AuDI: Toward Autonomous IoT Device-Type Identification Using Periodic Communication

Marchal, Samuel; Miettinen, Markus; Nguyen, Thien Duc; Sadeghi, Ahmad‐Reza; Asokan, N.

doi:10.1109/jsac.2019.2904364

Cited by 173 publications

(98 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar ML approach based on sent and received packet streams was proposed to recognize connected IoT device types in an experimental smart home network and the designed model helps describe IoT device network behaviors [ 26 ]. Another system named AuDI (Autonomous Device Identification) [ 27 ] was proposed by analyzing the device-related network communications to identify the device type in an IoT network traffic. An unsupervised learning algorithm was used for modeling periodic IoT devices’ communication traffic for identification.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic

Yousefnezhad

Malhi

Främling

2021

Sensors

View full text Add to dashboard Cite

In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic

Yousefnezhad

Malhi

Främling

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In this context unsupervised machine learning can be useful for the A model that works autonomously to detect the device identity by analyzing the network traffic. In this context, a system autonomous device identification (AuDI) [84] has been proposed. The system uses passive fingerprinting using the network traffic and does not require labeled training data nor prior knowledge of the device.…”

Section: ) DI and Unsupervised Mlmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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

show abstract

“…Unexpected behavior can more generally be classified as anomaly detection, a topic of significant prior work. Prior work focuses on intrusion detection systems that detect attacks by using device search engines [40], vulnerability repositories [46], and machine learning to identify known and unknown devices [20,22,29,33,42,45]. Alternatively, related work focuses on a policy enforcement approach to detecting anomalies.…”

Section: Traffic Characterizationmentioning

confidence: 99%

Information Exposure From Consumer IoT Devices

Ren

Dubois

Choffnes

et al. 2019

Proceedings of the Internet Measurement Conference

180

View full text Add to dashboard Cite

Internet of Things (IoT) devices are increasingly found in everyday homes, providing useful functionality for devices such as TVs, smart speakers, and video doorbells. Along with their benefits come potential privacy risks, since these devices can communicate information about their users to other parties over the Internet. However, understanding these risks in depth and at scale is difficult due to heterogeneity in devices' user interfaces, protocols, and functionality.In this work, we conduct a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK. Through a total of 34,586 rigorous automated and manual controlled experiments, we characterize information exposure in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, what are the IoT-device interactions that can be inferred from such content, and whether there are unexpected exposures of private and/or sensitive information (e.g., video surreptitiously transmitted by a recording device). We highlight regional differences between these results, potentially due to different privacy regulations in the US and UK. Last, we compare our controlled experiments with data gathered from an in situ user study comprising 36 participants.

show abstract

AuDI: Toward Autonomous IoT Device-Type Identification Using Periodic Communication

Cited by 173 publications

References 21 publications

Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic

Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic

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

Information Exposure From Consumer IoT Devices

Contact Info

Product

Resources

About