Deep Learning for Cyber Defense: Advancing Threat Detection and Mitigation through Intelligent Architecture

BFP/URG/ICT/24/004

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Deep Learning for Cyber Defense: Advancing Threat Detection and Mitigation through Intelligent Architecture

Abstract

Modern cybersecurity efforts are guided by deep learning architectures, which have the potential to strengthen defence systems against the threats and risks of cyberattacks. In response to the growing numbers and impacts of malicious activities, organizations are increasingly relying on deep learning techniques to strengthen their threat detection and mitigation capabilities. Deep learning architectures present an appealing solution to the shortcomings of traditional cybersecurity techniques by utilizing advanced machine learning methods like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). These advanced architectures incorporate threat intelligence, malware analysis, vulnerability assessment, and intrusion detection among other cybersecurity areas. The implementation of such architectures and approaches clarifies the revolutionary potential of deep learning architectures in transforming cyber defence methods and minimising the constantly changing cyber threat landscape.  Our project is organised around the five milestones that are demonstrated in our timeline, and we have provided a thorough methodology that can guide the implementation process. Furthermore, we have determined that this initiative will have a significant positive influence on Oman in a number of different areas and fields.