Smart Home Systems Security

Hussain, Abir Jaafar; Marcinonyte, Deimante Marcinonyte; Iqbal, Farkhund; Tawfik, Hissam; Baker, Thar; Al‐Jumeily, Dhiya

doi:10.1109/hpcc/smartcity/dss.2018.00235

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…For example, ChienYuan proposed an automation system to realize the expected requirements of power outlet control and environmental weather detection [21], Adrian designed a pneumatic door automation system for accessing control of all smart products [22], and Yu Hsiu proposed an automated energy management system [23]. Thirdly, in research on smart home security, scholars addressed security challenges through a variety of security technologies and approaches [24][25][26][27][28]. It is worth noting that most of these studies have been conducted from the perspective of technological innovation while few studies have focused on positive user experience.…”

Section: Related Work 21 Iot In Smart Homementioning

confidence: 99%

How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design

Xu,

Lee,

Ling

et al. 2024

Electronics

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Advances in Internet of Things (IoT) technologies have had a profound impact on the experiential tasks of the smart home, particularly automated tasks supported by sensors, embedded systems, and communication devices that are capable of autonomously performing a range of tasks previously performed by humans. However, the improvement in the level of smart home automation has not necessarily promoted more meaningful, relaxing, and positive experiences for users. This is largely due to the fact that it often overlooks the meaningful involvement of humans in the process. This paper proposes a conceptual design model for a smart home through two studies that aim to maintain the benefits of automation while ensuring positive experiences with user engagement tasks. Study 1 involved qualitative data using semi-structured interviews to understand what tasks lead to positive user experiences (P-UX) during the use of the smart home and what kind of factors influence these positive experiences. Study 2, using an online questionnaire, quantitatively investigated the varying impact of these factors on positive experiences in both automation and human–machine interactions. The results of both studies show that there are nine factors that influence positive user experience in the smart home, with instrumentality, convenience, and flexibility playing an important role in the positive experience of automation, aesthetics, immersion, association, and memory, having a greater effect on the positive experience of user engagement in interactions, and customization and emotion contributing to both paradigms. In future smart home design, this model will help designers rationally allocate tasks between automation and human–machine interaction as a way to enhance the overall positive user experience.

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Section: Related Work 21 Iot In Smart Homementioning

confidence: 99%

How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design

Xu,

Lee,

Ling

et al. 2024

Electronics

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“…2) RQ2: SMART HOME NETWORK SECURITY Home routers have poor protection against internet-based attacks [87]. Hussain et al [88] showed that various vulnerabilities like default passwords, infrequent password changes, and the absence of system updates could be reduced by accessing the home automation system using a single network. Lounis and Zulkernine [89] provided a taxonomy of attacks in Wi-Fi, Bluetooth, ZigBee, and Radio Frequency Identification (RFID) infrastructures, as well as a survey of assaults on each network technology.…”

Section: ) Rq1: Smart Home Device Securitymentioning

confidence: 99%

“…Eavesdropping [64][65] Node capture and replay attacks [66] Sleep deprivation attack [67] DDoS attack [68] Internet pairing default password [70] Configuration and device authentication [71], [85] Legacy authentication mechanism [72] Exposed services [75] Overprivileged configuration Apps [76], [84] Insecure hardware interfaces [73] OTA Updates and upgradeability weakness [75][76] Critical vulnerabilities: side channel, spyware, and backdoor pin code injection [83] Voice spoofing [79] Communal acts like renting, lending, etc. [80] Hardware-level attacks [82] COTS obsolescence risk [81] RQ2: Network Authentication and communication [87], [89] Default password [85][86] Insecure protocols [24], [91] Susceptibility to MITM attack [88][89], [95] Inappropriate use of encryption [94] Protocol attacks [96], [106] Relay attack [97] Sensitive data [100] Prospective attacks [98][99] Social engineering [99] RQ3: Cloud Information disclosure and access control [116][117] Authentication and verification [119], [94] Exploitable services [112]…”

