用大模型提前预测物联网攻击，准确率98%。
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction
- 用微调的BART模型预测网络流量，再用BERT评估异常。
- 在CICIoT2023数据集上达到98%预测准确率。
- 适合关注物联网安全预警的开发者与安全团队。
物联网技术在各领域广泛应用，带来便利的同时也引入了新的网络安全风险，近期大规模针对物联网设备的攻击事件已凸显此问题。传统入侵检测系统多为被动响应，依赖于网络中特定模式或异常触发。为应对这一挑战，本文提出一种主动预测机制，旨在提前预判并阻断恶意行为，防止潜在损害发生。该研究设计了一种基于预训练大语言模型（LLM）的入侵预测框架，集成两个微调模型：采用微调的双向自回归变换器（BART）模型进行网络流量预测，以及采用微调的双向编码器表示（BERT）模型对预测结果进行评估。通过BART的双向能力，框架进一步识别出其中的恶意数据包。在CICIoT2023物联网攻击数据集上的评估表明，该框架显著提升了预测性能，整体准确率达到98%，为应对物联网网络面临的网络安全挑战提供了有力解决方案。
原文摘要 · Abstract (English)
The integration of Internet of Things (IoT) technology in various domains has led to operational advancements, but it has also introduced new vulnerabilities to cybersecurity threats, as evidenced by recent widespread cyberattacks on IoT devices. Intrusion detection systems are often reactive, triggered by specific patterns or anomalies observed within the network. To address this challenge, this work proposes a proactive approach to anticipate and preemptively mitigate malicious activities, aiming to prevent potential damage before it occurs. This paper proposes an innovative intrusion prediction framework empowered by Pre-trained Large Language Models (LLMs). The framework incorporates two LLMs: a fine-tuned Bidirectional and AutoRegressive Transformers (BART) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model for evaluating the predicted traffic. By harnessing the bidirectional capabilities of BART the framework then identifies malicious packets among these predictions. Evaluated using the CICIoT2023 IoT attack dataset, our framework showcases a notable enhancement in predictive performance, attaining an impressive 98% overall accuracy, providing a powerful response to the cybersecurity challenges that confront IoT networks.
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