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

arXiv: https://arxiv.org/abs/2501.01664 · HTML: https://mlsanity.com/paper/2501.01664/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 3 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [SecureBERT: A Domain-Specific Language Model for Cybersecurity](https://mlsanity.com/paper/2204.02685/) (2022)
- [Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV](https://mlsanity.com/paper/2407.02969/) (2024)
- [Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks](https://mlsanity.com/paper/2408.14045/) (2024)
- [Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks](https://mlsanity.com/paper/2407.03070/) (2024)
- [Multi-agent Reinforcement Learning-based Network Intrusion Detection System](https://mlsanity.com/paper/2407.05766/) (2024)
- [A Life-long Learning Intrusion Detection System for 6G-Enabled IoV](https://mlsanity.com/paper/2407.15700/) (2024)
- [AI-Driven Fast and Early Detection of IoT Botnet Threats: A Comprehensive Network Traffic Analysis Approach](https://mlsanity.com/paper/2407.15688/) (2024)

## Built on by (most cited first, 4 of 4)

- [A Survey on Agentic Security: Applications, Threats and Defenses](https://mlsanity.com/paper/2510.06445/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization](https://mlsanity.com/paper/2504.18814/) (2025)

## Abstract (arXiv)

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.