Smart-Phones Attacking Smart-Homes

Sivaraman, Vijay; Chan, Dominic; Earl, Dylan; Boreli, Roksana

doi:10.1145/2939918.2939925

Cited by 113 publications

(59 citation statements)

References 5 publications

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“…To evaluate the completeness of ProvThings, we constructed SmartApps for a corpus of 26 possible attacks on IoT platforms through surveying relevant literature [44], [54], [61], [69]. Each attack represents a unique class of malware or a vulnerable app, with 12 based on reported IoT vulnerabilities and 14 migrated from malware classes from smartphone platforms.…”

Section: A Effectivenessmentioning

confidence: 99%

“…A lot of vulnerabilities have been identified in IoT devices [62], [61], [69], [51], [67], [10] and protocols [46], [6]. Fernandes et al [44] conducted the first security analysis of the SmartThings platform.…”

Section: Related Workmentioning

confidence: 99%

“…As long prophesied by our community, the expansion of IoT is also now bringing about new challenges in terms of security and privacy [61], [69], [67], [10]. In some cases, IoT attacks could have chilling safety consequences -burglars can now attack a smart door lock to break into homes [51], and arsonists may even attack a smart oven to cause a fire [42].…”

Section: Introductionmentioning

confidence: 99%

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Fear and Logging in the Internet of Things

Wang¹,

Hassan²,

Bates³

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

153

103

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Abstract-As the Internet of Things (IoT) continues to proliferate, diagnosing incorrect behavior within increasinglyautomated homes becomes considerably more difficult. Devices and apps may be chained together in long sequences of triggeraction rules to the point that from an observable symptom (e.g., an unlocked door) it may be impossible to identify the distantly removed root cause (e.g., a malicious app). This is because, at present, IoT audit logs are siloed on individual devices, and hence cannot be used to reconstruct the causal relationships of complex workflows. In this work, we present ProvThings, a platform-centric approach to centralized auditing in the Internet of Things. ProvThings performs efficient automated instrumentation of IoT apps and device APIs in order to generate data provenance that provides a holistic explanation of system activities, including malicious behaviors. We prototype ProvThings for the Samsung SmartThings platform, and benchmark the efficacy of our approach against a corpus of 26 IoT attacks. Through the introduction of a selective code instrumentation optimization, we demonstrate in evaluation that ProvThings imposes just 5% overhead on physical IoT devices while enabling real time querying of system behaviors, and further consider how ProvThings can be leveraged to meet the needs of a variety of stakeholders in the IoT ecosystem.

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Section: A Effectivenessmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fear and Logging in the Internet of Things

Wang¹,

Hassan²,

Bates³

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

153

103

View full text Add to dashboard Cite

show abstract

“…However, client devices can be exposed to Internet attacks by abusing the Universal Plug-n-Play (UPnP) port forwarding capability on a typical home gateway. We have shown in [10] that a malware application running on an unsuspecting user's mobile device can discover IoT devices within the home by using a standard Simple Service Discovery Protocol (SSDP); this can be followed by a UPnP port forwarding command to the home router that allows an external attacker to directly attack the IoT device. We note that SSDP and UPnP port forwarding messages are common in a typical home network environment for various peer-topeer applications (e.g.…”

Section: The Network Rulesmentioning

confidence: 99%

“…One could argue that the home router, by virtue of its NAT/firewall functionality, provides an effective protection against external attacks by dropping unsolicited Internet traffic directed to household IoT devices. However, our recent work in [10] has shown that even this perimeter defense can be bypassed via malware embedded into mobile Apps; such malware can scout the internal network for vulnerable IoT devices, and disable the home firewall to allow Internet attacks on such devices.…”

Section: Introductionmentioning

confidence: 99%

Low-cost flow-based security solutions for smart-home IoT devices

Sivanathan

Sherratt

Gharakheili

et al. 2016

2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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Abstract-The rapid growth of Internet-of-Things (IoT) devices, such as smart-bulbs, smoke-alarms, webcams, and healthmonitoring devices, is accompanied by escalating threats of attacks that can seriously compromise household and personal safety. Recent works have advocated the use of network-level solutions to detect and prevent attacks on smart-home IoT devices. In this paper we undertake a deeper exploration of network-level security solutions for IoT, by comparing flow-based monitoring with packet-based monitoring approaches. We conduct experiments with real attacks on real IoT devices to validate our flow-based security solution, and use the collected traces as input to simulations to compare its processing performance against a packet-based solution. Our results show that flow-based monitoring can achieve most of the security benefits of packetbased monitoring, but at dramatically reduced processing costs. Our study informs the design of future smart-home network-level security solutions.

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