Socio-Technical Study on the Effect of Trust and Context When Choosing WiFi Names

Ferreira, Ana; Huynen, Jean-Louis; Koenig, Vincent; Lenzini, Gabriele; Rivas, Salvador

doi:10.1007/978-3-642-41098-7_9

Cited by 14 publications

(14 citation statements)

References 12 publications

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“…More sophisticated evaluation of trust may take into account other aspects, e. g., current location of an employee [27]. Strength r of a network can be associated with the number of bars showing the strength of the signal, and one may expect that people would chose networks with high signal strength.…”

Section: Selection Of a Wi-fi Network With Mavtmentioning

confidence: 99%

“…Participants were asked to rank networks according to the order in which they would try to connect to the networks, assuming they have the password to access every available Wi-Fi. The network names were composed from randomly generated alphabetic characters and numbers to avoid bias due to perceived familiarity of network names (see [27] for a study of this effect). The names of screenshots encode the manner of presentation, which relates closely to the criteria to be used in the MAVT model presented in Section 5.2.…”

Section: Design Of Experimentsmentioning

confidence: 99%

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Modeling and analysis of influence power for information security decisions

Yevseyeva

Morisset

Moorsel

2016

Performance Evaluation

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Users of computing systems and devices frequently make decisions related to information security, e. g., when choosing a password, deciding whether to log into an unfamiliar wireless network. Employers or other stakeholders may have a preference for certain outcomes, without being able to or having a desire to enforce a particular decision. In such situations, systems may build in design nudges to influence the decision making, e. g., by highlighting the employer's preferred solution. In this paper we model influencing in information security to identify which approaches to influencing are most effective and how they can be optimized. To do so, we extend traditional multi-criteria decision analysis models with modifiable criteria, to represent the available approaches an influencer has for influencing the choice of the decision maker. The notion of influence power is introduced to characterize the extent to which an influencer can influence decision makers. We illustrate our approach using data from a controlled experiment on techniques to influence which public wireless network users select. This allows us to calculate influence power and identify which design nudges exercise the most influence over user decisions.

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Section: Selection Of a Wi-fi Network With Mavtmentioning

confidence: 99%

Section: Design Of Experimentsmentioning

confidence: 99%

Modeling and analysis of influence power for information security decisions

Yevseyeva

Morisset

Moorsel

2016

Performance Evaluation

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“…We used randomly generated network names to avoid biasing the results due to perceived familiarity with the network (see work on this by [13]. )…”

Section: B Study Materialsmentioning

confidence: 99%

Decision Justifications for Wireless Network Selection

Jeske

Coventry

Briggs

2014

2014 Workshop on Socio-Technical Aspects in Security and Trust

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Abstract-A number of security risks are associated with the selection of wireless networks. We examined wireless network choices in a study involving 104 undergraduate social science students. One research goal was to examine the extent to which features (such as padlocks) and colours could be used to 'nudge' individuals towards more secure network and away from open (unsecured) network options. Another goal was to better understand the basis for their decision-making. Using qualitative as well as quantitative data, we were able to differentiate groups whose decision were driven by security concerns, those who made convenience-based decisions, and those whose motives were unclear or undefined. These groups made different network choices, in part due to different perceived functionality of the padlock. We further observed significant effects for the use of colour when nudging participants towards more secure choices. We also wanted to examine the role of individual differences in relation to the choices individuals make. Perceived controllability of risk played a role in terms of the extent to which participants would make more secure vs. unsecure choices, although we obtained no significant group differences when we examined these variables in relation to the different decision justification groups. This indicates that perceived risk perceptions and reasons for decisions may relate differently to the actual behavioural choices individuals make, with perceptions of risk not necessarily relating to the reasons that participants consider when making security decisions.

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“…1: Geo-distribution of the collected SSIDs in Sydney studied the names of user devices that are sending mDNS in a WiFi network and found that 59% of the device names contained real names and 17.6% contained both first name and last name. Ferreira et al [15] investigated the effect that trust and context have on users when choosing wireless networks. For example, it was shown that users tend to connect to the networks known to them or have connected in the past, despite not completely trusting them.…”

Section: Related Workmentioning

confidence: 99%

“…Various aspects on how users name their smart devices has been also explored [10], [11], [12], [13], [14], [15]. O'Neill et al [10] studied Bluetooth names of 1,701 devices and showed that 42% devices tend to use default names given by the manufacturer.…”

Section: Related Workmentioning

confidence: 99%

SSIDs in the wild: Extracting semantic information from WiFi SSIDs

Seneviratne

Jiang

Cunche

et al. 2015

2015 IEEE 40th Conference on Local Computer Networks (LCN)

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Abstract-WiFi networks are becoming increasingly ubiquitous. In addition to providing network connectivity, WiFi finds applications in areas such as indoor and outdoor localisation, home automation, and physical analytics. In this paper, we explore the semantics of one key attribute of a WiFi network, SSID name. Using a dataset of approximately 120,000 WiFi access points and their corresponding geo-locations, we use a set of similarity metrics to relate SSID names to known business venues such as cafes, theatres, and shopping centres. Such correlations can be exploited by an adversary who has access to smartphone users preferred networks lists to build an accurate profile of the user and thus can be a potential privacy risk to the users.

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Socio-Technical Study on the Effect of Trust and Context When Choosing WiFi Names

Cited by 14 publications

References 12 publications

Modeling and analysis of influence power for information security decisions

Modeling and analysis of influence power for information security decisions

Decision Justifications for Wireless Network Selection

SSIDs in the wild: Extracting semantic information from WiFi SSIDs

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