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In the current digital epoch, cybersecurity emerges as a paramount concern, given the relentless advancement of cyber threats that pose formidable challenges to global digital infrastructures. This thesis embarks on a comprehensive exploration, centering on advanced neural network models, particularly emphasizing the Convolutional Neural Network (CNN), to address the imperative need for resilient cyber defense mechanisms. Employing meticulous experimentation and analysis utilizing the 'Cyber Security Indexes' dataset, this study meticulously evaluates the performance of these models across a spectrum of cyber-attack types. The findings illuminate the CNN model's robustness and efficacy, portraying its potential in fortifying cybersecurity measures and countering the evolving landscape of threats. Throughout this exploration, with a focus on achieving a 96% accuracy threshold, the study outlines the implications of these advancements in fostering a more secure digital landscape on a global scale. The comprehensive insights drawn from this research collectively underscore the pivotal role of the CNN model in fortifying cybersecurity defenses, offering a beacon of hope in mitigating the escalating cyber threats prevalent in today's digital milieu.