A Proxy Identifier Based on Patterns in Traffic Flows

Aghaei-Foroushani, Vahid; Zincir-Heywood, A. Nur

doi:10.1109/hase.2015.26

Cited by 17 publications

(5 citation statements)

References 11 publications

(14 reference statements)

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“…The research observes the traffic characteristics for individual SHIoT devices covered by the research (41 devices) at the traffic flow level. The traffic flow is defined by a sequence of packets with equal values of source IP address, destination IP address, source communication port, destination communication port and the protocol used, TCP (Transmission Control Protocol) or UDP (User Datagram Protocol) [35]. The reason for choosing the traffic flow as the level of observation and analysis of traffic characteristics is that it represents the aggregated (statistical) data of the packet header for communication between the source and the destination.…”

Section: Extraction Of Identified Traffic Featuresmentioning

confidence: 99%

Ensemble machine learning approach for classification of IoT devices in smart home

Cvitić

Peraković

Periša

et al. 2021

Int. J. Mach. Learn. & Cyber.

166

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The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

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Section: Extraction Of Identified Traffic Featuresmentioning

confidence: 99%

Ensemble machine learning approach for classification of IoT devices in smart home

Cvitić

Peraković

Periša

et al. 2021

Int. J. Mach. Learn. & Cyber.

166

View full text Add to dashboard Cite

show abstract

“…Application classification using machine learning has long been researched. The most frequently utilized features were flow features, such as flow duration and flow bytes per second, packet features, such as packet size and inter-packet duration, or combinations of flow and packet features [ 16 , 17 , 18 , 19 , 20 ]. Recently, deep learning methodologies have been used for application classification [ 14 , 15 , 21 , 22 , 23 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Lightweight Transformer Model for Mobile Application Classification

Gwak,

Cha,

Yoon

et al. 2024

Sensors

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Recently, realistic services like virtual reality and augmented reality have gained popularity. These realistic services require deterministic transmission with end-to-end low latency and high reliability for practical applications. However, for these real-time services to be deterministic, the network core should provide the requisite level of network. To deliver differentiated services to each real-time service, network service providers can classify applications based on traffic. However, due to the presence of personal information in headers, application classification based on encrypted application data is necessary. Initially, we collected application traffic from four well-known applications and preprocessed this data to extract encrypted application data and convert it into model input. We proposed a lightweight transformer model consisting of an encoder, a global average pooling layer, and a dense layer to categorize applications based on the encrypted payload in a packet. To enhance the performance of the proposed model, we determined hyperparameters using several performance evaluations. We evaluated performance with 1D-CNN and ET-BERT. The proposed transformer model demonstrated good performance in the performance evaluation, with a classification accuracy and F1 score of 96% and 95%, respectively. The time complexity of the proposed transformer model was higher than that of 1D-CNN but performed better in application classification. The proposed transformer model had lower time complexity and higher classification performance than ET-BERT.

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“…Defining the class of SHIoT devices in this research is based on the statistical characteristics of a particular device's traffic flows. The traffic flow is defined by a packet with equal source of source IP address, destination IP address, source communication port, destination communication port, and protocol used (TCP or UDP) (Aghaei-Foroushani & Zincir-Heywood, 2015). The reason for choosing a traffic flow as the level of observation and analysis of the feature is that it represents the packet header's aggregated (statistical) data for communication between the source and the destination.…”

Section: Extraction Of Traffic Flows Of Iot Devicesmentioning

confidence: 99%

Novel Classification of IoT Devices Based on Traffic Flow Features

Cvitić

Peraković

Periša

et al. 2021

Journal of Organizational and End User Computing

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The concept of IoT (Internet of Things) assumes a continuous increase in the number of devices, which raises the problem of classifying them for different purposes. Based on their semantic characteristics, meaning, functionality or domain of usage, the system classes have been identified so far. This research purpose is to identify devices classes based on traffic flow characteristics such as the coefficient of variation of the received and sent data ratio. Such specified classes can combine devices based on behavior predictability and can serve as the basis for the creation of network management or network anomaly detection classification models. Four generic classes of IoT devices where defined by using the classification of the coefficient of variation method.

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A Proxy Identifier Based on Patterns in Traffic Flows

Cited by 17 publications

References 11 publications

Ensemble machine learning approach for classification of IoT devices in smart home

Ensemble machine learning approach for classification of IoT devices in smart home

Lightweight Transformer Model for Mobile Application Classification

Novel Classification of IoT Devices Based on Traffic Flow Features

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