Function-level Access Control System for Home IoT Devices

Hattori, Yoshiyuki; Arakawa, Yutaka; Koike, Daichi; Ishida, Shigemi; Inoue, Shunsuke

doi:10.18494/sam3901

Cited by 1 publication

(6 citation statements)

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“…Hence, as also shown in Figures 11 and 12, we can conclude that two-stage clustering can more appropriately extract features that are important to describe IoT traffic, and the time series representations based on two-stage clustering can express not only whether but also whatever an IoT device communicates. Table 4 shows a comparison of the identification performance outcomes between the proposed method and the conventional methods [6][7][8][9][10][11][12][13][14]. Although it is difficult to strictly compare among them due to the lack of detailed data, the identification accuracy of the proposed method is higher than some of the conventional methods [6][7][8] but slightly lower than the others [9][10][11][12][13][14].…”

Section: Resultsmentioning

confidence: 99%

“…Table 4 shows a comparison of the identification performance outcomes between the proposed method and the conventional methods [6][7][8][9][10][11][12][13][14]. Although it is difficult to strictly compare among them due to the lack of detailed data, the identification accuracy of the proposed method is higher than some of the conventional methods [6][7][8] but slightly lower than the others [9][10][11][12][13][14]. This could be because some traffic features were lost due to feature extraction by clustering.…”

Section: Resultsmentioning

confidence: 99%

“…We will try to improve the identification performance in the future. [9] Centralized 0.910 ---Ammer et al [10] Centralized 0.980 ---Nguyen-An et al [11] Centralized 0.940 ---Okui et al [12] Centralized --0.985 -Trad et al [13,14] Centralized ---0.858…”

Section: Resultsmentioning

confidence: 99%

“…Hattori et al proposed a method to estimate the execution functions on several types of IoT devices [9], which is also an extended version of the previous two methods. To examine the possibility of analyzing which functions of an IoT device have been executed, they used eight IoT devices from four categories: two smart cameras, two smart remote controllers, two smart speakers, and two smart plugs.…”

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%

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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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