A Comparative Analysis of Deep Learning Based Techniques for Cyber Security

Over time, the need for cybersecurity has become a need. It consists of the entirety of actions involved in preventing hackers. It prevents the exploitation of not only the data in internet-connected systems but also the hardware and software entities from cyber threats. Deep learning (DL) techniques, with their ability to process vast data and detect complex patterns, offer significant advancements in identifying malware, intrusions, and anomalies. This study reviews various DL models like Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) applied to cybersecurity. Our comparative analysis of CNN and Long Short-Term Memory (LSTM) models reveals LSTM’s superior accuracy in detecting cyber threats, achieving $\mathbf{9 6 \%}$ compared to CNN’s 85%. The results highlight DL’s potential in creating adaptive security systems, though challenges in dataset preparation and model testing persist. Future work should focus on refining these datasets to enhance DL’s efficiency in cybersecurity applications.