Preserving privacy in secured ZigBee wireless sensor networks

Santos, Jessye Dos; Hennebert, Christine; Lauradoux, Cédric

doi:10.1109/wf-iot.2015.7389142

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…Metadata can in turn be extracted in order to discover the identities of participants (MAC addresses, IP addresses, UDP ports) and their roles, but also the characteristics of the network, allowing attackers to identify the communication protocols employed and thus to exploit their weaknesses. This is the case of ZigBee, which offers many vulnerabilities as regards eavesdropping [180]. The first privacy attack is eavesdropping, which is also an MITM attack [178].…”

Section: Privacy Issuesmentioning

confidence: 99%

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

et al. 2021

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During the last decade, the smart grid (SG) concept has started to become a reality, mainly thanks to the technical progress achieved in telecommunications, informatics and power electronics, among other domains, leading to an evolution of the traditional electrical grid into an intelligent one. Nowadays, the SG can be seen as a system of smart systems that include cyber and physical parts from different technologies that interact with each other. In this context, intelligent buildings (IBs) constitute a paradigm in which such smart systems are able to guarantee the comfort of residents while ensuring an appropriate tradeoff of energy production and consumption by means of an energy management system (EMS). These interconnected EMSs remain the objective of potential cyber-attacks, which is a major concern. Therefore, this paper conducts a survey, from a multidisciplinary point of view, of some of the main security and privacy issues related to IBs as part of the SG, including an overview of EMS, smart meters, and the main communication networks employed to connect IBs to the overall SG. Future research directions towards a security enhancement from both technical and human perspectives are also provided.

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Section: Privacy Issuesmentioning

confidence: 99%

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

et al. 2021

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“…The parsed AST with symbols resolved (parsedSmblAST) is then input to the AnalyzeClass function (line 7) which recursively analyzes all related classes to identify the dependencies among variables. During the class analysis, each method which belongs to the class is analyzed by the AnalyzeMethods function, to find the domain-specific types, local actions and branch information (lines [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. If a value Ev.v is equivalent to a variable with a known domain type (dT), HOMESCAN assigns the dT to the Ev.t (lines [16][17][18][19][20][21][22][23][24].…”

Section: Inference Of Protocol Informationmentioning

confidence: 99%

“…input, where ast is the parsed AST with resolved symbols; dTmap is a map of (node, domain type, related method); IK is the initial knowledge and returns an updated dTmap. The function analyzes each method node (methodNode) in the input ast (lines [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. During the analysis, HOMESCAN gets the child nodes of methodNode, and further analyzes each child node n based on its expression (lines 30-42).…”

Section: Domain Type Annotation and Local Action Identification (Lines 26-45)mentioning

confidence: 99%

Scrutinizing Implementations of Smart Home Integrations

Mahadewa

Wang

Bai

et al. 2021

IIEEE Trans. Software Eng.

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A key feature of the booming smart home is the integration of a wide assortment of technologies, including various standards, proprietary communication protocols and heterogeneous platforms. Due to customization, unsatisfied assumptions and incompatibility in the integration, critical security vulnerabilities are likely to be introduced by the integration. Hence, this work addresses the security problems in smart home systems from an integration perspective, as a complement to numerous studies that focus on the analysis of individual techniques. We propose HOMESCAN, an approach that examines the security of the implementations of smart home systems. It extracts the abstract specification of application-layer protocols and internal behaviors of entities, so that it is able to conduct an end-to-end security analysis against various attack models. Applying HOMESCAN on three extensively-used smart home systems, we have found twelve non-trivial security issues, which may lead to unauthorized remote control and credential leakage.

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“…We have modified and designed algorithms pertaining to 3D cloaking and monitoring in our earlier paper [16]. Jessye and others [17] have worked for information leakage privacy protection in zigbee network based applications. F. Giselle and others [18] have demonstrated use of holomorphic encryption techniques for privacy which do not show the effect of 3 rd dimension of location.…”

Section: Related Workmentioning

confidence: 99%

Privacy Aware Monitoring of Mobile Users in Sensor Networks Environment

Jagdale

Bakal²

2019

Int. J. Interact. Mob. Technol.

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<p><strong>Abstract—</strong>Due to complex monitoring systems in various business domains, secrecy and privacy has become critical issue for movable resources including human being. In a mobile object monitoring systems, balancing computing resources and quality of privacy is required. Objects can be mobile devices or users or any other moving entity. This work is presented in wireless sensor network environment. Earlier work does not consider presence of mobile objects in 3D space. We can observe that objects also carry Z axis in city area where high rise buildings are present. Earlier monitoring applications are mainly designed in two dimensional space to protect privacy. Our novelty is to suggest and design mechanism that reflect Z position (height) of mobile objects for protecting privacy. While calculating cloaking area and counting of objects, height is also considered as third dimension. This results in better location privacy as compared to the privacy delivered by the scheme that considers two dimensional space. We have presented performance, communications cost and privacy strength of modified 3D quality algorithm and 3D resource algorithm. Moreover, we present novel containment resolution algorithm that handles duplicate counting due to 3D presence of wireless system and mobile devices.</p>

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Preserving privacy in secured ZigBee wireless sensor networks

Cited by 12 publications

References 5 publications

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

Scrutinizing Implementations of Smart Home Integrations

Privacy Aware Monitoring of Mobile Users in Sensor Networks Environment

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