Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security

• URL: http://arxiv.org/abs/2402.05332v1
• Date: Thu, 8 Feb 2024 00:23:42 GMT
• Title: Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security
• Authors: Abdurrahman Elmaghbub, Bechir Hamdaoui,
• Abstract summary: Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality. This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework.
• Score: 7.8344795632171325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. This interconnectivity raises concerns about privacy and security, given the potential network-wide impact of a single compromise. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality. This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework designed to serve as the device authentication layer within the ZT architecture, with a focus on resource-constrained IoT devices. At the core of EPS-CNN, a Convolutional Neural Network (CNN) is utilized to generate the device identity from a unique RF signal representation, known as the Double-Sided Envelope Power Spectrum (EPS), which effectively captures the device-specific hardware characteristics while ignoring device-unrelated information. Experimental evaluations show that the proposed framework achieves over 99%, 93%, and 95% testing accuracy when tested in same-domain (day, location, and channel), cross-day, and cross-location scenarios, respectively. Our findings demonstrate the superiority of the proposed framework in enhancing the accuracy, robustness, and adaptability of deep learning-based methods, thus offering a pioneering solution for enabling ZT IoT device identification.

Related papers

• Communication Traffic Characteristics Reveal an IoT Devices Identity [0.0]
  This paper proposes a machine learning-based device fingerprinting (DFP) model for identifying network-connected IoT devices. Experimental results have shown that the proposed DFP method achieves over 98% in classifying individual IoT devices.
  arXiv  Detail & Related papers  (2024-02-25T18:58:09Z)
• SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic [49.549771439609046]
  We propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication. Extensive experimental results show the effectiveness and efficiency of SISSA.
  arXiv  Detail & Related papers  (2024-02-21T03:31:40Z)
• zk-IoT: Securing the Internet of Things with Zero-Knowledge Proofs on Blockchain Platforms [0.0]
  This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems. Our framework ensures the integrity of firmware execution and data processing in potentially compromised IoT devices.
  arXiv  Detail & Related papers  (2024-02-13T09:34:23Z)
• 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)
• AFR-Net: Attention-Driven Fingerprint Recognition Network [47.87570819350573]
  We improve initial studies on the use of vision transformers (ViT) for biometric recognition, including fingerprint recognition. We propose a realignment strategy using local embeddings extracted from intermediate feature maps within the networks to refine the global embeddings in low certainty situations. This strategy can be applied as a wrapper to any existing deep learning network (including attention-based, CNN-based, or both) to boost its performance.
  arXiv  Detail & Related papers  (2022-11-25T05:10:39Z)
• 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)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning [30.536369182792516]
  Device-type identification combined with data from vulnerability databases can pinpoint vulnerable IoT devices in a network. We present and evaluate two deep learning approaches to the reliable IoT device-type identification.
  arXiv  Detail & Related papers  (2021-02-16T14:49:34Z)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)
• Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices [20.449229983283736]
  We propose an enhanced deep learning framework for IoT device identification using physical layer signals. We have evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation.
  arXiv  Detail & Related papers  (2020-08-27T20:50:48Z)
• Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds [53.07042574352251]
  We design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework.
  arXiv  Detail & Related papers  (2020-08-12T12:03:27Z)

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.