Deep Learning for Cybersecurity: Threat Detection System Case Study

Abstract

Because cyber attacks are becoming smarter, more sophisticated security measures are required. Systems that detect these dangers are increasingly incorporating deep learning. Improved cybersecurity can be achieved thru the use of deep learning algorithms, which can enhance the detection of cyberattacks such as malware, phishing, and network intrusions. With the help of CNNs, RNNs, and deep neural networks, threat detection systems can sift thru massive datasets in near real-time. In doing so, they are able to spot patterns and outliers that could have gone unnoticed by others. This paper presents a case study that demonstrates how a deep learning-based threat detection system can improve the accuracy, efficiency, and scalability of cybersecurity operations compared to traditional methods. The requirement for large datasets and the possibility of assaults from other parties are two issues that arise when attempting to apply deep learning to cybersecurity. The results of this study demonstrate the potential impact of deep learning on threat detection and provide recommendations for improving the integration of this technology into cybersecurity systems.