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ChatGPT: Technology Frontiers and Cybersecurity Challenges
- 1 School of software, Taiyuan University of Technology, Jinzhong, 030600, China
- 2 School of Computer Science and Technology, Anhui University, Hefei, 230601, China
- 3 Aliyun School of Big Data, Changzhou University, Changzhou 213000, China
- 4 Faculty of Engineering, Bristol, The University of Bristol, Bristol BS8 1QU, United Kingdom
* Author to whom correspondence should be addressed.
Abstract
This paper examines the evolution and application of OpenAI's advanced conversational AI, ChatGPT, particularly within the domain of cybersecurity. With an architecture built on the Transformer model, ChatGPT demonstrates significant capabilities in language understanding and generation. It leverages vast datasets, ranging from social media posts to technical documents, ensuring the model adapts to diverse fields and maintains compliance with privacy and security regulations. The paper explores ChatGPT's role in network security, highlighting its proficiency in threat detection, vulnerability assessment, and incident response, essential as regulations like GDPR and CCPA become more stringent. Furthermore, the study addresses potential security risks associated with AI, such as phishing and misinformation, and discusses mitigation strategies through advanced training techniques like adversarial training and multi-task learning. A novel variational autoencoder (VAE)-based method, T-VAE, is introduced, offering enhanced generalization capabilities across different tasks and scenarios. The findings suggest that while ChatGPT has made significant strides in cybersecurity applications, continuous improvements in model robustness and adaptability are necessary to mitigate emerging threats and adapt to evolving digital landscapes.
Keywords
ChatGPT, Cybersecurity, Proficiency.
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Cite this article
Mao,X.;Xu,S.;Yang,G.;Yang,Y. (2024). ChatGPT: Technology Frontiers and Cybersecurity Challenges. Applied and Computational Engineering,110,65-70.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning
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