T2Pair++: Secure and Usable IoT Pairing with Zero Information Loss

• URL: http://arxiv.org/abs/2409.16530v1
• Date: Wed, 25 Sep 2024 00:41:02 GMT
• Title: T2Pair++: Secure and Usable IoT Pairing with Zero Information Loss
• Authors: Chuxiong Wu, Xiaopeng Li, Lannan Luo, Qiang Zeng,
• Abstract summary: We introduce a novel technique called Universal Operation Sensing, enabling IoT devices to sense the user's physical operations without the need for inertial sensors. With this technique, users can complete the pairing process within seconds using simple actions such as pressing a button or twisting a knob. We propose an accurate pairing protocol, which does not use fuzzy commitment and incurs zero information loss.
• Score: 9.680415326163823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Secure pairing is crucial for ensuring the trustworthy deployment and operation of Internet of Things (IoT) devices. However, traditional pairing methods are often unsuitable for IoT devices due to their lack of conventional user interfaces, such as keyboards. Proximity-based pairing approaches are usable but vulnerable to exploitation by co-located malicious devices. While methods based on a user's physical operations (such as shaking) on IoT devices offer greater security, they typically rely on inertial sensors to sense the operations, which most IoT devices lack. We introduce a novel technique called Universal Operation Sensing, enabling IoT devices to sense the user's physical operations without the need for inertial sensors. With this technique, users can complete the pairing process within seconds using simple actions such as pressing a button or twisting a knob, whether they are holding a smartphone or wearing a smartwatch. Moreover, we reveal an inaccuracy issue in the fuzzy commitment protocol, which is frequently used for pairing. To address it, we propose an accurate pairing protocol, which does not use fuzzy commitment and incurs zero information loss. The comprehensive evaluation shows that it is secure, usable and efficient.

Related papers

• DB-PAISA: Discovery-Based Privacy-Agile IoT Sensing+Actuation [10.978372324294153]
  Internet of Things (IoT) devices are becoming increasingly commonplace in numerous public and semi-private settings. Currently, most such devices lack mechanisms to facilitate their discovery by casual (nearby) users who are not owners or operators. This naturally triggers privacy, security, and safety issues. In this work, we construct DB-PAISA which addresses these issues via a pull-based method, whereby devices reveal their presence and capabilities only upon explicit user request.
  arXiv  Detail & Related papers  (2024-12-16T08:57:24Z)
• Novel_Authentication_Protocols_Tailored_for_Ambient_IoT_Devices_in_3GPP_5G_Networks [11.156335507344979]
  AIoT devices offer features such as low cost, easy deployment, and maintenance-free operation. Existing standard security mechanisms are not specifically designed for AIoT devices. We propose dedicated ultra-lightweight access authentication protocols based on various technologies and algorithms.
  arXiv  Detail & Related papers  (2024-04-03T03:33:14Z)
• Is Your Kettle Smarter Than a Hacker? A Scalable Tool for Assessing Replay Attack Vulnerabilities on Consumer IoT Devices [1.5612101323427952]
  ENISA and NIST security guidelines emphasize the importance of enabling default local communication for safety and reliability. We propose a tool, named REPLIOT, able to test whether a replay attack is successful or not, without prior knowledge of the target devices. We find that 75% of the remaining devices are vulnerable to replay attacks with REPLIOT having a detection accuracy of 0.98-1.
  arXiv  Detail & Related papers  (2024-01-22T18:24:41Z)
• 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)
• Tamper-Evident Pairing [55.2480439325792]
  Tamper-Evident Pairing (TEP) is an improvement of the Push-Button configuration (PBC) standard. TEP relies on the Tamper-Evident Announcement (TEA), which guarantees that an adversary can neither tamper a transmitted message without being detected, nor hide the fact that the message has been sent. This paper provides a comprehensive overview of the TEP protocol, including all information needed to understand how it works.
  arXiv  Detail & Related papers  (2023-11-24T18:54:00Z)
• A Lightweight and Secure PUF-Based Authentication and Key-exchange Protocol for IoT Devices [0.0]
  Device Authentication and Key exchange are major challenges for the Internet of Things. PUF appears to offer a practical and economical security mechanism in place of typically sophisticated cryptosystems like PKI and IBE. We present a system in which the IoT device does not require a continuous active internet connection to communicate with the server in order to Authenticate itself.
  arXiv  Detail & Related papers  (2023-11-07T15:42:14Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• The Internet of Senses: Building on Semantic Communications and Edge Intelligence [67.75406096878321]
  The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors' We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
  arXiv  Detail & Related papers  (2022-12-21T03:37:38Z)
• Rapid IoT Device Identification at the Edge [5.213147236587845]
  We show a novel method of rapid IoT device identification using neural networks trained on device DNS traffic. The method identifies devices by fitting a model to the first seconds of DNS second-level-domain traffic following their first connection. We classify 30 consumer IoT devices from 27 different manufacturers with 82% and 93% accuracy for product type and device manufacturers respectively.
  arXiv  Detail & Related papers  (2021-10-26T18:11:38Z)
• OmniTact: A Multi-Directional High Resolution Touch Sensor [109.28703530853542]
  Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. We introduce OmniTact, a multi-directional high-resolution tactile sensor. We evaluate the capabilities of OmniTact on a challenging robotic control task.
  arXiv  Detail & Related papers  (2020-03-16T01:31:29Z)
• IoT Device Identification Using Deep Learning [43.0717346071013]
  The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization's network also increases the risk of attacks. In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network.
  arXiv  Detail & Related papers  (2020-02-25T12:24:49Z)

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.