Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
Source abstract
Abstract
Cybersecurity is a critical concern in today’s digital age, where organizations face an ever-evolving cyber threat landscape. This study explores the potential of leveraging artificial intelligence and Amazon Web Services to improve cybersecurity practices. Combining the capabilities of OpenAI’s GPT-3 and DALL-E models with Amazon Web Services infrastructure aims to improve threat detection, generate high-quality synthetic training data, and optimize resource utilization. This work begins by demonstrating the ability of artificial intelligence to create synthetic cybersecurity data that simulates real-world threats. These data are essential for training threat detection systems and strengthening an organization’s resilience against cyberattacks. While our research shows the promising potential of artificial intelligence and Amazon Web Services in cybersecurity, it is essential to recognize the limitations. Continued research and refinement of AI models are needed to address increasingly sophisticated threats. Additionally, ethical and privacy considerations must be addressed when employing AI in cybersecurity practices. The results support the notion that this collaboration can revolutionize how organizations address cyber challenges, delivering greater efficiency, speed, and accuracy in threat detection and mitigation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Information and Cyber Security · Digital and Cyber Forensics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Escuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías en Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador
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