Cyber Threat Detection and Vulnerability Assessment System using Generative AI and Large Language Model

TL;DR

This paper presents a novel cyber threat detection system that leverages an enhanced RoBERTa model trained on encrypted network data to improve accuracy and reliability in identifying various cyber-attacks.

Contribution

The study introduces a RoBERTa-based approach with specialized tokenization and encryption techniques for more effective cyber threat detection compared to existing models.

Findings

Achieved 0.99 accuracy in threat detection

Improved recall to 0.91 over previous models

Enhanced precision to 0.89 with the proposed system

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 above-mentioned 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.