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On the Analysis of Semantic Denial-of-Service Attacks Affecting Smart Living Devices

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Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1229))

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Abstract

With the interconnectedness of heterogeneous IoT devices being deployed in smart living spaces, it is imperative to assure that connected devices are resilient against Denial-of-Service (DoS) attacks. DoS attacks may cause economic damage but may also jeopardize the life of individuals, e.g., in a smart home healthcare environment since there might be situations (e.g., heart attacks), when urgent and timely actions are crucial. To achieve a better understanding of the DoS attack scenario in the ever so private home environment, we conduct a vulnerability assessment of five commercial-off-the-shelf IoT devices: a gaming console, media player, lighting system, connected TV, and IP camera, that are typically found in a smart living space. This study was conducted using an automated vulnerability scanner – Open Vulnerability Assessment System (OpenVAS) – and focuses on semantic DoS attacks. The results of the conducted experiment indicate that the majority of the tested devices are prone to DoS attacks, in particular those caused by a failure to manage exceptional conditions, leading to a total compromise of their availability. To understand the root causes for successful attacks, we analyze the payload code, identify the weaknesses exploited, and propose some mitigations that can be adopted by smart living developers and consumers.

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Notes

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Acknowledgments

This work has been carried out within the research profile “Internet of Things and People,” funded by the Knowledge Foundation and Malmö University in collaboration with 10 industrial partners.

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Correspondence to Joseph Bugeja .

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Bugeja, J., Jacobsson, A., Spalazzese, R. (2020). On the Analysis of Semantic Denial-of-Service Attacks Affecting Smart Living Devices. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_32

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