Technical Report-IoT Devices Proximity Authentication In Ad Hoc Network
Environment
- URL: http://arxiv.org/abs/2210.00175v1
- Date: Sat, 1 Oct 2022 03:07:42 GMT
- Title: Technical Report-IoT Devices Proximity Authentication In Ad Hoc Network
Environment
- Authors: Ali Abdullah S. AlQahtani, Hosam Alamleh, Baker Al Smadi
- Abstract summary: Internet of Things (IoT) is a distributed communication technology system that offers the possibility for physical devices to connect and exchange data.
authentication to the IoT devices is essential as it is the first step in preventing any negative impact of possible attackers.
This paper implements an IoT devices authentication scheme based on something that is in the IoT devices environment.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Internet of Things (IoT) is a distributed communication technology system
that offers the possibility for physical devices (e.g. vehicles home appliances
sensors actuators etc.) known as Things to connect and exchange data more
importantly without human interaction. Since IoT plays a significant role in
our daily lives we must secure the IoT environment to work effectively. Among
the various security requirements authentication to the IoT devices is
essential as it is the first step in preventing any negative impact of possible
attackers. Using the current IEEE 802.11 infrastructure this paper implements
an IoT devices authentication scheme based on something that is in the IoT
devices environment (i.e. ambient access points). Data from the broadcast
messages (i.e. beacon frame characteristics) are utilized to implement the
authentication factor that confirms proximity between two devices in an ad hoc
IoT network.
Related papers
- IoT-LM: Large Multisensory Language Models for the Internet of Things [70.74131118309967]
IoT ecosystem provides rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio.
Machine learning presents a rich opportunity to automatically process IoT data at scale.
We introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem.
arXiv Detail & Related papers (2024-07-13T08:20:37Z) - 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) - Towards Artificial General Intelligence (AGI) in the Internet of Things
(IoT): Opportunities and Challenges [55.82853124625841]
Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities.
This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things.
The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
arXiv Detail & Related papers (2023-09-14T05:43:36Z) - IoT Device Identification Based on Network Communication Analysis Using
Deep Learning [43.0717346071013]
The risk of attacks on an organization's network has increased due to the growing use of less secure IoT devices.
To tackle this threat and protect their networks, organizations generally implement security policies in which only white listed IoT devices are allowed on the network.
In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network.
arXiv Detail & Related papers (2023-03-02T13:44:58Z) - 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) - Machine Learning Methods for Device Identification Using Wireless
Fingerprinting [1.0499611180329804]
We study a large class of machine learning algorithms for device identification using wireless fingerprints.
We design, implement, deploy, collect relevant data sets, train and test a multitude of machine learning algorithms.
The proposed solution is currently being deployed in a real-world industrial IoT environment as part of H2020 project COLLABS.
arXiv Detail & Related papers (2022-11-03T16:42:41Z) - Learning, Computing, and Trustworthiness in Intelligent IoT
Environments: Performance-Energy Tradeoffs [62.91362897985057]
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications.
This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
arXiv Detail & Related papers (2021-10-04T19:41:42Z) - 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) - 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.