Securing the Digital Realm Data Engineering and Deep Learning in Cybersecurity Crime Prevention
A. Jeyaram, A. Muthukumaravel · 2024
The digital world of today is always evolving; hence strong cybersecurity measures are more crucial than before. Conventional cybersecurity defences frequently fall short in identifying and countering sophisticated cyberattacks because their actions are based on predetermined rulesets. The study suggests a novel way to address these problems by enhancing cybercrime prevention through the integration of data engineering and deep learning techniques. Deep learning model selection, preprocessing, and extensive data collection are used by the proposed system to identify tiny signs of cyber threats that elude traditional methods. Results of comparing performance indicators demonstrate notable improvements in detection precision, recall, detection accuracy, and reaction times over existing systems. With a 95% detection accuracy, a 2% false positive rate, and an average reaction time of 5 seconds, the proposed system vastly outperforms the existing system. Because it can distinguish between benign and hostile behaviour, the proposed system is a major advancement in proactive cyber security that gives businesses more precision and resilience in safeguarding their digital assets.