PingPong: Packet-Level Signatures for Smart Home Device Events

Trimananda, Rahmadi; Varmarken, Janus; Markopoulou, Athina; Demsky, Brian

doi:10.48550/arxiv.1907.11797

Cited by 8 publications

(10 citation statements)

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“…1 foundation.mozilla.org/en/privacynotincluded/ IoT Inspector 2 HomeSnitch [46] Fing 3 HomeNet Profiler [13] IoT Sentinel [42] Peek-a-Boo [1] PingPong [57] IoTSense [6] HoMonit [65] X-Ray Refine [59] For 1. DG1 (Legibility) -To make the privacy-implicated activities of devices legible to their users, in the forms of both real-time and historical records of all information flows and their destinations.…”

Section: Designing the Technology Probementioning

confidence: 99%

“…Device fingerprinting in IoTSense [6] and IoTSentinel [42] was able to identify devices based on the network traffic they produced. Behaviour classifiers such as Home-Snitch [46], Peek-a-Boo [1], PingPong [57], and HoMonit [65] used machine learning to infer the reasons that devices were sending data to particular destinations at particular times.…”

Section: Overview Of Existing Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Informing the Design of Privacy-Empowering Tools for the Connected Home

Seymour

Kraemer

Binns

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Connected devices in the home represent a potentially grave new privacy threat due to their unfettered access to the most personal spaces in people's lives. Prior work has shown that despite concerns about such devices, people often lack sufficient awareness, understanding, or means of taking effective action. To explore the potential for new tools that support such needs directly we developed Aretha, a privacy assistant technology probe that combines a network disaggregator, personal tutor, and firewall, to empower end-users with both the knowledge and mechanisms to control disclosures from their homes. We deployed Aretha in three households over six weeks, with the aim of understanding how this combination of capabilities might enable users to gain awareness of data disclosures by their devices, form educated privacy preferences, and to block unwanted data flows. The probe, with its novel affordancesand its limitations-prompted users to co-adapt, finding new control mechanisms and suggesting new approaches to address the challenge of regaining privacy in the connected home.

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Section: Designing the Technology Probementioning

confidence: 99%

Section: Overview Of Existing Toolsmentioning

confidence: 99%

Informing the Design of Privacy-Empowering Tools for the Connected Home

Seymour

Kraemer

Binns

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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show abstract

“…They tested their approaches with 81 IoT devices deployed on two different continents and protected by a virtual private network (VPN). Following the same idea, other works ( [8], [9]) are also focused on deducing the type and activity of IoT devices. One of the other goals of using machine learning algorithms for attackers, is to infer the behavior of their victims.…”

Section: Smart Attack In the Literaturementioning

confidence: 99%

Evolution of IoT Security: The Era of Smart Attacks

Bout

Loscrì

Gallais

2022

IEEE Internet Things M.

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“…Device fingerprinting tools like IoTSense [258] and IoTSentinel [259] allow for the reliable identification of devices based on their network traffic. Classifying behaviours of devices themselves is also possible with tools like HomeSnitch [260], Peek-a-Boo [261], PingPong [262], and HoMonit [263]. These can be used to infer why a device is sending data to a particular destination at a particular time.…”

Section: Internet-of-things and Smart Homesmentioning

confidence: 99%

Tracking on the Web, Mobile and the Internet-of-Things

Binns¹

2022

Preprint

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Tracking' is the collection of data about an individual's activity across multiple distinct contexts and the retention, use, or sharing of data derived from that activity outside the context in which it occurred. This paper aims to introduce tracking on the web, smartphones, and the Internet of Things, to an audience with little or no previous knowledge. It covers these topics primarily from the perspective of computer science and humancomputer interaction, but also includes relevant law and policy aspects. Rather than a systematic literature review, it aims to provide an overarching narrative spanning this large research space.Section 1 introduces the concept of tracking. Section 2 provides a short history of the major developments of tracking on the web. Section 3 presents research covering the detection, measurement and analysis of web tracking technologies. Section 4 delves into the countermeasures against web tracking and mechanisms that have been proposed to allow users to control and limit tracking, as well as studies into end-user perspectives on tracking. Section 5 focuses on tracking on 'smart' devices including smartphones and the internet of things. Section 6 covers emerging issues affecting the future of tracking across these different platforms.42 https://perma.cc/UZ7Y-EHLZ 43 https://perma.cc/XL8L-HZJU

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PingPong: Packet-Level Signatures for Smart Home Device Events

Cited by 8 publications

References 0 publications

Informing the Design of Privacy-Empowering Tools for the Connected Home

Informing the Design of Privacy-Empowering Tools for the Connected Home

Evolution of IoT Security: The Era of Smart Attacks

Tracking on the Web, Mobile and the Internet-of-Things

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