Modern cybersecurity faces a relentless evolution of threats, ranging from stealthy phishing attempts to disruptive Denial of Service (DoS) attacks. While traditional detection models have served as the frontline, they often struggle with a limited contextual understanding of complex network traffic patterns. To address these critical gaps, researchers have unveiled a sophisticated framework centered on the RoBERTa model, which is a more robustly optimized version of the standard transformer architecture designed to capture deeper nuances in textual and numerical data. The proposed system introduces a rigorous security layer by extracting data from network packet captures and applying Fully Homomorphic Encryption (FHE). This specialized encryption allows the AI to process and analyze the information without ever needing to decrypt it, effectively preserving data privacy during the entire detection phase. By utilizing a Byte-level Byte Pair Encoding (BBPE) tokenizer, the model effectively maps these encrypted values into a structured vocabulary that the transformer can interpret and learn from. Performance metrics indicate a significant leap forward, with the system reaching a stellar 99% accuracy rate. To categorize these threats, the model employs a Softmax layer, a mathematical function that calculates probabilities to distinguish between different attack types like malware or ransomware. This high precision is complemented by a 0.91 recall score, ensuring that very few genuine threats slip through the cracks unnoticed. As businesses grapple with increasingly automated cyber-attacks, integrating such high-performing models into defensive infrastructures marks a pivotal shift toward proactive security.

Modern cybersecurity faces a relentless evolution of threats, ranging from stealthy phishing attempts to disruptive Denial of Service (DoS) attacks. While traditional detection models have served as the frontline, they often struggle with a limited contextual understanding of complex network traffic patterns. To address these critical gaps, researchers have unveiled a sophisticated framework centered on the RoBERTa model, which is a more robustly optimized version of the standard transformer architecture designed to capture deeper nuances in textual and numerical data. The proposed system introduces a rigorous security layer by extracting data from network packet captures and applying Fully Homomorphic Encryption (FHE). This specialized encryption allows the AI to process and analyze the information without ever needing to decrypt it, effectively preserving data privacy during the entire detection phase. By utilizing a Byte-level Byte Pair Encoding (BBPE) tokenizer, the model effectively maps these encrypted values into a structured vocabulary that the transformer can interpret and learn from. Performance metrics indicate a significant leap forward, with the system reaching a stellar 99% accuracy rate. To categorize these threats, the model employs a Softmax layer, a mathematical function that calculates probabilities to distinguish between different attack types like malware or ransomware. This high precision is complemented by a 0.91 recall score, ensuring that very few genuine threats slip through the cracks unnoticed. As businesses grapple with increasingly automated cyber-attacks, integrating such high-performing models into defensive infrastructures marks a pivotal shift toward proactive security.