AI-Driven Cybersecurity Threat Detection Framework Using Tabnet and Pelican Algorithm in Cloud Environments on Synthetic Event Data

In this paper, a new hybrid AI-based cybersecurity system is presented that employs TabNet model to combine with the Pelican Optimization Algorithm (POA) to optimize features adaptively in clouds. The originality is in the fact that it combines time-frequency-based feature extraction with the help of STFT and metaheuristic feature selection with the help of POA which is specifically designed to work with non-stationary cybersecurity event data. This integration results in better interpretability, scalability, and detection accuracy of real-time monitoring of cloud threat. The growing popularity of cloud computing has increased the need to have advanced cybersecurity measures that are capable of combating emerging and less obvious threats of cyberattacks. This paper describes a cybersecurity system based on AI to select the most successful features in a cloud environment, where TabNet and Pelican Optimization are used. With the help of STFT, SMOTE and AES-256 encryption, it identifies the different types of cyberattacks with 98.24% accuracy. It is a resilient real-time threat identification and mitigation system that is scaled and interpretable. Such an approach can result in an increase in cybersecurity protection and can therefore be a useful asset toward the security of cloud-based systems against current and upcoming cyber-attacks.

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