Automatic Device Classification from Network Traffic Streams of Internet of Things

Bai, Lei; Yao, Lina; Kanhere, Salil S.; Wang, Xianzhi; Yang, Zheng

doi:10.1109/lcn.2018.8638232

Cited by 89 publications

(57 citation statements)

References 19 publications

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“…en, they use the CNN to learn features from device graphs and distinguish different devices. Another study in [19] attempts to automatically identify the semantic type of a device by analysing its network traffic. First, they define a collection of discriminating features from raw traffic flows, and those features are used to characterise the attributes of devices.…”

Section: Identify the Uniqueness Of Iot Devices Using DLmentioning

confidence: 99%

“…e graphs are then treated as images, and all images are converted to a size of 150 × 150, where they are then given as input to a neural network to identify device behaviour pattern. Research from [19] considers network traffic from devices as sequences of packets. ey first split traffic into subflows with fixed time interval T. For each subflow, features related to the number of packets, packet length statistics, and protocol-related features are extracted.…”

Section: Network Behaviour Modelling With Deep Learningmentioning

confidence: 99%

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Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

Yue

Legg

et al. 2021

Security and Communication Networks

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Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.

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Section: Identify the Uniqueness Of Iot Devices Using DLmentioning

confidence: 99%

Section: Network Behaviour Modelling With Deep Learningmentioning

confidence: 99%

Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

Yue

Legg

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Recently [23] proposed a cascade model to automatically identify the semantic type of devices using neural networks. It classifies devices in four classes with less than 75% accuracy.…”

Section: Fig 2: Iot Applicationsmentioning

confidence: 99%

Clustering of IoT Devices Using Device Profiling and Behavioral Analysis to Build Efficient Network Policies

Hamza

Geelani

Nawaz

et al. 2021

Mehran Univ. res. j. eng. technol.

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The Internet of Things (IoT) has emerged as a new paradigm, and billions of devices are connected with the internet. IoT is being penetrated in major domains of daily life like health care, agriculture, industry, smart homes and monitoring of the environment. The operator of such complex, huge and diverse heterogeneous networks may not even be fully aware of their IoT devices working, activity, behavior and resource utilization etc. The efficient management of IoT devices becomes a challenge for network managers to ensure smooth network operation. Network traffic analysis of IoT devices is a necessary and rudimentary tool to understand the behavior of devices. In this paper firstly, we identify insights of device network traffic, discuss the activity patterns of some IoT devices and present a visual description of the pattern of IoT devices. Secondly, after analyzing the device's behavior, we build and demonstrate a profile of each device based on its activity cycle and traffic patterns information. Thirdly, the K-Means clustering algorithm is used to make clusters of IoT devices using their profile information. The clustering algorithm groups similar devices in a single group. The obtained results clearly describe the patterns of devices which help the network managers to make appropriate network policies for efficient secure network management.

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“…Also, the fingerprinting tools needs to have intimate knowledge of the object's neighborhood in order for this scheme to be effective. Bai, Yao, Kanhere, Wang, and Yang (2018) use long-short-term-memory-convolutional neural network cascade model to classify IoT devices based on their observed network behavior. First, they collect network data of devices that may correspond to various network configuration data like DNS queries, user activity data like Google Home commands, and background device-to-server data.…”

Section: Behavioral Fingerprinting Of Iot Devicesmentioning

confidence: 99%

Behavioral fingerprinting of Internet‐of‐Things devices

Bezawada

Ray

2019

WIREs Data Min & Knowl

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Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain. This article is categorized under: Application Areas > Science and Technology Application Areas > Internet Technologies > Machine Learning Application Areas > Industry Specific Applications

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Automatic Device Classification from Network Traffic Streams of Internet of Things

Cited by 89 publications

References 19 publications

Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

Clustering of IoT Devices Using Device Profiling and Behavioral Analysis to Build Efficient Network Policies

Behavioral fingerprinting of Internet‐of‐Things devices

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