earticle

영어

As cyberthreats continue to rise rapidly, there is an urgent need for high quality cybersecurity AI datasets. These datasets are essential in training advanced AI models that enhance cybersecurity measures. The construction of such datasets is often faced with data quality, diversity and ethical consideration issues. Moreover, current datasets suffer from bias, incompleteness, and real-world representations. Given the dynamic nature of emerging cyber threats, there is also need for real time updates that traditional methods often fail to avail. This results in outdated cybersecurity AI datasets. Another issue is the ethical handling of sensitive data where compliance with regulations such as General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPPA) are often overlooked, thus endangering ethical handling of data in cybersecurity AI systems. Thus, our paper proposes a conceptual framework to address the challenges by considering state of the art technologies such as edge computing, real time processing and machine learning for enhanced data collection, processing, labeling and feature extraction. We integrate in diverse data sources and other innovative methods making our framework achieve high quality datasets that are highly needed for enhanced AI model performance in cybersecurity AI applications. We also consider data privacy and compliance thus contributing to a achieving a more secure and resilient cyberspace.