A risk analysis of a smart home automation system
Introduction
In the near future, it is estimated that somewhat 90 million people around the world will live in smart homes, using technology to improve home security, comfort, and energy usage [1]. A recent study has shown that more than every fourth person in Sweden feels that they have poor knowledge and control over their energy use, and that four out of ten would like to be more aware and to have better control over their energy consumption [2]. A solution is to provide the householders with feedback on their energy consumption, for instance, through a smart home automation system [3]. Studies have shown that householders can reduce energy consumption with up to 20% when gaining such feedback [2], [3]. Smart home automation is a prime example of a smart environment built on various types of cyber–physical systems generating volumes of diverse, heterogeneous, complex, and distributed data from a multitude of applications and sensors. Thereby, such home automation is also an example of an Internet of Things (IoT) scenario, where a communication network extends the present Internet by including everyday items and sensors, which in this case includes the possibility to monitor and manage energy usage [4]. As such, smart home automation systems incorporate common devices that control features of the home, but they do not only turn devices on and off [5]. For instance, smart home automation systems can monitor the configuration of the internal environment and the activities that are being undertaken whilst the house is occupied (and unoccupied). The result of these modifications to the technology is that a smart home automation system can autonomously operate devices and thus manage the home on behalf of the end-users, i.e., humans.
Smart home automation is attracting more and more attention from commercial actors, such as, energy suppliers, infrastructure providers, and third party software and hardware vendors [6], [3]. Among the non-commercial stakeholders, there are various governmental institutions and municipalities, as well as, end-users. Knowledge, tools, and infrastructures related to software and data have begun to evolve in order to cover the challenges brought on by the complexity and the heterogeneity of massively inter-connected services and devices, but there is at this point no well-established practice to design such intelligent systems [7]. For instance, accepted reference architecture alternatives or software platforms, let alone such that include otherwise crucial system requirements, such as, security and privacy in the process are currently missing [7], [8]. As a result, there are multiple vertical solutions where vendors claim to support the whole chain from the sensors and devices to the gateways and servers, with whatever dedicated software that is appropriate in the perspective of the specific company. For example, this includes highly specialized APIs for the integration of additional services on top of the existing solutions. This creates a complex situation where, among many things, it is hard to avoid customer lock-in, something which may further smother their involvement and commitment. As a consequence, this also creates difficulties for executing system-hygienic tasks, such as, analyzing risks, enhancing privacy, and enforcing security in these environments.
In a joint research project involving leading industrial actors in the segment of home/building automation, a common interface of a smart home automation system (hereinafter denoted SHAS) that combines various vendors’ systems has been developed.1 Using SHAS, it is possible to transparently manage several smart home automation systems simultaneously in real-time. It is also possible for third party stakeholders, such as, property owners and municipalities, to both monitor energy consumption and remotely control electronic devices in the homes and buildings. Furthermore, end-users (e.g., as tenants) can collect aggregated energy consumption statistics on their buildings (e.g., from the owners). Based on the collected data, various services can be implemented, primarily as a way to raise the energy-awareness among end-users, e.g., by using gamification approaches. Also, on top of the common interface, an open mobile platform for energy efficiency services allows end-users to access various applications through an ecosystem of online services and smartphone applications. Through an open API, it is also possible for third party developers to connect their services and applications. In the research project, SHAS is tested on an apartment complex situated in Malmö, Sweden.
In IoT systems, particularly in those that involve human actors, such as, our SHAS, understanding the risks related to the use and potential misuse of information about customers, partners, and end-users, as well as, forming methods for integrating security-enhancing measures in the design is not straightforward and thus requires substantial analysis [4], [9]. In addition, measures ensuring the IoT architecture’s resilience to attacks, such as, authentication, access control, and user privacy need to be established [10]. In fact, the difficulty in achieving security in IoT environments has been identified as one of the top barriers of smart home automation [7], underlining that this is a cumbersome, yet important task.
In this paper, we apply a common risk analysis method in order to evaluate system vulnerabilities and threats, as well as, their likeliness of occurrence and potential impacts, i.e., the system’s risk exposure. The analysis of risk exposure in SHAS is thus based on the well-known Information Security Risk Analysis (ISRA) method, documented by, e.g., Peltier [11]. The application of ISRA on SHAS is founded on a review of current advancements in science and industry. In order to fully understand the scope of the consequences brought on by smart homes, it is crucial to analyze not only the system risks related to privacy and security, but also the types of scenarios with respect to user privacy and home security that they entail. The main contribution is thus the results of the risk analysis on the smart home automation system in combination with the scenarios highlighting the consequences to user privacy and the review of the state of the art.
The paper is organized as follows. First, we set the scene by introducing the potential risk scenarios with respect to security and privacy of smart home automation. Then, related work, the architecture of SHAS, the ISRA method and its results are accounted for. This is followed by a discussion about the general risk exposure in relation to the main observations from the literature and scenario descriptions. In the end, conclusions and pointers for future work are summarized.
