When CSI Meets Public WiFi

Li, Mengyuan; Meng, Yan; Liu, Junyi; Zhu, Haojin; Liang, Xiaohui; Liu, Yao; Ruan, Na

doi:10.1145/2976749.2978397

Cited by 137 publications

(38 citation statements)

References 21 publications

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“…This type of attack infers the keystrokes by recording changes from the channel state information corresponding to hand or finger movements. There are approaches like WiKey [4] and WindTalker [5] which get the keystrokes on a physical and soft keyboards respectively using external WiFi access points as signal sources. WiPass [6] is presented as a way of getting passwords and authentication patterns using the victim's smartphone personal hotspot as emanation font or, recently, Shen et al [7] present a similar approach to Ali et al [4] and Li et al [5] but using a different classification model.…”

Section: A Wifi-basedmentioning

confidence: 99%

“…There are approaches like WiKey [4] and WindTalker [5] which get the keystrokes on a physical and soft keyboards respectively using external WiFi access points as signal sources. WiPass [6] is presented as a way of getting passwords and authentication patterns using the victim's smartphone personal hotspot as emanation font or, recently, Shen et al [7] present a similar approach to Ali et al [4] and Li et al [5] but using a different classification model. These attacks cannot tolerate changes in the environment other than the victim's hand or finger movement.…”

Section: A Wifi-basedmentioning

confidence: 99%

See 1 more Smart Citation

Eye-based keystroke prediction for natural texts – a feasibility analysis

Cazorla

Fuentes

González-Manzano

2022

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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The use of videoconferencing is on the rise after COVID-19, being common to look at the screen and see someone typing. A side-channel attack may be launched to infer the text written from the face image. In this paper, we analyse the feasibility of such an attack, being the first proposal which work with a complete keyset (50 keys) and natural texts. We use different scenarios, lighting conditions and natural texts to increase realism. Our study involves 30 participants, who typed 49,365 keystrokes. We characterize the effect of lighting, gender, age and use of glasses. Our results show that on average 13.71% of keystrokes are revealed without error, and up to 31.8%, 52.5% and 61.2% are guessed with a maximum error of 1, 2 and 3 keys, respectively.

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Section: A Wifi-basedmentioning

confidence: 99%

Section: A Wifi-basedmentioning

confidence: 99%

Eye-based keystroke prediction for natural texts – a feasibility analysis

Cazorla

Fuentes

González-Manzano

2022

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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“…Some other studies of RAP using the agent, were conducted by Chatfield and Haddad [71], with cosine similarity and data sectoring of RSSI, for identifying the presence of RAP. Li et al [72] proposed a framework to Protect the users from the sensitive keystrokes that reflected by CSI, while Sharma and Gupta [73] developing the three-layered IDS security system, to encounter the RAP and also protecting the user.…”

Section: Agent Deploymentmentioning

confidence: 99%

The rogue access point identification: a model and classification review

Arisandi

Ahmad

Kannan³

2021

IJEECS

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Most people around the world make use of public Wi-Fi hotspots, as their daily routine companion in communication. The access points (APs) of public Wi-Fi are easily deployed by anyone and everywhere, to provide hassle-free Internet connectivity. The availability of Wi-Fi increases the danger of adversaries, taking advantages of sniffing the sensitive data. One of the most serious security issues encountered by Wi-Fi users, is the presence of rogue access points (RAP). Several studies have been published regarding how to identify the RAP. Using systematic literature review, this research aims to explore the various methods on how to distinguish the AP, as a rogue or legitimate, based on the hardware and software approach model. In conclusion, all the classifications were summarized, and produced an alternative solution using beacon frame manipulation technique. Therefore, further research is needed to identify the RAP.

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“…WiFi based fine-grained activities sensing: With the availability of CSI readings from commodity WiFi devices [32], significant progresses have been made for contactless sensing, enabling new fine-grained activity sensing applications such as keystroke identification [48], hand gesture recognition [20], [21], [49], [50], vital sign monitoring [51], [52], [53], [24], [25], [54], [55], and speaking tracking [56]. For non-periodical activity recognition, WiHear was developed as a lip-reading system that uses WiFi CSI to recognize lip movements, which requires directional antennas and is very sensitive to environment changes [56].…”

Section: Related Workmentioning

confidence: 99%

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

Niu

Zhang

Jiang

et al. 2019

IEEE Internet Things J.

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Recent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with Motor Neurone Disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voicebased solutions fail. In contrast, most patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus we propose to develop a Morse code based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the Channel State Information (CSI) between two antennas to enhance the Signal to Noise Ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity.

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When CSI Meets Public WiFi

Cited by 137 publications

References 21 publications

Eye-based keystroke prediction for natural texts – a feasibility analysis

Eye-based keystroke prediction for natural texts – a feasibility analysis

The rogue access point identification: a model and classification review

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

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