An efficient approach for device identification and traffic classification in IoT ecosystems

Santos, Matias R. P. dos; Andrade, Rossana M. C.; Gomes, Danielo G.; Callado, Arthur

doi:10.1109/iscc.2018.8538630

Cited by 36 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are plenty of studies on IoT device type classification or fingerprinting in the literature [14], [46]- [54]. Those studies concentrate on identifying IoT devices type for different reasons including security, access control, provisioning, resource allocation, and management [47]. Actually, most of those studies concentrate on security in response to recent incidents [55], [56].…”

Section: A Use Case: Iot Device Type Classificationmentioning

confidence: 99%

Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning

Mohammed

Tabatabai

Al-Fuqaha

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

Machine Learning (ML), and Deep Learning (DL) in particular, play a vital role in providing smart services to the industry. These techniques however suffer from privacy and security concerns since data is collected from clients and then stored and processed at a central location. Federated Learning (FL), an architecture in which model parameters are exchanged instead of client data, has been proposed as a solution to these concerns. Nevertheless, FL trains a global model by communicating with clients over communication rounds, which introduces more traffic on the network and increases the convergence time to the target accuracy. In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process. Next, we propose an online stateful FL heuristic to find the best candidate clients. Additionally, we propose an IoT client alarm application that utilizes the proposed heuristic in training a stateful FL global model based on IoT device type classification to alert clients about unauthorized IoT devices in their environment. To test the efficiency of the proposed online heuristic, we conduct several experiments using a real dataset and compare the results against state-of-the-art algorithms. Our results indicate that the proposed heuristic outperforms the online random algorithm with up to 27% gain in accuracy. Additionally, the performance of the proposed online heuristic is comparable to the performance of the best offline algorithm.

show abstract

Section: A Use Case: Iot Device Type Classificationmentioning

confidence: 99%

Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning

Mohammed

Tabatabai

Al-Fuqaha

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Conventionally attributes, such as number of packets in a flow, frequency of flows, inter arrival time between packets within a flow, size of the packet, etc., are used as unique identifiers for each device type. Both supervised [6,7,9,10], unsupervised [11,12], and deep learning [13,14] methods have been used for classification.…”

Section: Classification Using Flow Characteristicsmentioning

confidence: 99%

IoTHunter: IoT network traffic classification using device specific keywords

2021

View full text Add to dashboard Cite

With the proliferation of IoT devices, network management and security monitoring are becoming a challenge. For the timely detection of IoT device status and their behaviour, traffic classification methods are used. Herein, IoTHunter, a Deep Packet Inspection based IoT traffic classifier, is described. It extracts unique keywords comprising domain names, device names etc. to identify flows belonging to a particular device. IoTHunter automates the keyword extraction using the frequency of occurrence of words belonging to flows of different devices. To further enhance the performance, IoTHunter combines device specific keywords with MAC address of device for subsequent flow labelling. A publicly available IoT dataset is experimented and a good classification accuracy of it over a range of IoT devices is demonstrated. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…In particular, they refer to synchronized data, SQLite databases and cache files containing connection information to IoT platforms. There is also work on understanding event logs [15], [16] and analyzing local network flow [17]- [19], particularly in the context of intrusion detection [20], [21].…”

Section: A Forensic Artefacts From Iot Productsmentioning

confidence: 99%

“…The victim is lying on the bed in room 2. She has an Apple Watch series 3 (18) on her right arm and an iPhone SE (19) in her pocket. Hidden in the bed, there is a sleep sensor named Terraillon Dot (20).…”

Section: A Presentation Of the Crime Scenementioning

confidence: 99%