• Development of a comprehensive artificial intelligence-based cybersecurity system.
  • Integration of diverse datasets to improve the understanding of AI performance across various scenarios and data types.
  • Incorporation of contemporary datasets to address the challenge of training and testing AI models on outdated data.
  • Design of AI-based cybersecurity solutions that seamlessly integrate into the entire system, promoting relevance to real-world scenarios.
  • Evaluation of solutions against a broad range of real-world scenarios and diverse attack types to ensure practical applicability.
  • Implementation of robust infrastructure and methodologies for the storage and analysis of extensive security data, addressing the big-data problem.
  • Rigorous testing of each AI model with varied datasets to enhance adaptability and robustness.
  • Thorough evaluation of specific datasets with various AI models to optimize their performance in different scenarios.
  • Contribution to the advancement of AI in cybersecurity by bridging existing research gaps.
  • Overall enhancement of the effectiveness, relevance, and practical application of AI-based cybersecurity solutions.