标题： BARTPredict：利用LLM驱动的网络威胁预测增强物联网安全 显示英文标题

标题： BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction

摘要： 物联网（IoT）技术在各个领域的集成带来了运营上的进步，但也引入了新的网络安全威胁漏洞，这由最近针对物联网设备的大规模网络攻击所证实。入侵检测系统通常是被动的，由网络中观察到的特定模式或异常触发。为了解决这一挑战，本工作提出了一种主动方法，以预见并预先缓解恶意活动，旨在在潜在损害发生之前防止其发生。本文提出了一种创新的入侵预测框架，该框架由预训练的大语言模型（LLMs）支持。该框架结合了两个LLMs：一个微调的双向和自回归变压器（BART）模型用于预测网络流量，一个微调的变压器双向编码器表示（BERT）模型用于评估预测的流量。通过利用BART的双向能力，该框架随后在这些预测中识别恶意数据包。使用CICIoT2023物联网攻击数据集进行评估，我们的框架展示了预测性能的显著提升，达到了惊人的98%总体准确率，为应对物联网网络面临的网络安全挑战提供了强大的响应。 显示英文摘要

摘要： 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.