Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks
This addresses cybersecurity challenges for IoT networks, offering a robust solution, though it appears incremental as it builds on existing LLM and LSTM methods.
The paper tackles the problem of reactive intrusion detection in IoT networks by proposing a proactive framework that combines Large Language Models (LLMs) with LSTM networks to predict cyber attacks, achieving 98% accuracy on the CICIoT2023 dataset.
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.