Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities

• URL: http://arxiv.org/abs/2502.10599v1
• Date: Fri, 14 Feb 2025 23:11:51 GMT
• Title: Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities
• Authors: Milad Rahmati,
• Abstract summary: Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
• Score: 0.0
• License:
• Abstract: The rapid expansion of the Internet of Things (IoT) ecosystem has transformed various sectors but has also introduced significant cybersecurity challenges. Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. To address these issues, this study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. The framework enables decentralized data processing by training models locally on edge devices, ensuring data privacy. Secure aggregation of these locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information. The proposed framework utilizes recurrent neural networks (RNNs) for anomaly detection, optimized for resource-constrained IoT networks. Experimental results demonstrate that the system effectively detects complex cyber threats, including distributed denial-of-service (DDoS) attacks, with over 98% accuracy. Additionally, it improves energy efficiency by reducing resource consumption by 20% compared to centralized approaches. This research addresses critical gaps in IoT cybersecurity by integrating federated learning with advanced threat detection techniques. The framework offers a scalable and privacy-preserving solution adaptable to various IoT applications. Future work will explore the integration of blockchain for transparent model aggregation and quantum-resistant cryptographic methods to further enhance security in evolving technological landscapes.

Related papers

• Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security [1.2369895513397127]
  Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. To efficiently secure IoT devices, real-time detection of intrusion systems is critical. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security.
  arXiv  Detail & Related papers  (2024-10-01T19:24:34Z)
• A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain [29.968086297894626]
  We propose a framework named DFLoc to achieve precise 3D localization tasks. Specifically, we address the issue of single-point failure for a reliable and accurate indoor localization system. We introduce an updated model verification mechanism within the blockchain to alleviate the concern of malicious node attacks.
  arXiv  Detail & Related papers  (2024-07-08T04:14:19Z)
• A Cutting-Edge Deep Learning Method For Enhancing IoT Security [0.0]
  This paper proposes an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS 2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious.
  arXiv  Detail & Related papers  (2024-06-18T08:42:51Z)
• 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 Against DDoS Attacks through Federated Learning [0.0]
  Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Traditional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning.
  arXiv  Detail & Related papers  (2024-03-16T16:45:28Z)
• 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)
• Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems [30.16714420093091]
  Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. This article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods.
  arXiv  Detail & Related papers  (2023-12-05T04:42:04Z)
• FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination [1.0621485365427565]
  This paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC. We put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. To adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD.
  arXiv  Detail & Related papers  (2023-04-07T13:43:57Z)
• RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems [41.1371349978643]
  We present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data.
  arXiv  Detail & Related papers  (2022-08-27T14:50:00Z)
• HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [0.0]
  We propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
  arXiv  Detail & Related papers  (2022-04-08T19:06:16Z)
• Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM [31.480769801354413]
  This work aims to develop secure distributed algorithms to protect the learning from data poisoning and network attacks. We establish a game-theoretic framework to capture the conflicting goals of a learner who uses distributed support vector machines (SVMs) and an attacker who is capable of modifying training data and labels. The numerical results show that distributed SVM is prone to fail in different types of attacks, and their impact has a strong dependence on the network structure and attack capabilities.
  arXiv  Detail & Related papers  (2020-03-08T18:54:17Z)

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.