MeDI: Measurement-based Device Identification Framework for Internet of Things

Yousefnezhad, Narges; Madhikermi, Manik; Främling, Kary

doi:10.1109/indin.2018.8472080

Cited by 13 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By collecting traffic traces over 3 weeks, the authors indicated that their classification method can not only distinguish IoT from non-IoT traffic, but also identify specific IoT devices with over 95% accuracy. Yousefnezhad et al [211] developed Measurement-based Device Identification (MeDI), a framework based on device behavior or device profile. It monitors the data packets coming from smart devices to protect the server from receiving and spreading false data.…”

Section: E Authenticationmentioning

confidence: 99%

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

et al. 2021

View full text Add to dashboard Cite

Novel intelligent systems to assist the energy transition and improve sustainability and people's life can be deployed at different scales, ranging from a house to an entire region. University campuses are an interesting intermediate size (big enough to matter and small enough to be tractable) for research, development, test and training on the integration of smartness at all levels, which led to the emergence of the concept of "smart campus" over the last few years. This review article proposes an extensive analysis of the scientific literature on smart campuses from the last decade (2010-2020). The 182 selected publications are distributed into seven categories of smartness: smart building, smart environment, smart mobility, smart living, smart people, smart governance and smart data. The main open questions and challenges regarding smart campuses are presented at the end of the review and deal with sustainability and energy transition, acceptability and ethics, learning models, open data policies and interoperability. The present work was carried out within the framework of the Energy Network of the Regional Leaders Summit (RLS-Energy) as part of its multilateral research efforts on smart regions.

show abstract

Section: E Authenticationmentioning

confidence: 99%

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, we adopt the payload data for device identification alongside header information and generated statistical features. The idea of using payload data or sensor measurements for device identification is initially proposed by [ 8 ]. This identification framework employs only payload data and some extracted statistical features from a public dataset without taking into consideration any attack scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For proof-of-concept, we implement a demonstrator system for the IoT system comprising of temperature and humidity sensors integrated through open standards called Open Messaging Interface (O-MI) and Open Data Format (O-DF). The present paper is an extended version of our paper, presented at the INDIN conference [ 8 ]. This paper significantly expands the feature sets and develops realistic results by running a real use-case, collecting real data, and evaluating the system through various attack scenarios.…”

Section: Introductionmentioning

confidence: 99%

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

Yousefnezhad

Malhi

Främling

2021

Sensors

Self Cite

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

“…After identifying the type of the device, the system evaluates the vulnerability of the device, and isolates the device from the network if there is a security threat. In order to prevent illegal devices from stealing legal identity to attack the network and other devices, Yousefnezhad, Narges et al [26] proposed a device identification framework (MeDI) based on device behavior analysis. It identifies the security of the device by monitoring the data packet sent by the device and protect the server from accepting and spreading false data.…”

Section: Introductionmentioning

confidence: 99%

IIS: Intelligent Identification Scheme of Massive IoT Devices

Liu

Sun

et al. 2021

2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

View full text Add to dashboard Cite

Device identification is of great importance in system management and network security. Especially, it is the priority in industrial internet of things (IIoT) scenario. Since there are massive devices producing various kinds of information in manufacturing process, the robustness, reliability, security and real-time control of the whole system is based on the identification of the massive IIoT devices. Previous IIoT device identification solutions are mostly based on a centralized architecture, which brings a lot of problems in scalability and security. In addition, most traditional identification systems can only identify inherent types of devices which is not suitable for the adaptive device management in IIoT. In order to solve these problems, this paper proposes a Intelligent Identification Scheme(IIS) of Massive IoT Devices, a completely distributed intelligent identification scheme of massive IIoT devices. The scheme changes the traditional centralized architecture and realizes more efficient clustering identification of massive IIoT devices. Moreover, IIS can identify more and more types of devices intelligently with the continuous learning ability since the identification model is constantly updated according to the ledger which is maintained by all gateways collaboratively. We also conduct experiments to evaluate the performance of IIS based on the data obtained from real IIoT devices, which proves that IIS is efficient in device identification and intelligent for the adaptive device management in IIoT.

show abstract

MeDI: Measurement-based Device Identification Framework for Internet of Things

Cited by 13 publications

References 23 publications

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

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

IIS: Intelligent Identification Scheme of Massive IoT Devices

Contact Info

Product

Resources

About