Cyber Attack Detection Model to Improve Network Security using Text-to-Text Transfer Transformer

The sophistication of cybersecurity threats has grown, calling for more complex methods to effectively detect and prevent them. Innovative cybersecurity solutions are needed since traditional methods are unable to keep up with the ever-changing nature of cyber threats. By sifting through mountains of data in search of indicative patterns that point to malevolent activity, deep learning, a branch of AI, offers encouraging ways to identify and mitigate such dangers. In order to identify cyberattacks, this research investigates multiple deep learning models, such as CNNs, RNNs, and hybrid architectures. Using metrics like detection accuracy, false positive rates, and computational efficiency, we assess the effectiveness of detection systems powered by deep learning on various datasets. This research emphasizes the immense power of deep learning in the continuous battle against cyber threats. It proposes a Pattern Resemblance Verification for Cyber Attack Detection using Text-to-Text Transfer Transformer (PRV- CAD-T5T). The proposed model exhibits better performance in cyberattack detection when compared to traditional models.