Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes. While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment. In this paper we perform a systematic security evaluation of two popular smart home platforms, Google's Nest platform and Philips Hue, that implement home automation "routines" (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store. Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines. This analysis results in ten key findings with serious security implications. For instance, we demonstrate the potential for the misuse of smart home routines in the Nest platform to perform a lateral privilege escalation, illustrate how Nest's product review system is ineffective at preventing multiple stages of this attack that it examines, and demonstrate how emerging platforms may fail to provide even bare-minimum security by allowing apps to arbitrarily add/remove other apps from the user's smart home. Our findings draw attention to the unique security challenges of platforms that execute routines via centralized data stores, and highlight the importance of enforcing security by design in emerging home automation platforms.
more »
« less
Security in Centralized Data Store-based Home Automation Platforms: A Systematic Analysis of Nest and Hue
Home automation platforms enable consumers to conveniently automate various physical aspects of their homes. However, the security flaws in the platforms or integrated third-party products can have serious security and safety implications for the user’s physical environment. This article describes our systematic security evaluation of two popular smart home platforms, Google’s Nest platform and Philips Hue, which implement home automation “routines” (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store . Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines, and it leads to 11 key findings with serious security implications. We combine several of the vulnerabilities we find to demonstrate the first end-to-end instance of lateral privilege escalation in the smart home, wherein we remotely disable the Nest Security Camera via a compromised light switch app. Finally, we discuss potential defenses, and the impact of the continuous evolution of smart home platforms on the practicality of security analysis. Our findings draw attention to the unique security challenges of smart home platforms and highlight the importance of enforcing security by design.
more »
« less
- Award ID(s):
- 1815336
- NSF-PAR ID:
- 10334416
- Date Published:
- Journal Name:
- ACM Transactions on Cyber-Physical Systems
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2378-962X
- Page Range / eLocation ID:
- 1 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Prior work has developed numerous systems that test the security and safety of smart homes. For these systems to be applicable in practice, it is necessary to test them with realistic scenarios that represent the use of the smart home, i.e., home automation, in the wild. This demo paper presents the technical details and usage of Helion, a system that uses n-gram language modeling to learn the regularities in user-driven programs, i.e., routines developed for the smart home, and predicts natural scenarios of home automation, i.e., event sequences that reflect realistic home automation usage. We demonstrate the HelionHA platform, developed by integrating Helion with the popular Home Assistant smart home platform. HelionHA allows an end-to-end exploration of Helion’s scenarios by executing them as test cases with real and virtual smart home devices.more » « less
-
This paper focuses on developing a security mechanism geared towards appified smart-home platforms. Such platforms often expose programming interfaces for developing automation apps that mechanize different tasks among smart sensors and actuators (e.g., automatically turning on the AC when the room temperature is above 80 F). Due to the lack of effective access control mechanisms, these automation apps can not only have unrestricted access to the user's sensitive information (e.g., the user is not at home) but also violate user expectations by performing undesired actions. As users often obtain these apps from unvetted sources, a malicious app can wreak havoc on a smart-home system by either violating the user's security and privacy, or creating safety hazards (e.g., turning on the oven when no one is at home). To mitigate such threats, we propose Expat which ensures that user expectations are never violated by the installed automation apps at runtime. To achieve this goal, Expat provides a platform-agnostic, formal specification language UEI for capturing user expectations of the installed automation apps' behavior. For effective authoring of these expectations (as policies) in UEI, Expat also allows a user to check the desired properties (e.g., consistency, entailment) of them; which due to their formal semantics can be easily discharged by an SMT solver. Expat then enforces UEI policies in situ with an inline reference monitor which can be realized using the same app programming interface exposed by the underlying platform. We instantiate Expat for one of the representative platforms, OpenHAB, and demonstrate it can effectively mitigate a wide array of threats by enforcing user expectations while incurring only modest performance overhead.more » « less
-
The NTT (Nippon Telegraph and Telephone) Data Corporation report found that 80% of U.S. consumers are concerned about their smart home data security. The Internet of Things (IoT) technology brings many benefits to people's homes, and more people across the world are heavily dependent on the technology and its devices. However, many IoT devices are deployed without considering security, increasing the number of attack vectors available to attackers. Numerous Internet of Things devices lacking security features have been compromised by attackers, resulting in many security incidents. Attackers can infiltrate these smart home devices and control the home via turning off the lights, controlling the alarm systems, and unlocking the smart locks, to name a few. Attackers have also been able to access the smart home network, leading to data exfiltration. There are many threats that smart homes face, such as the Man-in-the-Middle (MIM) attacks, data and identity theft, and Denial of Service (DoS) attacks. The hardware vulnerabilities often targeted by attackers are SPI, UART, JTAG, USB, etc. Therefore, to enhance the security of the smart devices used in our daily lives, threat modeling should be implemented early on in developing any given system. This past Spring semester, Morgan State University launched a (senior) capstone project targeting undergraduate (electrical) engineering students who were thus allowed to research with the Cybersecurity Assurance and Policy (CAP) center for four months. The primary purpose of the capstone was to help students further develop both hardware and software skills while researching. For this project, the students mainly focused on the Arduino Mega Board. Some of the expected outcomes for this capstone project include: 1) understanding the physical board components, 2) learning how to attack the board through the STRIDE technique, 3) generating a Data Flow Diagram (DFD) of the system using the Microsoft threat modeling tool, 4) understanding the attack patterns, and 5) generating the threat based on the user's input. To prevent future threats and attacks from taking advantage of systems vulnerabilities, the practice of "threat modeling" is implemented. This method allows the analysis of potential attackers, including their goals and techniques, while also providing solutions and mitigation strategies. Although Threat modeling can be performed throughout the development of a system, implementing it during developmental stages will prevent further problems in the future. Threat Modeling is crucial because it will help identify any potential threat before it propagates in the system. Identifying threats and providing countermeasures will save both time and money while also keeping the consumers safe. As a result, students must grow to understand how essential detecting and preventing attacks are to protect consumer information systems and networks. At the end of this capstone project, students should take away hands-on skills in cyber defense.more » « less
-
Many studies of mobile security and privacy are, for simplicity, limited to either only Android users or only iOS users. However, it is not clear whether there are systematic differences in the privacy and security knowledge or preferences of users who select these two platforms. Understanding these differences could provide important context about the generalizability of research results. This paper reports on a survey (n=493) with a demographically diverse sample of U.S. Android and iOS users. We compare users of these platforms using validated privacy and security scales (IUIPC-8 and SA-6) as well as previously deployed attitudinal and knowledge questions from Pew Research Center. As a secondary analysis, we also investigate potential differences among users of different smart-speaker platforms, including Amazon Echo and Google Home. We find no significant differences in privacy attitudes of different platform users, but we do find that Android users have more technology knowledge than iOS users. In addition, we find evidence (via comparison with Pew data) that Prolific participants have more technology knowledge than the general U.S. population.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3418286
