A Castle of Glass: Leaky IoT Appliances in Modern Smart Homes

Sun, Andy; Gong, Wei; Shea, Ryan; Liu, Jiangchuan

doi:10.1109/mwc.2017.1800120

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…Many side-channel attacks also have this property. In addition, sensor eavesdropping and firmware analysis may also be considered hardware attacks [66,67]. Considering multiple case studies, Jha et al [68] indicated that microrobots with micro-electro-mechanical systems (MEMS) can forge the data in NB-IOTs.…”

Section: ) Typical Attack Technologiesmentioning

confidence: 99%

A Comprehensive Survey of Security Issues of Smart Home System: “Spear” and “Shields,” Theory and Practice

Yang

Sun

2022

IEEE Access

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The rapid development and wide application of the Internet of Things (IoT) and sensor technologies have produced good opportunities for the development of IoT-based smart home systems (SHSs). However, during the rapid market expansion of SHSs, security challenges associated with SHSs have become a primary concern of people because they are so closely related to people's daily lives. These security problems may damage information assets and pose a serious threat to people's health and life. This study investigates security issues in SHSs and provides a comprehensive overview of research to date. In this review, after analyzing the existing definition and concept of SHSs proposed by authoritative encyclopedias and academic literature, we propose a more accurate, elaborated definition of SHSs, analyze their architecture, extract six natural and contextual features, and summarize spears (cyber-attack means) vs. shields (countermeasures) in detail in the context of SHSs. Then, the security frameworks and evaluation technologies in SHSs are discussed. Different scenarios for technology integration and the practical research results in SHSs, such as blockchain, cloud computing, Internet of Vehicles, and AI are presented meticulously. After that, two special issues related to security are discussed. We believe that future research on SHS security should focus on four aspects: the unification of architecture, resource limitation, fragmentation, and code and firmware security. In addition, research on SHS security should be given priority over its commercialization process.

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Section: ) Typical Attack Technologiesmentioning

confidence: 99%

A Comprehensive Survey of Security Issues of Smart Home System: “Spear” and “Shields,” Theory and Practice

Yang

Sun

2022

IEEE Access

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“…Next, [13] using this IoT device Danmini, Ennio, Ecobee, Philips B120N/10, Provision PT-737E, Provision PT-838, Simple Home XCS7-1002-WHT, Simple Home XCS7-1003-WHT, and Samsung SNH 1011N. Next, type of IoT device is used in toys, such as Hello Barbie, Furby Connect, Toy-Fi Teddy, and I-Que Intelligent Robot [14], [15] state that surveillance cameras are used in the experiment while [16] used Central hub, Lightbulb, Power switch, Motion sensor, Security cam for the testbed. In this journal [14], using toys that can be connected to the internet.…”

Section: Related Workmentioning

confidence: 99%

“…This device can collect the data and samples from the user devices to harm the user in multiple ways. From this paper [12], [13], [15], [16], the device that recently been attack is Ip Camera and Security Camera. There are many reasons for Ip Camera can be easily attacked by malware.…”

Section: Related Workmentioning

confidence: 99%

Constructing IoT Botnets Attack Pattern for Host-based and Network-based Platform

Zaki¹,

Abdullah²,

Yassin³

et al. 2021

IJACSA

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Internet of things (IoT) is the things or devices with software, intelligent sensors interconnected via the internet to send and receive data with another device. This capacity makes things, i.e., smartphones, smart homes, intelligent toys, baby monitors, IP cameras, and many more to act as intelligent devices like artificial intelligence (AI) and be utilized in the everyday lifeworld widely. IoT has enormous expansion potential, and many challenges have been acknowledged but are still open today. The botnet is a collection of bots from IoT devices used to launch extensive network attacks. In addition, rapid growth in technology has led to an incomplete understanding of IoT. The increasing number of IoT devices has led to the spread of malware targeting IoT devices make IoT Botnet behaviors challenging to identify and determine. To detect these IoT Botnets, a preliminary experiment on flow analysis is necessary. This paper is to identify IoT Botnet attack patterns from the IoT Botnet behavior that get from IoT Botnet activities. Therefore, this research is to identify IoT Botnet attack patterns in a hostbased and network-based environment. First, this paper contributes to discovering, recognizing, categorizing, and detecting IoT Botnet activities. Next, organizing information to have a better understanding of the IoT botnet's problem and potential solutions. Then, construct the IoT Botnet attack pattern by analyzing the characteristics of the IoT Botnet behavior. This IoT Botnet attack pattern divides into two environments which are host-based and network-based. As a result, this paper aims to inform people about the attack pattern when the IoT device has been infected and become part of the botnet.

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“…Such networking has allowed the diffusion of different Internet-of-Things (IoT) applications providing end users with innovative services. At the same time though, these networks are posing new potential threats to security and privacy [1].…”

Section: Introductionmentioning

confidence: 99%

Looking Through Walls: Inferring Scenes from Video-Surveillance Encrypted Traffic

Mari

Piazzetta

Bordin

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Nowadays living environments are characterized by networks of interconnected sensing devices that accomplish different tasks, e.g., video surveillance of an environment by a network of CCTV cameras. A malicious user could gather sensitive details on people's activities by eavesdropping the exchanged data packets. To overcome this problem, video streams are protected by encryption systems, but even secured channels may still leak some information. In this paper, we show that it is possible to infer visual data by intercepting the encrypted video stream of a surveillance system, and how this may be leveraged to track the movements of a person inside the secured area. We trained an automatic classifier on a computer graphic simulator and tested it on real videos, with standard encryption protocols. Experiments proved the transferability of the classifier trained on synthetic sequences, succeeding in the detection of up to four different walking directions on real videos, with a limited amount of intercepted traffic.

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A Castle of Glass: Leaky IoT Appliances in Modern Smart Homes

Cited by 7 publications

References 9 publications

A Comprehensive Survey of Security Issues of Smart Home System: “Spear” and “Shields,” Theory and Practice

A Comprehensive Survey of Security Issues of Smart Home System: “Spear” and “Shields,” Theory and Practice

Constructing IoT Botnets Attack Pattern for Host-based and Network-based Platform

Looking Through Walls: Inferring Scenes from Video-Surveillance Encrypted Traffic

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