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

arXiv: https://arxiv.org/abs/2408.14045 · HTML: https://mlsanity.com/paper/2408.14045/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [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)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices](https://mlsanity.com/paper/2306.14263/) (2023)
- [LogGPT: Log Anomaly Detection via GPT](https://mlsanity.com/paper/2309.14482/) (2023)

## Built on by (most cited first, 7 of 7)

- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence](https://mlsanity.com/paper/2508.10677/) (2025)
- [BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction](https://mlsanity.com/paper/2501.01664/) (2025)
- [Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokens](https://mlsanity.com/paper/2507.02964/) (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)
- [Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning](https://mlsanity.com/paper/2506.06730/) (2025)
- [Anticipating Adversary Behavior in DevSecOps Scenarios through Large Language Models](https://mlsanity.com/paper/2602.14106/) (2026)

## Abstract (arXiv)

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.