LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems

• URL: http://arxiv.org/abs/2505.00240v1
• Date: Thu, 01 May 2025 01:18:54 GMT
• Title: LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems
• Authors: Yazan Otoum, Arghavan Asad, Amiya Nayak,
• Abstract summary: This paper presents a novel Large Language Model (LLM)-based framework for comprehensive threat detection and prevention in IoT environments.<n>The system integrates lightweight LLMs fine-tuned on IoT-specific datasets for real-time anomaly detection and automated, context-aware mitigation strategies.<n> Experimental results in simulated IoT environments demonstrate significant improvements in detection accuracy, response latency, and resource efficiency over traditional security methods.
• Score: 6.649910168731417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing complexity and scale of the Internet of Things (IoT) have made security a critical concern. This paper presents a novel Large Language Model (LLM)-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on IoT-specific datasets (IoT-23, TON_IoT) for real-time anomaly detection and automated, context-aware mitigation strategies optimized for resource-constrained devices. A modular Docker-based deployment enables scalable and reproducible evaluation across diverse network conditions. Experimental results in simulated IoT environments demonstrate significant improvements in detection accuracy, response latency, and resource efficiency over traditional security methods. The proposed framework highlights the potential of LLM-driven, autonomous security solutions for future IoT ecosystems.

Related papers

• LLMs meet Federated Learning for Scalable and Secure IoT Management [6.649910168731417]
  Traditional centralized architectures struggle with latency, privacy concerns, and excessive resource consumption.<n>This paper presents a novel Federated Learning-driven Large Language Model (FL-LLM) framework, designed to enhance IoT system intelligence.
  arXiv  Detail & Related papers  (2025-04-22T16:56:59Z)
• Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
  This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge.<n>We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic.<n>This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
  arXiv  Detail & Related papers  (2025-04-22T09:40:05Z)
• FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection [0.0]
  Internet of Things (IoT) devices have expanded the attack surface, necessitating efficient intrusion detection systems (IDSs) for network protection.<n>This paper presents FLARE, a feature-based lightweight aggregation for robust evaluation of IoT intrusion detection.<n>We employ four supervised learning models and two deep learning models to classify attacks in IoT IDS.
  arXiv  Detail & Related papers  (2025-04-21T18:33:53Z)
• Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things [11.185300073739098]
  Traditional Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks.<n>This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments.
  arXiv  Detail & Related papers  (2025-04-09T18:52:15Z)
• Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices [38.16309790239142]
  Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices make it challenging to deploy conventional computing-based IDSs. We propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset.
  arXiv  Detail & Related papers  (2024-06-04T20:36:21Z)
• Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems [1.749521391198341]
  The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. This paper focuses on improving the effectiveness of ML-based IDS at the edge level by introducing a novel method to find a balanced trade-off between cost and accuracy.
  arXiv  Detail & Related papers  (2024-04-29T21:26:18Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments [0.0]
  IoT as a domain has grown so much in the last few years that it rivals that of the mobile network environments in terms of data volumes as well as cybersecurity threats. The confidentiality and privacy of data within IoT environments have become very important areas of security research within the last few years. More and more security experts are interested in designing robust IDS systems to protect IoT environments as a supplement to the more traditional security methods.
  arXiv  Detail & Related papers  (2023-06-14T08:48:46Z)
• Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments [67.24598072875744]
  Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies. This can hamper dramatically subsequent decision-making, such as forecasting.
  arXiv  Detail & Related papers  (2022-06-15T19:46:59Z)
• Lightweight Collaborative Anomaly Detection for the IoT using Blockchain [40.52854197326305]
  Internet of things (IoT) devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. We present a distributed IoT simulation platform, which consists of 48 Raspberry Pis.
  arXiv  Detail & Related papers  (2020-06-18T14:50:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.