Enhancing IoT Security Against DDoS Attacks through Federated Learning

• URL: http://arxiv.org/abs/2403.10968v1
• Date: Sat, 16 Mar 2024 16:45:28 GMT
• Title: Enhancing IoT Security Against DDoS Attacks through Federated Learning
• Authors: Ghazaleh Shirvani, Saeid Ghasemshirazi, Mohammad Ali Alipour,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid proliferation of the Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Nonetheless, the escalating threat of Distributed Denial of Service (DDoS) attacks jeopardizes the integrity and reliability of IoT networks. Conventional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems, potentially compromising data privacy. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning that allows multiple IoT devices or edge nodes to collaboratively build a global model while preserving data privacy and minimizing communication overhead. The research aims to investigate Federated Learning's effectiveness in detecting and mitigating DDoS attacks in IoT. Our proposed framework leverages IoT devices' collective intelligence for real-time attack detection without compromising sensitive data. This study proposes innovative deep autoencoder approaches for data dimensionality reduction, retraining, and partial selection to enhance the performance and stability of the proposed model. Additionally, two renowned aggregation algorithms, FedAvg and FedAvgM, are employed in this research. Various metrics, including true positive rate, false positive rate, and F1-score, are employed to evaluate the model. The dataset utilized in this research, N-BaIoT, exhibits non-IID data distribution, where data categories are distributed quite differently. The negative impact of these distribution disparities is managed by employing retraining and partial selection techniques, enhancing the final model's stability. Furthermore, evaluation results demonstrate that the FedAvgM aggregation algorithm outperforms FedAvg, indicating that in non-IID datasets, FedAvgM provides better stability and performance.

Related papers

• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
  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.
  arXiv  Detail & Related papers  (2025-02-14T23:11:51Z)
• A Comparative Analysis of Machine Learning Models for DDoS Detection in IoT Networks [0.0]
  It evaluates the efficacy of different machine learning models, such as XGBoost, in detecting DDoS attacks from normal network traffic. The effectiveness of these models is analyzed, showing how machine learning can greatly enhance IoT security frameworks.
  arXiv  Detail & Related papers  (2024-11-08T12:23:41Z)
• FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses [50.921333548391345]
  Federated Learning is a privacy preserving decentralized machine learning paradigm. Recent research has revealed that private ground truth data can be recovered through a gradient technique known as Deep Leakage. This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-05T11:42:26Z)
• FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method [7.842334649864372]
  Internet of Things (IoT) devices across multiple sectors has escalated serious network security concerns. Traditional Machine Learning (ML)-based Intrusion Detection Systems (IDSs) for cyber-attack classification require data transmission from IoT devices to a centralized server for traffic analysis, raising severe privacy concerns. We introduce FedMADE, a novel dynamic aggregation method, which clusters devices by their traffic patterns and aggregates local models based on their contributions towards overall performance.
  arXiv  Detail & Related papers  (2024-08-13T18:42:34Z)
• 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)
• 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)
• Federated Deep Learning for Intrusion Detection in IoT Networks [1.3097853961043058]
  A common approach to implementing AI-based Intrusion Detection systems (IDSs) in distributed IoT systems is in a centralised manner. This approach may violate data privacy and prohibit IDS scalability. We design an experiment representative of the real world and evaluate the performance of an FL-based IDS.
  arXiv  Detail & Related papers  (2023-06-05T09:08:24Z)
• Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study [37.97034388920841]
  Federated edge learning can be essential in supporting privacy-preserving, artificial intelligence (AI)-enabled activities in digital twin 6G-enabled Internet of Things (IoT) environments. We propose an anticipatory study for poisoning attacks in federated edge learning for digital twin 6G-enabled IoT environments.
  arXiv  Detail & Related papers  (2023-03-21T11:12:17Z)
• 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)
• On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections [40.586736738492384]
  Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme. A curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects.
  arXiv  Detail & Related papers  (2020-12-11T12:44:37Z)

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.