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

2024/08/26 by Alaeddine Diaf, Abdelaziz Amara Korba, Diaf, Alaeddine +5 · 4 citations
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Misinformation and Its Impacts #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2408.14045

openalex publication_date 2024/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway
• Network Intrusion Detection: Evolution from Conventional Approaches to LLM Collaboration and Emerging Risks
• Large Language Models for Security Operations Centers: A Comprehensive Survey
• Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence

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