Deep Learning in Cybersecurity: Applications, Challenges, and Future Prospects

Cybersecurity risks are heightened by the quick proliferation of smart things and the growing frequency and severity of intrusions. Cybersecurity primarily guards against external assaults on the data, software, and hardware that are part of a system with an active internet connection. Cybersecurity is primarily used by organizations to guard against unwanted access to their records and systems. In this article review the various literature’s study on deep learning in cybersecurity. Additionally, explore the challenges, application and future prospects in Cybersecurity. It concluded that deep learning plays a crucial role in cybersecurity by enhancing intrusion detection, malware classification, and anomaly detection. Techniques like SMOTE address class imbalance, while models such as CatBoost and XGBoost outperform deep learning in identifying cyber threats. Challenges include handling untidy, hierarchical data, optimizing model parameters, and balancing accuracy with training time. Future advancements will focus on improving detection performance, securing neural networks against adversarial attacks, and optimizing models for resource-constrained environments. Integrating multiple deep learning models in parallel can enhance efficiency, making deep learning a vital tool for securing IoT networks and addressing evolving cybersecurity threats.