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

TL;DR

This paper introduces BARTPredict, a proactive IoT cybersecurity framework using fine-tuned LLMs, notably BART and BERT, to predict and evaluate network traffic, significantly improving attack detection accuracy.

Contribution

The work presents a novel LLM-based framework for proactive IoT intrusion prediction, combining BART and BERT models for improved threat anticipation and mitigation.

Findings

Achieved 98% overall accuracy in IoT attack prediction

Enhanced proactive detection of malicious network traffic

Demonstrated effectiveness on CICIoT2023 dataset

Abstract

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.