用RoBERTa模型提升网络攻击检测准确率，效果优于传统方法。
Cyber Threat Detection and Vulnerability Assessment System using Generative AI and Large Language Model
- 用加密数据训练改进的RoBERTa模型，增强文本上下文理解。
- 在真实流量数据上实现99%准确率、91%召回率、89%精确率。
- 适合安全研究人员和企业威胁检测系统开发者参考。
背景：近年来网络攻击迅速演变，个人和企业均受到勒索软件、恶意软件、钓鱼及拒绝服务（DoS）等攻击影响。挑战：传统模型如生成式人工智能和安全版BERT在处理文本数据时上下文理解有限，影响攻击检测效果。方法：提出基于鲁棒优化的BERT预训练方法（RoBERTa），结合分组捕获（PCAP）文件数据与全谐波加密（FHE）技术，使用字节级和字节对编码（BBPE）分词器生成令牌，再通过大规模训练的RoBERTa模型进行攻击检测与分类，最后采用Softmax输出结果。实验表明，该方法在准确率（0.99）、召回率（0.91）和精确率（0.89）上均优于现有BERT模型。
原文摘要 · Abstract (English)
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.
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