Iot device fingerprint using deep learning

Author: agck

August undefined, 2024

Web4 mrt. 2024 · This study examines the problem of allocating resources for edge … Web13 dec. 2024 · Leveraging these features, we have developed a deep learning based classification model for IoT device fingerprinting. Using a real-world IoT dataset, our evaluation results demonstrate that the proposed method can achieve \({\sim }99\%\) accuracy in IoT device-type identification based on single network flow classification.

Automated IoT Device Fingerprinting Through Encrypted Stream ...

WebAbstract: Device Fingerprinting (DFP) is the identification of a device without using its … Web19 apr. 2024 · Device Authentication Codes based on RF Fingerprinting using Deep … china creates digital currency

Intrusion Detection in IoT Networks Using Deep Learning …

WebTo perform the fingerprint attack, we train machine-learning algorithms based on selected features extracted from the encrypted IoT traffic. Extensive simulations involving the baseline approach show that we achieve not only a significant mean accuracy improvement of 18.5% and but also a speedup of 18.39 times for finding the best estimators ... WebIoT devices using deep learning. The proposed method is based on RF ﬁngerprinting since physical layer based features are device speciﬁc and more difﬁcult to impersonate. RF traces are collected Web19 apr. 2024 · In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT devices with wireless interface by exploiting their radio frequency (RF) signatures. The proposed DAC is based on RF fingerprinting, information theoretic method, feature learning, and discriminatory power of deep learning. china creative wind energy

Iot device fingerprint using deep learning

Deep Learning-Based Security Behaviour Analysis in IoT

Web12 jan. 2024 · The proposed device fingerprinting model demonstrates over 99% and 95% precisions in distinguishing between known and unknown traffic traces and in identifying IoT and non-IoT traffic traces, respectively. 98.49% precision has also been demonstrated on an individual device classification task. Web3 nov. 2024 · Data-based RF fingerprint identification uses deep learning algorithms, which can automatically train the raw data of the signal to identify mobile devices. Before 2024, the research of radio frequency fingerprint identification mainly focused on the use of machine learning algorithms, e.g., the support vector machines (SVM) algorithms are …

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Web1 okt. 2024 · Deep learning is a promising way to acquire various IoT devices' … Web1 jan. 2024 · Device fingerprinting is a problem of identifying a network device using network traffic data to secure against cyber-attacks. Automated device classification from a large set of network...

Web10 jan. 2024 · Index Terms—IoT Testbed, RF Dataset Collection and Release, RF Fingerprinting, Deep Learning, LoRa Protocol. I. INTRODUCTION This paper presents and releases a comprehensive dataset consisting of massive RF signal data captured from 25 LoRa-enabled transmitters using Ettus USRP B210 receivers. The RF Web1 nov. 2024 · Device Fingerprinting (DFP) is the identification of a device without using …

Web3 nov. 2024 · IoT Device Fingerprint using Deep Learning. Abstract: Device … Web18 apr. 2024 · In this paper, we propose Device Authentication Code (DAC), a novel …

Web1 okt. 2024 · Radio Frequency (RF) fingerprinting as a physical layer authentication method could be used to distinguish legitimate wireless devices from adversarial ones. In this paper, we present a wireless device identification platform to improve Internet of things (IoT) security using deep learning techniques.

Web1 apr. 2024 · The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning … grafton hill worcesterWeb13 jun. 2024 · In this study, a novel intrusion detection method is proposed to detect … china creative makeup mirror china created islandsWeb25 jan. 2024 · Ferdowsi and Saad proposed a deep learning method based on the long short-term memory (LSTM), which uses the fingerprints of the signal generated by an IoT mobile device. In addition, LSTM algorithm is used to allow an IoT mobile device updating the bit stream by considering the sequence of generated data. china creative wooden medalsWeb26 apr. 2024 · The results of the study are expected to be used in a network-based intrusion detection system (NIDS) to conduct anomaly detection on an IoT network. This article is organized as follows. Section 2 introduces the security and deep-learning method. A machine-learning application in IoT security is presented in Section 3. grafton hillclimbWeb18 jan. 2024 · Device Fingerprinting (DFP) is the identification of a device without … grafton high school yorktown virginiaWeb26 apr. 2024 · One proposed way to improve IoT security is to use machine learning. … china creative outdoor media