Called function identification of IoT devices by network traffic analysis

Koike, Daichi; Ishida, Shigemi; Arakawa, Yutaka

doi:10.1145/3412841.3441951

Cited by 2 publications

(8 citation statements)

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“…Koike et al proposed a method for identifying the called functions of IoT devices using the characteristic time variability of their generated traffic to visualize their operating status [7]. This method collects traffic data using specific functions on a smart speaker.…”

Section: Iot Device Identification Methodsmentioning

confidence: 99%

“…This method can automatically monitor the operation status of IoT devices, but the identification target is limited to one smart speaker. Koike et al proposed an improved version of this method [8]. This version uses two categories of features extracted from traffic data of functions called on three smart speakers: Amazon Echo Spot, Amazon Echo Dot, and Amazon Echo Flex.…”

Section: Iot Device Identification Methodsmentioning

confidence: 99%

“…In the first category, the features extracted from each individual packet, such as the transport protocol, port number, and source and destination addresses, were used. In the second category, the features of each window consisting of 30 consecutive packets were used, as was done in [7]. However, they were extended to the average, variance, standard deviation, maximum, minimum, and difference between the maximum and minimum packet sizes.…”

Section: Iot Device Identification Methodsmentioning

confidence: 99%

“…Moreover, a device identification method for the three types of Amazon Echo was implemented with an accuracy of 99.1%. This method can automatically and more precisely monitor how IoT devices operate than the previous method [7]. Nevertheless, the target is still limited to smart speakers only.…”

Section: Iot Device Identification Methodsmentioning

confidence: 99%

“…For this purpose, it is important to automatically identify connected IoT devices in some way. Several methods for identifying IoT devices or their behavior based on traffic analysis using machine learning have been proposed [6][7][8][9][10][11][12][13][14]. Although these methods automatically identify IoT devices and their behavior from traffic data, they do not consider how they work in smart home environments.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Internet-of-Things Traffic Analysis and Device Identification Based on Two-Stage Clustering in Smart Home Environments

Asano,

Miyoshi,

Yamazaki

2023

Future Internet

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Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices and challenges in replacing or the updating their firmware, adequate security measures have not been implemented. Instead, IoT device identification methods based on traffic analysis have been proposed. Since conventional methods process and analyze traffic data simultaneously, bias in the occurrence rate of traffic patterns has a negative impact on the analysis results. Therefore, this paper proposes an IoT traffic analysis and device identification method based on two-stage clustering in smart home environments. In the first step, traffic patterns are extracted by clustering IoT traffic at a local gateway located in each smart home and subsequently sent to a cloud server. In the second step, the cloud server extracts common traffic units to represent IoT traffic by clustering the patterns obtained in the first step. Two-stage clustering can reduce the impact of data bias, because each cluster extracted in the first clustering is summarized as one value and used as a single data point in the second clustering, regardless of the occurrence rate of traffic patterns. Through the proposed two-stage clustering method, IoT traffic is transformed into time series vector data that consist of common unit patterns and can be identified based on time series representations. Experiments using public IoT traffic datasets indicated that the proposed method could identify 21 IoTs devices with an accuracy of 86.9%. Therefore, we can conclude that traffic analysis using two-stage clustering is effective for improving the clustering quality, device identification, and implementation in distributed environments.

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Section: Iot Device Identification Methodsmentioning

confidence: 99%

Section: Iot Device Identification Methodsmentioning

confidence: 99%

Section: Iot Device Identification Methodsmentioning

confidence: 99%

Section: Iot Device Identification Methodsmentioning

confidence: 99%