Autonomous Identification of IoT Device Types based on a Supervised Classification

Ammar, Nesrine; Noirie, Ludovic; Tixeuil, Sébastien

doi:10.1109/icc40277.2020.9148821

Cited by 12 publications

(11 citation statements)

References 14 publications

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“…The author [23] uses CNN and RNN models in a profound learning combination to identify flow forms, such as HTTP, SMTP, Telnet, QUIC, Office365, and YouTube, with six features, specifically source/destination port number, payload length, TCP window size, interarrival time and directions from the first 20 flow packets. Another method employs autonomous IoT system classification using a combination of textual and flow features used for classification [24,25]. From the perspective of network security, ML methods are used to classify IoT devices with the aim of determining if a system is on a whitelist of devices that are authorized to link to the network.…”

Section: Related Workmentioning

confidence: 99%

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

et al. 2021

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Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.

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Section: Related Workmentioning

confidence: 99%

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

et al. 2021

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“…Device identification is a multiclass classification problem [75]. As the process involves the identification of a different class of devices and their instances.…”

Section: B Device Classification Using Machine Learning (Ml)mentioning

confidence: 99%

“…Also, the study proposes a security model to enforce the policies for the IoT device in the network, detect the malicious device, and restricts their communication in the network. Random forest [75], [79] used for the classification of the device that is further compared to other machine learning algorithms. The random forest is compared to some of the deep learning algorithms, still, it comes out to be the better choice.…”

Section: ) DI and Supervised Mlmentioning

confidence: 99%

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Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

et al. 2021

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This paper presents an overview of device identification techniques and the Manufacturer Usage Description (MUD) standard used for the Internet of things to reduce the IoT attack surface. The ongoing diversity and the sheer increase in the number of connected IoT devices have crumpled security efforts. There is a need to reconsider and redesign the underlying concept of developing security systems to resolve IoT security challenges. In this backdrop, device profiling and identification have emerged as an exciting technique that helps to reduce IoT device attack surface. One of the known approaches for device identification is to fingerprint a device. There are many ways to fingerprint the device, mostly using device network flows or device local attributes. The device identification ensures the authenticity of the device attached to the network, like user authentication. Since IoT devices mostly work using machine-to-machine (M2M) communication, this requires identifying each device properly. But there is no unified approach for device identification for the ever-growing world of IoT devices and applications. One of the major steps forward in this direction is the development of the Manufacturer Usage Description (MUD) standard that defines the role of a device within the network. It limits the device to execute the primary task only, which will help to reduce the attack surface. Since the inception of MUD, many security frameworks use this standard for IoT security. However, there is a need to scrutinize the security frameworks based on the MUD, to find out the claimed effectiveness of the standard in IoT security. This paper initially identifies and classifies the potential vulnerabilities in IoT devices. Then, the study provides an overview of the research that focuses on device identification techniques and analyzes their role in IoT security. Finally, the research presents an overview of MUD technology, its implementation scenarios, the limitation of the latest MUD standard, and its applications in the industry. The prime aim of this work is to examine the MUD benefits in IoT security along with the weaknesses and challenges while implementing this standard along with future directions. INDEX TERMSManufacturer usage description (MUD), Internet of Things (IoT), device identification (DI), software defined network (SDN), machine learning (ML), deep learning (DL). NOMAN MAZHAR received the B.E. degree in software engineering and the M.S. degree in information technology from the

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“…The run-time measurements and power measurements on the IoT system show 3 to save energy. Ammar, N., et al [42] approached to near-real time grading based on network characteristics derived both from traffic flow characteristics and from packets' payloads in order to differentiate system types. A newly connected computer to a home network is automatically detected by our solution.…”

Section: Related Workmentioning

confidence: 99%

Iot Device Classification in Cloud Organizations Using Deep Belief Networks

M²,

et al. 2021

Preprint

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With the rapid increase in the usage of IoT devices, the cyber threats are increasing among the communication between the IoT devices. The challenges related to security surmounts with increasing number of IoT devices due to its functionality and heterogeneity. In recent times, deep learning algorithms are offered to resolve the constraints associated with detection of malicious devices among the networks. In this paper, we utilize deep belief network (DBN) to resolve the problems associated with identification, detection of anomaly IoT devices. Several features are extracted initially to find the malicious devices in the IoT device network that includes storage, computational resources and high dimensional features. These features extracted from the network traffic assists in achieving the classification of devices by DBN. The simulation is performed to test the accuracy and detection rate of the proposed deep learning classifier. The results show that the proposed method is effective in implementing the detection of malicious nodes in the network than existing methods.

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Autonomous Identification of IoT Device Types based on a Supervised Classification

Cited by 12 publications

References 14 publications

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

Iot Device Classification in Cloud Organizations Using Deep Belief Networks

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