In recent times, the importance of cybersecurity has surged significantly, affecting both organizations and individuals alike. The proliferation of sophisticated cyber-attacks underscores the critical need for effective and efficient means of identifying and mitigating these threats. Convolutional Neural Networks (CNNs), originally prominent in computer vision tasks, have recently garnered attention within the realm of network security. This paper introduces a CNN-driven method for detecting cybersecurity threats through machine learning. We assess the performance of our approach using a publicly available dataset and juxtapose its results against those of contemporary state-of-the-art techniques. Our findings illustrate that our CNN-based approach surpasses alternative methods in terms of accuracy and detection rates. Furthermore, we delve into an analysis of the features acquired by the CNN, offering valuable insights into the attributes of diverse cyber-attacks. Consequently, our approach presents a valuable tool for enhancing network security across various domains, including finance, healthcare, and government sectors.