Innovating Cybersecurity with Convolutional Neural Networks and Big Data Analytics: A Machine Learning Approach

Abstract

As cyber threats grow increasingly sophisticated, organizations must adopt innovative strategies to safeguard their digital assets. This paper explores the integration of Convolutional Neural Networks (CNNs) and Big Data analytics to enhance cybersecurity measures through a machine learning approach. By leveraging CNNs, which excel in processing and analyzing visual data, cybersecurity systems can effectively detect and classify anomalies in network traffic and system behavior. The application of Big Data analytics enables the processing of vast datasets, allowing for real-time threat detection and mitigation. This study evaluates the performance of CNNs in identifying various cyber threats, including malware, phishing attempts, and intrusion detection, while also examining the role of data preprocessing and feature extraction in improving model accuracy. The results demonstrate that CNNs, when combined with Big Data technologies, significantly enhance the capability to predict and respond to cyber threats efficiently. Furthermore, the research discusses the implications of this approach for future cybersecurity strategies, emphasizing the need for continuous adaptation to evolving threats. This innovative framework not only strengthens existing cybersecurity infrastructures but also sets the stage for developing more resilient and adaptive security solutions in the digital age.