Section snippets
Scenarios of the private/public home
Before examining the risk exposure of SHAS by applying ISRA, we pinpoint some common scenarios for smart home automation systems. These scenarios have emerged as a result of discussions with key stakeholders within the smart home automation industry, i.e., the industry partners of the project management group of SHAS.
Property, as well as, users and the information that they are generating constitute an integral part of smart home automation, and as smart home automation systems become
Related work
The reviewed related work presented below has been assimilated based on the research field of the project, i.e., smart home automation, and on the scenarios as introduced above. The account for related work is grouped in four sections, based on the main theme of the reviewed work, and in the end, some general observations are presented. The results of this study are also incorporated in the ISRA design, as described in 5.
SHAS architecture
In smart home automation, energy services depend on a broad range of connected hardware and software components for monitoring and controlling an apartment or building [30], [31], [32]. In the case of SHAS, these sensors and actuators record and report metrics, such as, water usage, indoor temperature, CO2 levels, and power consumption. Each device runs independently of each other and communicates using a local mesh network; in this case Zigbee (see, e.g., [33], [34]) is used with a home
The information security risk analysis methodology
The reason for applying the ISRA approach in the development phase of SHAS is motivated based on the main observations made in the review of related work accounted for in 3. Proper and efficient integration of security in IoT-based smart home systems must be founded on the sound analysis of risk, i.e., the likeliness of loss [36], [11]. In order to enable the identification of a reasonable level of security in SHAS, a methodology that embraces central security concepts like confidentiality,
ISRA results
In this section, the results from ISRA applied on SHAS are presented. A total of 32 risks were identified during the risk analysis session. Each risk is represented by the following six attributes: a unique identifier according to description in the previous section, an explanation of the vulnerability exploited by the risk, an explanation of the threat that the risk poses, the mean probability value, the mean consequence value, and the resulting risk value. Both the probability and the
Discussion
As pointed out in 2, extending products and services to the residents of a smart home make it possible to collect additional information about the household. In the case of smart home automation, this process takes place in people’s ordinary lives and typically without their understanding of the implications that this collection may have on their privacy and home security. The digital traces that the users leave behind and that the various stakeholders can collect may also be combined and
Conclusion
In a joint research project involving leading industrial actors in the segment of home/building automation, a common interface of a smart home automation system that combines various vendors’ systems has been developed. Using this common interface, third party stakeholders can both monitor energy consumption and remotely control electronic devices in the homes and buildings. Open system architecture allows end-users to access various applications through an ecosystem of online services and
Future work
A general observation is that a more concentrated focus on the development of integrated and automated risk analysis tools that are easy to use is a topic that has not yet been given the attention it deserves. As an example, a systematic and rigorous risk analysis process that includes more analytical aspects, such as, normalization and calibration of evaluations from the evaluators, would be an interesting step forward. With access to such approaches for the analysis of risk and threats in
Acknowledgments
This work has been carried out within the project “Mobile Services for Energy Efficiency in Existing Buildings”, partially funded by Vinnova (Grant No. 2012-01245)—the Swedish Governmental Agency for Innovation Systems. The authors would also like to thank all the members of the project.
Andreas Jacobsson (b. 1977), Assistant Professor in Computer Science at Malmö University. Jacobsson received his Ph.D. in Computer Science in 2008 at Blekinge Institute of Technology in Sweden. His research interests include the theory and application of information security in Internet-based information systems. The results of this work have been published in more than 30 peer-reviewed scientific articles published in international books, journals and conference proceedings. He is a member of
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Andreas Jacobsson (b. 1977), Assistant Professor in Computer Science at Malmö University. Jacobsson received his Ph.D. in Computer Science in 2008 at Blekinge Institute of Technology in Sweden. His research interests include the theory and application of information security in Internet-based information systems. The results of this work have been published in more than 30 peer-reviewed scientific articles published in international books, journals and conference proceedings. He is a member of the Internet of Things and People Research Center at Malmö University. He is also the Dean of Education at the Faculty of Technology and Society at his University.
Martin Boldt (b. 1977), Assistant Professor in Computer Science at Blekinge Institute of Technology. He received his Ph.D. in Computer Science in 2010 at Blekinge Institute of Technology in Sweden. His research interests include information security, privacy, and data mining. The results of this work have been published in numerous peer-reviewed scientific articles published in international books, journals, and conference proceedings.
Bengt Carlsson (b. 1951), Professor in Computer Science at Blekinge Institute of Technology. His doctoral thesis concerned the multi-agent area with a focus on evolutionary and game theoretical models for explaining both competing and cooperating mechanisms within distributed agent systems. Today, he combines education within candidate and master security programs with research and supervising of Ph.D. students. He has more than 50 peer-reviewed scientific articles published in international books, journals and conference proceedings. The main recent research areas are privacy within information ecosystems, security within virtual companies, malware behavior/prevention and analysis of software in commercial use.