Rapid IoT Device Identification at the Edge

• URL: http://arxiv.org/abs/2110.13941v1
• Date: Tue, 26 Oct 2021 18:11:38 GMT
• Title: Rapid IoT Device Identification at the Edge
• Authors: Oliver Thompson, Anna Maria Mandalari, Hamed Haddadi
• Abstract summary: 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.
• Score: 5.213147236587845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consumer Internet of Things (IoT) devices are increasingly common in everyday homes, from smart speakers to security cameras. Along with their benefits come potential privacy and security threats. To limit these threats we must implement solutions to filter IoT traffic at the edge. To this end the identification of the IoT device is the first natural step. In this paper we demonstrate a novel method of rapid IoT device identification that uses neural networks trained on device DNS traffic that can be captured from a DNS server on the local network. The method identifies devices by fitting a model to the first seconds of DNS second-level-domain traffic following their first connection. Since security and privacy threat detection often operate at a device specific level, rapid identification allows these strategies to be implemented immediately. Through a total of 51,000 rigorous automated experiments, we classify 30 consumer IoT devices from 27 different manufacturers with 82% and 93% accuracy for product type and device manufacturers respectively.

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)
• Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
  This paper provides a classification of IoT malware. Major targets and used exploits for attacks are identified and referred to the specific malware. The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
  arXiv  Detail & Related papers  (2023-12-01T16:10:43Z)
• CNN based IoT Device Identification [0.0]
  We present a method that identifies devices in the Aalto dataset using the convolutional neural network (CNN) In this study, we present a method that identifies devices in the Aalto dataset using the convolutional neural network (CNN)
  arXiv  Detail & Related papers  (2023-04-27T00:37:16Z)
• 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)
• CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals [48.813942331065206]
  We propose a security hardening system for in-vehicle networks. The proposed system includes two mechanisms that process deep features extracted from voltage signals measured on the CAN bus.
  arXiv  Detail & Related papers  (2021-06-15T06:12:33Z)
• InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks [65.78061366594106]
  We propose InstantNet to automatically generate and deploy instantaneously switchable-precision networks which operate at variable bit-widths. In experiments, the proposed InstantNet consistently outperforms state-of-the-art designs.
  arXiv  Detail & Related papers  (2021-04-22T04:07:43Z)
• Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey [16.3730669259576]
  The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. We classify the IoT device identification and detection into four categories: device-specific pattern recognition, Deep Learning enabled device identification, unsupervised device identification, and abnormal device detection.
  arXiv  Detail & Related papers  (2021-01-25T15:51:04Z)
• Lightweight IoT Malware Detection Solution Using CNN Classification [2.288885651912488]
  The security aspect of IoT devices is an infant field, which is why it is our focus in this paper. We developed a system that can recognize malicious behavior of a specific IoT node on the network. Through convolutional neural network and monitoring, we were able to provide malware detection for IoT using a central node that can be installed within the network.
  arXiv  Detail & Related papers  (2020-10-13T10:56:33Z)
• Smart Home, security concerns of IoT [91.3755431537592]
  The IoT (Internet of Things) has become widely popular in the domestic environments. People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed. Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.
  arXiv  Detail & Related papers  (2020-07-06T10:36:11Z)
• 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.