Harsenning Deep Learning Techniques for Predictive Cybersecurity: Challenges and Solutions

Predictive cybersecurity - an emerging field that aims to proactively identify and mitigate cyber threats applies deep learning techniques. The application of contemporary deep learning methods, such as Long Short-Term Memory networks (LSTMs), recurrent neural networks (RNNs), and Convolutional neural networks (CNNs), for malware classification, intrusion detection systems (IDS), and anomaly detection is explored in this study. Although these models are effective, they have some drawbacks, including as the constantly changing and dynamic nature of cyber-attacks, the need for large and varied datasets, and the interpretability-impairing black-box nature of deep learning models.This paper offers a hybrid strategy to address these issues by combining classic deep learning models with Generative Adversarial Networks (GANs) to boost data creation and detection performance. To attain improved accuracy and resilience, we also present an ensemble learning framework that combines the advantages of many techniques. The goal of the suggested remedies is to create resilient, adaptable, and explainable cybersecurity systems that are capable of real-time threat detection and prediction. The suggested techniques take advantage of data augmentation and transfer learning to overcome data shortages and enhance model generalisation. Furthermore, the integration of explainable AI techniques endeavours to augment the transparency and interpretability of the models, hence enhancing their dependability for cybersecurity specialists.Through a methodical approach to these problems, the research hopes to push predictive cybersecurity to new heights and offer a more proactive and safe defence against ever-more-advanced cyber-attacks.