SSIDs in the wild: Extracting semantic information from WiFi SSIDs

Seneviratne, Suranga; Jiang, Fangzhou; Cunche, Mathieu; Seneviratne, Aruna

doi:10.1109/lcn.2015.7366361

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…Different similarity measures and feature compositions are considered. Along the same lines, the authors of [25] show how SSIDs captured in probe request packets can be used to infer known business venues.…”

Section: Users/device Profilingmentioning

confidence: 96%

Building up knowledge through passive WiFi probes

Redondi

Cesana

2018

Computer Communications

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Section: Users/device Profilingmentioning

confidence: 96%

Building up knowledge through passive WiFi probes

Redondi

Cesana

2018

Computer Communications

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“…• SSID: Service Set Identity is the name that is generally assigned to the owner of a network device so that users can easily find their devices for recognition or connection purpose [50].…”

Section: Iot Devices Securable Parameters For This Workmentioning

confidence: 99%

Securing the Perceptual Layer of the Internet of Things (Iot) Devices Using Elliptic Curve Cryptography

Okediran

Vincent

Abayomi‐Alli

et al. 2023

Preprint

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The security of IOT layers has become very important considering the various attacks faced by IOT devices especially, the perceptual layer. Reported attacks in recent times include direct Denial of Service (DDoS), Botnet attack and Falsification attacks. Several security techniques in place can not be directly implemented on IOT devices; hence, there is a need to improve the research.This work has proposed an ECC based on binary field solution, there were three stages of implementation which includes device registration where each devices are recognised and locked against all forms of communication, device authentication for key agreement and unlocking, communication phase for exchange of information. It was implemented on a contactless ATM card and a mobile phone as an IOT devices used for electronic payment. The evaluation result for storage capacity showed that the mobile phone and the ATM card has the capacity to run ECC security protocol directly, the Jitter result obtained reveals that the Jitter value did not deviate from normal for both devices while, the computational time showed that it is computationally efficient to use ECC over binary field for the security of the perceptual layer.

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“…Signals from the crowd [7] uses gathered SSIDs to discover user's country of origin, device manufacturer, and other, as is [8,9,35]. SSIDs in the Wild [36] are mapping SSIDs to real-world locations, and [10] finds social relation between users by matching PNL. WiFiPi [11] tracks user movement at mass events using a combination of MAC address and SSIDs, using a deployed sensor network.…”

Section: Related Workmentioning

confidence: 99%

SSID Oracle Attack on Undisclosed Wi‐Fi Preferred Network Lists

Dagelić

Perković

Vujatović

et al. 2018

Wireless Communications and Mobile Computing

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User's location privacy concerns have been further raised by today's Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user's PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an example method for building an effective SSID dictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.

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SSIDs in the wild: Extracting semantic information from WiFi SSIDs

Cited by 11 publications

References 9 publications

Building up knowledge through passive WiFi probes

Building up knowledge through passive WiFi probes

Securing the Perceptual Layer of the Internet of Things (Iot) Devices Using Elliptic Curve Cryptography

SSID Oracle Attack on Undisclosed Wi‐Fi Preferred Network Lists

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