Ddos Detection in Mitigation Using Deep Learning with Cyber Security

Cybersecurity is a significant concern for financial institutions due to the fast advancement of technology and the growing use of digital services. Robust safety protocols must be implemented to secure sensitive financial information and shield customers from possible risks arising from the increase in cyber-attacks aimed at digital enterprises. Data privacy is crucial in the banking sector to preserve clients' sensitive information, avert financial crime, assure regulatory compliance, and protect intellectual property. The work has gotten tougher owing to the rise in internet use and digital transactions. In this context, a DDoS assault represents a significant threat that compromises client privacy. Effective and resilient strategies for attack detection and prevention are essential. Deep Learning (DL) is the most efficient method for implementing cyber-attack detection systems. The performance of the models was evaluated based on recall, precision, accuracy, and F1-score measures. Assessed methodologies indicate that deep learning has a validated efficacy in identifying DDoS assaults, with a classification accuracy of 99.77%.