In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intrusion detection systems remain mostly reactive, responding to specific patterns or observed anomalies. This work proposes a proactive approach to anticipate and mitigate malicious activities before they cause damage. This paper proposes a novel network intrusion prediction framework that combines Large Language Models (LLMs) with Long Short Term Memory (LSTM) networks. The framework incorporates two LLMs in a feedback loop: a fine-tuned Generative Pre-trained Transformer (GPT) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) for evaluating the predicted traffic. The LSTM classifier model then identifies malicious packets among these predictions. Our framework, evaluated on the CICIoT2023 IoT attack dataset, demonstrates a significant improvement in predictive capabilities, achieving an overall accuracy of 98%, offering a robust solution to IoT cybersecurity challenges.

翻译：近年来，大规模网络攻击利用物联网设备的现象屡见不鲜，且随着物联网技术的持续普及，预计此类攻击将进一步升级。尽管在攻击检测方面已付出巨大努力，入侵检测系统大多仍处于被动响应状态，仅针对特定模式或已观测到的异常行为做出反应。本研究提出了一种主动预测方法，旨在恶意活动造成损害之前进行预警与缓解。本文提出了一种新颖的网络入侵预测框架，该框架将大型语言模型与长短期记忆网络相结合。该框架在反馈循环中整合了两个LLM：一个用于预测网络流量的微调生成式预训练Transformer模型，以及一个用于评估预测流量的微调双向编码器表示模型。随后，LSTM分类器模型在这些预测中识别恶意数据包。我们的框架在CICIoT2023物联网攻击数据集上进行了评估，结果表明其预测能力显著提升，整体准确率达到98%，为物联网网络安全挑战提供了一个强有力的解决方案。