Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach

• URL: http://arxiv.org/abs/2106.15349v1
• Date: Fri, 25 Jun 2021 06:33:35 GMT
• Title: Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach
• Authors: Sayan Chatterjee and Manjesh K. Hanawal
• Abstract summary: We first present an architecture for IDS based on hybrid ensemble model, named PHEC, which gives improved performance compared to state-of-the-art architectures. Next, we propose Noise-Tolerant PHEC in centralized and federated settings to address the label-noise problem. Experimental results on four benchmark datasets drawn from various security attacks show that our model achieves high TPR while keeping FPR low on noisy and clean data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Critical role of Internet of Things (IoT) in various domains like smart city, healthcare, supply chain and transportation has made them the target of malicious attacks. Past works in this area focused on centralized Intrusion Detection System (IDS), assuming the existence of a central entity to perform data analysis and identify threats. However, such IDS may not always be feasible, mainly due to spread of data across multiple sources and gathering at central node can be costly. Also, the earlier works primarily focused on improving True Positive Rate (TPR) and ignored the False Positive Rate (FPR), which is also essential to avoid unnecessary downtime of the systems. In this paper, we first present an architecture for IDS based on hybrid ensemble model, named PHEC, which gives improved performance compared to state-of-the-art architectures. We then adapt this model to a federated learning framework that performs local training and aggregates only the model parameters. Next, we propose Noise-Tolerant PHEC in centralized and federated settings to address the label-noise problem. The proposed idea uses classifiers using weighted convex surrogate loss functions. Natural robustness of KNN classifier towards noisy data is also used in the proposed architecture. Experimental results on four benchmark datasets drawn from various security attacks show that our model achieves high TPR while keeping FPR low on noisy and clean data. Further, they also demonstrate that the hybrid ensemble models achieve performance in federated settings close to that of the centralized settings.

Related papers

• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model [8.19957400564017]
  Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant attention for addressing this concern. A challenge arises in federated learning due to the Byzantine Attack Problem, where malicious local models can compromise the global model's performance during aggregation. This article proposes the integration of Byzantine-Robust Federated Learning (BRLF) model that combines federated learning with blockchain technology.
  arXiv  Detail & Related papers  (2023-10-20T10:21:50Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids [5.705281336771011]
  False data injection attack (FDIA) is one of the classes of attacks that target the smart measurement devices by injecting malicious data. We propose a federated learning-based scheme combined with a hybrid deep neural network architecture. We validate the proposed architecture by extensive simulations on the IEEE 57, 118, and 300 bus systems and real electricity load data.
  arXiv  Detail & Related papers  (2023-06-17T20:29:55Z)
• Benchmarking FedAvg and FedCurv for Image Classification Tasks [1.376408511310322]
  This paper focuses on the problem of statistical heterogeneity of the data in the same federated network. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv) have already been proposed. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.
  arXiv  Detail & Related papers  (2023-03-31T10:13:01Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• FedCC: Robust Federated Learning against Model Poisoning Attacks [0.0]
  Federated Learning is designed to address privacy concerns in learning models. New distributed paradigm safeguards data privacy but differentiates the attack surface due to the server's inaccessibility to local datasets.
  arXiv  Detail & Related papers  (2022-12-05T01:52:32Z)
• FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity [56.82825745165945]
  Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. We propose a hierarchical synchronous FL framework, i.e., FedHiSyn, to tackle the problems of straggler effects and outdated models. We evaluate the proposed framework based on MNIST, EMNIST, CIFAR10 and CIFAR100 datasets and diverse heterogeneous settings of devices.
  arXiv  Detail & Related papers  (2022-06-21T17:23:06Z)
• Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder [23.860842627883187]
  We teach the model to capture broader variations of the feature distributions with a novel noise-enhanced supervised autoencoder (NSAE) NSAE trains the model by jointly reconstructing inputs and predicting the labels of inputs as well as their reconstructed pairs. We also take advantage of NSAE structure and propose a two-step fine-tuning procedure that achieves better adaption and improves classification performance in the target domain.
  arXiv  Detail & Related papers  (2021-08-11T04:45:56Z)
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
  Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain. We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one. Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
  arXiv  Detail & Related papers  (2020-12-08T08:52:00Z)
• Searching Central Difference Convolutional Networks for Face Anti-Spoofing [68.77468465774267]
  Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods rely on stacked convolutions and expert-designed network. Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC)
  arXiv  Detail & Related papers  (2020-03-09T12:48:37Z)

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.