Deep Learning Approach and its Application in the Cybersecurity Domain

Cyber Security has become especially prominent and critical for organizations all over the world because of the fast advancements in technology and the increasing sophistication of modern cyber threats. Most traditional methods related to cyber security are unable to match the scale and complexity of modern cyber-attacks. Deep learning has emerged in this context as a powerful tool for offering brand-new approaches and avenues toward threat detection, anomaly identification, and protection of data. In this paper, we investigate the basic principles of deep learning and its significant architectures Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks which provide models for solving various issues in the field of cybersecurity. We describe the contribution of deep learning to intrusion detection systems, malware detection, phishing protection, DDoS attack mitigation, and fraud detection. Deep learning also presents some advantages in cybersecurity. In this paper, I reviewed the basic applications, challenges, and potential solutions for enhancing cybersecurity systems by techniques of deep learning. Every organization, right from financial institutes to the entire globe employing itself to reach a developmental stage, has been forced to consider the implications of cybersecurity threats carefully. Most traditional methods relevant to cybersecurity have been ineffective at combatting modern cyber-attacks' scale and complexity.