🤖 AI Summary
This work proposes a RoBERTa-based system for network threat detection and vulnerability assessment that addresses the limitations of existing security models—such as Security BERT—in identifying sophisticated cyberattacks like ransomware, phishing, and DoS within encrypted traffic due to insufficient contextual understanding. The approach extracts traffic data from PCAP files, preserves privacy through fully homomorphic encryption (FHE), and employs a byte-level Byte Pair Encoding (BBPE) tokenizer to generate semantically rich tokens for input into the RoBERTa model. Experimental results demonstrate significant improvements over current BERT-based models, achieving an accuracy of 0.99, recall of 0.91, and precision of 0.89, thereby enhancing semantic comprehension and detection efficacy for advanced threats in encrypted network traffic.
📝 Abstract
Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various ways. Cyber-attacks include various threats such as ransomware, malware, phishing, and Denial of Service (DoS)-related attacks. Challenges: Traditional models such as Generative Artificial Intelligence (AI) and Security Bidirectional Encoder Representations from Transformers (BERT) were implemented to detect cyber threats. However, the existing Security BERT model has a limited contextual understanding of text data, which has less impact on detecting cyber-attacks. Proposed Methodology: To overcome the abovementioned challenges, Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) model is proposed which consists of diverse words of vocabulary understanding. Initially, data are extracted from a Packet Capture (PCAP) file and encrypted using Fully Harmonic Encryption (FHE). Subsequently, a Byte-level and Byte Pair Encoding (BBPE) tokenizer was used to generate tokens and help maintain the vocabulary for the encrypted values. Then, these values are applied to the RoBERTa model of the transformer with extensive training. Finally, Softmax is used for the detection and classification of attacks. The proposed RoBERTa model achieved better results than the existing BERT model in terms of accuracy (0.99), recall (0.91), and precision (0.89) respectively.