Systematically Debugging IoT Control System Correctness for Building Automation

Liang, Chieh-Jan Mike; Bu, Lei; Zhao, Li; Zhang, Junbei; Han, Soohee; Karlsson, Börje F.; Zhang, Dongmei; Zhao, Feng

doi:10.1145/2993422.2993426

Cited by 45 publications

(31 citation statements)

References 27 publications

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“…Many do not support desired complex functionality [4], many TAP interfaces lack feedback during rule creation [19], and temporal issues in TAP can lead to inaccurate mental models [22]. Initial attempts to mitigate some of these problems leverage techniques typical of software engineering, including formal verification [34,57], information-flow control [49], privilege isolation [15], and dynamic instrumentation [52].…”

Section: Related Workmentioning

confidence: 99%

“…The TAP approach can be used to connect physical IoT devices, as in the example above, or as a substitute for shell scripting in workplace environments (e.g., allowing novice programmers to write rules that automatically back up data). Academic researchers have recognized the effectiveness of this paradigm for end-user development, conducting a number of studies that rely on TAP or a close variant [4,9,13,18,19,22,23,34,[50][51][52].…”

Section: Introductionmentioning

confidence: 99%

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How Users Interpret Bugs in Trigger-Action Programming

Brackenbury

Deora

Ritchey

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Trigger-action programming (TAP) is a programming model enabling users to connect services and devices by writing if-then rules. As such systems are deployed in increasingly complex scenarios, users must be able to identify programming bugs and reason about how to fix them. We first systematize the temporal paradigms through which TAP systems could express rules. We then identify ten classes of TAP programming bugs related to control flow, timing, and inaccurate user expectations. We report on a 153-participant online study where participants were assigned to a temporal paradigm and shown a series of pre-written TAP rules. Half of the rules exhibited bugs from our ten bug classes. For most of the bug classes, we found that the presence of a bug made it harder for participants to correctly predict the behavior of the rule. Our findings suggest directions for better supporting end-user programmers.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Users Interpret Bugs in Trigger-Action Programming

Brackenbury

Deora

Ritchey

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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“…SIFT [31] and Salus [30] are designed to help users debug trigger-action rules by verifying whether the devices' interaction (through automation rules) meets users' expectations. VI: Rules for case study.…”

Section: A Security Comparisonmentioning

confidence: 99%

“…However, they only unroll the while loop for a fixed number of steps (k), and thus may have false negatives for violations occurring after k steps. The subsequent work, Salus [30], adopts model-checking techniques as we do. However, it is unclear whether Salus can handle the growing complexity in IoT, since their experiments show that the time needed for verification increases exponentially after irrelevant devices are installed.…”

Section: R4mentioning

confidence: 99%

“…In each run of the experiment, we randomly sampled N out of the 4,161 rules we encoded and generate security polices with respect to these sampled rules to simulate different scenarios. SAFECHAIN can work with most model checkers; to compare with previous work [30], we use the open-sourced model checker, NuSMV, as our backend tool. Dataset.…”

Section: Performance Evaluationmentioning

confidence: 99%

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SafeChain: Securing Trigger-Action Programming From Attack Chains

Hsu

Chiang

Hsiao

2019

IEEE Trans.Inform.Forensic Secur.

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The proliferation of Internet of Things (IoT) is reshaping our lifestyle. With IoT sensors and devices communicating with each other via the Internet, people can customize automation rules to meet their needs. Unless carefully defined, however, such rules can easily become points of security failure as the number of devices and complexity of rules increase. Device owners may end up unintentionally providing access or revealing private information to unauthorized entities due to complex chain reactions among devices. Prior work on trigger-action programming either focuses on conflict resolution or usability issues, or fails to accurately and efficiently detect such attack chains. This paper explores security vulnerabilities when users have the freedom to customize automation rules using triggeraction programming. We define two broad classes of attackprivilege escalation and privacy leakage -and present a practical model-checking-based system called SAFECHAIN that detects hidden attack chains exploiting the combination of rules. Built upon existing model-checking techniques, SAFECHAIN identifies attack chains by modeling the IoT ecosystem as a Finite State Machine. To improve practicability, SAFECHAIN avoids the need to accurately model an environment by frequently rechecking the automation rules given the current states, and employs rule-aware optimizations to further reduce overhead. Our comparative analysis shows that SAFECHAIN can efficiently and accurately identify attack chains, and our prototype implementation of SAFECHAIN can verify 100 rules in less than one second with no false positives.

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Automated Verification and Validation of IoRT Systems

Devi¹,

Nalini²

2021

Human Communication Technology

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Systematically Debugging IoT Control System Correctness for Building Automation

Cited by 45 publications

References 27 publications

How Users Interpret Bugs in Trigger-Action Programming

How Users Interpret Bugs in Trigger-Action Programming

SafeChain: Securing Trigger-Action Programming From Attack Chains

Automated Verification and Validation of IoRT Systems

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