Section: Rq1: Devicementioning

confidence: 99%

Ranking Security of IoT-Based Smart Home Consumer Devices

AlLifah

Zualkernan

2022

IEEE Access

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Smart home consumer devices like home theatres, music players, voice-based assistants, smart lighting, and security cameras have widely adopted the Internet of Things (IoT). These devices pose a significant security risk to consumers because the devices are exposed to mobile applications and cloud-based services with known security vulnerabilities. IoT technologies also introduce additional vulnerabilities specific to smart home architectures. Most current home consumer devices provide little or no information about the level of security they afford. Since most consumers are not tech-savvy, it is currently difficult for a consumer to make an informed decision about which consumer device model (e.g., which smart television model?) has the best security. Hence, consumers need an objective security ranking of each type (e.g., security cameras) of home consumer devices. This paper proposes a novel methodology to systematically build such security rankings for home consumer devices. The proposed methodology can be applied by utilizing data from any security assessment study. The paper discusses previous efforts in applying Analytic Hierarchy Process (AHP) to rank security risks in general. The paper also presents a systematic survey of security vulnerabilities of smart home consumer devices when viewed from an IoT lens. Using the proposed methodology, a case study where an AHP model for ranking commonly used home consumer devices including home theatres, security cameras, smart lighting, smart speakers, video surveillance, smart switches, home automation systems, home security systems, smart routers, wireless doorbell cameras, and home audio systems, was developed. Relative security rankings for each type of consumer device were derived from the AHP model. According to the AHP model, network security was the primary driver of smart home device security with a priority of 0.6893 while application security had the least priority of 0.0591. Critical Vulnerabilities were the most important for device security (priority=0.4397), Man-in-The-Middle attacks for network security (priority=0.2019), exploitable services for cloud security (priority=0.26), and sensitive data for application security (0.7626). The AHP model was internally consistent (Consistency Ratio < 0.1). Sensitivity analysis showed that the AHP model was robust against pairing assumptions.INDEX TERMS AHP, consumer applications, cloud computing, home consumer devices, Internet of Things, network security, smart home.

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“…In this paper, we do not deal with security and mobility problems. For this, it is suggested to refer to existing methods 9–13 …”

Section: Related Workmentioning

confidence: 99%

Finding the polygon hull of a network without conditions on the starting vertex

Bounceur

Bezoui

Hammoudeh

et al. 2019

Trans Emerging Tel Tech

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Many real-life problems arising within the fields of wireless communication, image processing, combinatorial optimisation, etc., can be modeled by means of Euclidean graphs. In the case of Wireless Sensor Networks (WSN), the overall topology of the graph is not known, because sensor nodes are often randomly deployed. One of the significant problems in this field is the search for boundary nodes. This problem is important in cases such as the surveillance of an area of interest, image contour reconstruction, graph matching problems, routing or clustering data, etc. In the literature, many algorithms are proposed to solve this problem, a recent one of which is the LPCN algorithm and its distributed version D-LPCN which are both based on the concept of a polar angle visit. An inconvenience of these algorithms is the determination of the starting vertex. In effect, the point with the minimum x-coordinate is a possible starting point but it has to be known at the beginning which considerably increases the algorithms' complexity. In this article, we propose a new method called RRLPCN (Reset and Restart with Least Polar-angle Connected Node) which is based on the LPCN algorithm to find the boundary vertices of a Euclidean graph. The main idea is to start the LPCN algorithm from an arbitrary vertex and, whenever it finds a vertex with an x-coordinate smaller than that of the starting one, LPCN is reset and restarted from this new vertex. The algorithm stops as soon as it visits the

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Smart Home Systems Security

Cited by 9 publications

References 13 publications

How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design

How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design

Ranking Security of IoT-Based Smart Home Consumer Devices

Finding the polygon hull of a network without conditions on the starting vertex

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