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
• 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

• Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention [53.84395207661823]
  We propose Tactile-WAM, a touch-aware WAM with a Tactile Asymmetric Attention Mechanism (TAAM)<n>On ManiFeel, Tactile-WAM improves the mean success rate by 38.9% overall and by 86% on contact-rich tasks.
  arXiv  Detail & Related papers  (2026-06-25T06:54:56Z)
• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• Things that Matter -- Identifying Interactions and IoT Device Types in Encrypted Matter Traffic [0.9176056742068813]
  We analyze the robustness of the Matter IoT standard to encrypted traffic analysis performed by a passive eavesdropper.<n>We identify patterns in metadata of the encrypted Matter traffic that allow inferring the specific interactions occurring between end devices and controllers.<n>These patterns can be used to create fingerprints that allow a passive attacker to infer the type of devices used in the network.
  arXiv  Detail & Related papers  (2026-02-02T10:34:06Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
  This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge. We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic. This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
  arXiv  Detail & Related papers  (2025-04-22T09:40:05Z)
• 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)
• Caveat (IoT) Emptor: Towards Transparency of IoT Device Presence (Full Version) [12.842258850026878]
  Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation. This paper constructs a privacy-agileuation RootofTrust architecture for devices, called PAISA. It guarantees timely and secure announcements about IoT devices' presence and their capabilities.
  arXiv  Detail & Related papers  (2023-09-07T09:08:31Z)
• 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)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• 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)
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy. We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
  arXiv  Detail & Related papers  (2020-06-10T16:05:05Z)
• 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)
• Identification of Wearable Devices with Bluetooth [8.195638774687056]
  We introduce a wearable fingerprinting technique focusing on Bluetooth classic protocol.<n>Specifically, we propose a non-intrusive wearable device identification framework which utilizes 20 different Machine Learning (ML) algorithms.<n>Our detailed accuracy results show on average 98.5%, 98.3% precision and recall for identifying wearables using the Bluetooth classic protocol.
  arXiv  Detail & Related papers  (2018-09-27T07:58:48Z)

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.