of three prominent machine learning models: logistic regression, random forest, and gradient boost classifiers. Through a comprehensive evaluation using a suite of performance metrics, including accuracy, precision, recall, and F1 scores, the study scrutinizes the predictive capabilities of these models in forecasting and categorizing cyber threats encountered by SMEs. The findings unveil nuanced insights into the multifaceted challenges and opportunities inherent in the cybersecurity landscape of SMEs, shedding light on the complex interplay between AI-driven solutions and evolving cyber threats. While the analysis reveals commendable performance across the models, it also uncovers inherent limitations in accurately discerning and categorizing specific types of attacks. These findings underscore the critical need for ongoing refinement and optimization of AI-driven cybersecurity solutions, necessitating a continuous, iterative process informed by real-world data and adaptive learning mechanisms. By harnessing the strengths of machine learning and embracing a proactive stance towards cybersecurity, SMEs can fortify their defense mechanisms and safeguard their digital assets in the increasingly volatile and interconnected digital ecosystem.
Nadella et al. (Fri,) studied this question.