Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks

TL;DR

This paper introduces a proactive IoT attack prediction framework that combines large language models with LSTM networks, significantly improving early detection accuracy of cyber threats in IoT networks.

Contribution

It presents a novel integration of LLMs and LSTM for proactive attack prediction, advancing beyond reactive detection methods in IoT cybersecurity.

Findings

Achieved 98% overall prediction accuracy on CICIoT2023 dataset.

Demonstrated improved early detection capabilities over traditional methods.

Validated the effectiveness of combining LLMs with LSTM in cybersecurity.

Abstract

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.