Network-Protocol-Based IoT Device Identification

Ammar, Nesrine; Noirie, Ludovic; Tixeuil, Sébastien

doi:10.1109/fmec.2019.8795318

Cited by 22 publications

(21 citation statements)

References 14 publications

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“…However, previous studies have taken a different approach to building a testbed. In [18,43,44], only one manufacturer per device was used; in [45], three different manufacturers for the camera were used; in [21], three different manufacturers for the plug were used. Besides, some testbeds [44,45] have additional non-IoT devices, such as Raspberry Pi sensors [21].…”

Section: Smart-home Testbedmentioning

confidence: 99%

“…In [18,43,44], only one manufacturer per device was used; in [45], three different manufacturers for the camera were used; in [21], three different manufacturers for the plug were used. Besides, some testbeds [44,45] have additional non-IoT devices, such as Raspberry Pi sensors [21]. These studies all used smart-home testbeds and machine learning to identify the classes of IoT devices (e.g., hubs, cameras, and plugs).…”

Section: Smart-home Testbedmentioning

confidence: 99%

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IoT Traffic: Modeling and Measurement Experiments

Nguyen-An

Silverston

Yamazaki

et al. 2021

IoT

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We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

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Section: Smart-home Testbedmentioning

confidence: 99%

Section: Smart-home Testbedmentioning

confidence: 99%

IoT Traffic: Modeling and Measurement Experiments

Nguyen-An

Silverston

Yamazaki

et al. 2021

IoT

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“…Miettinen et al worked on IoT device fingerprinting and identification that utilized the network traffic during the setup phase [12]. Ammar et al presented a different machinelearning based approach to identifying IoT devices [13]. Their work used service discovery and DHCP protocols to collect information about the IoT devices during their setup phases.…”

Section: Related Workmentioning

confidence: 99%

Network Traffic Characteristics of IoT Devices in Smart Homes

Mainuddin,

Duan,

Dong

2021

Preprint

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Understanding network traffic characteristics of IoT devices plays a critical role in improving both the performance and security of IoT devices, including IoT device identification, classification, and anomaly detection. Although a number of existing research efforts have developed machine-learning based algorithms to help address the challenges in improving the security of IoT devices, none of them have provided detailed studies on the network traffic characteristics of IoT devices. In this paper we collect and analyze the network traffic generated in a typical smart homes environment consisting of a set of common IoT (and non-IoT) devices. We analyze the network traffic characteristics of IoT devices from three complementary aspects: remote network servers and port numbers that IoT devices connect to, flow-level traffic characteristics such as flow duration, and packet-level traffic characteristics such as packet inter-arrival time. Our study provides critical insights into the operational and behavioral characteristics of IoT devices, which can help develop more effective security and performance algorithms for IoT devices.

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“…IHS-Markit [7] has predicted that IoT devices connected to the internet will reach approximately 125 billion by the year 2030. Proliferated growth of IoT and non-IoT devices has imposed new challenges; on network administrators and operators, to securely manage and control network services [8], [9]. Though essential for communication, using traditional explicit identifiers, such as internet protocol (IP) and media access control (MAC) addresses, to determine identities of devices that are connected to a network, is not secure, as these addresses are easily mutable.…”

Section: Introductionmentioning

confidence: 99%

Device identification using optimized digital footprints

Chowdhury

Idris²,

Abas³

2023

IJ-AI

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<span lang="EN-US">The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.</span>

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Network-Protocol-Based IoT Device Identification

Cited by 22 publications

References 14 publications

IoT Traffic: Modeling and Measurement Experiments

IoT Traffic: Modeling and Measurement Experiments

Network Traffic Characteristics of IoT Devices in Smart Homes

Device identification using optimized digital footprints

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