Recognizing Keystrokes Using WiFi Devices
Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ali, Kamran [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE journal on selected areas in communications - New York, NY : IEEE, 1983, 35(2017), 5, Seite 1175-1190 |
|---|---|
| Übergeordnetes Werk: |
volume:35 ; year:2017 ; number:5 ; pages:1175-1190 |
| Links: |
|---|
| DOI / URN: |
10.1109/JSAC.2017.2680998 |
|---|
| Katalog-ID: |
OLC1994911999 |
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| 245 | 1 | 0 | |a Recognizing Keystrokes Using WiFi Devices |
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| 520 | |a Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. | ||
| 650 | 4 | |a Shape | |
| 650 | 4 | |a Keystroke recognition | |
| 650 | 4 | |a Wireless fidelity | |
| 650 | 4 | |a human computer interaction (HCI) | |
| 650 | 4 | |a Activity recognition | |
| 650 | 4 | |a Privacy | |
| 650 | 4 | |a wireless sensing | |
| 650 | 4 | |a Wireless communication | |
| 650 | 4 | |a wireless security | |
| 650 | 4 | |a Receivers | |
| 650 | 4 | |a Keyboards | |
| 700 | 1 | |a Liu, Alex X |4 oth | |
| 700 | 1 | |a Wang, Wei |4 oth | |
| 700 | 1 | |a Shahzad, Muhammad |4 oth | |
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| 856 | 4 | 1 | |u http://dx.doi.org/10.1109/JSAC.2017.2680998 |3 Volltext |
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10.1109/JSAC.2017.2680998 doi PQ20170901 (DE-627)OLC1994911999 (DE-599)GBVOLC1994911999 (PRQ)i943-c372c0d0173aa824fd561135adf03c34b7593ed921addc12520ceeaf3b28e0480 (KEY)0128448720170000035000501175recognizingkeystrokesusingwifidevices DE-627 ger DE-627 rakwb eng 620 DE-600 53.00 bkl Ali, Kamran verfasserin aut Recognizing Keystrokes Using WiFi Devices 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. Shape Keystroke recognition Wireless fidelity human computer interaction (HCI) Activity recognition Privacy wireless sensing Wireless communication wireless security Receivers Keyboards Liu, Alex X oth Wang, Wei oth Shahzad, Muhammad oth Enthalten in IEEE journal on selected areas in communications New York, NY : IEEE, 1983 35(2017), 5, Seite 1175-1190 (DE-627)130399868 (DE-600)605072-4 (DE-576)015903427 0733-8716 nnns volume:35 year:2017 number:5 pages:1175-1190 http://dx.doi.org/10.1109/JSAC.2017.2680998 Volltext http://ieeexplore.ieee.org/document/7875144 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2061 53.00 AVZ AR 35 2017 5 1175-1190 |
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10.1109/JSAC.2017.2680998 doi PQ20170901 (DE-627)OLC1994911999 (DE-599)GBVOLC1994911999 (PRQ)i943-c372c0d0173aa824fd561135adf03c34b7593ed921addc12520ceeaf3b28e0480 (KEY)0128448720170000035000501175recognizingkeystrokesusingwifidevices DE-627 ger DE-627 rakwb eng 620 DE-600 53.00 bkl Ali, Kamran verfasserin aut Recognizing Keystrokes Using WiFi Devices 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. Shape Keystroke recognition Wireless fidelity human computer interaction (HCI) Activity recognition Privacy wireless sensing Wireless communication wireless security Receivers Keyboards Liu, Alex X oth Wang, Wei oth Shahzad, Muhammad oth Enthalten in IEEE journal on selected areas in communications New York, NY : IEEE, 1983 35(2017), 5, Seite 1175-1190 (DE-627)130399868 (DE-600)605072-4 (DE-576)015903427 0733-8716 nnns volume:35 year:2017 number:5 pages:1175-1190 http://dx.doi.org/10.1109/JSAC.2017.2680998 Volltext http://ieeexplore.ieee.org/document/7875144 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2061 53.00 AVZ AR 35 2017 5 1175-1190 |
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10.1109/JSAC.2017.2680998 doi PQ20170901 (DE-627)OLC1994911999 (DE-599)GBVOLC1994911999 (PRQ)i943-c372c0d0173aa824fd561135adf03c34b7593ed921addc12520ceeaf3b28e0480 (KEY)0128448720170000035000501175recognizingkeystrokesusingwifidevices DE-627 ger DE-627 rakwb eng 620 DE-600 53.00 bkl Ali, Kamran verfasserin aut Recognizing Keystrokes Using WiFi Devices 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. Shape Keystroke recognition Wireless fidelity human computer interaction (HCI) Activity recognition Privacy wireless sensing Wireless communication wireless security Receivers Keyboards Liu, Alex X oth Wang, Wei oth Shahzad, Muhammad oth Enthalten in IEEE journal on selected areas in communications New York, NY : IEEE, 1983 35(2017), 5, Seite 1175-1190 (DE-627)130399868 (DE-600)605072-4 (DE-576)015903427 0733-8716 nnns volume:35 year:2017 number:5 pages:1175-1190 http://dx.doi.org/10.1109/JSAC.2017.2680998 Volltext http://ieeexplore.ieee.org/document/7875144 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2061 53.00 AVZ AR 35 2017 5 1175-1190 |
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10.1109/JSAC.2017.2680998 doi PQ20170901 (DE-627)OLC1994911999 (DE-599)GBVOLC1994911999 (PRQ)i943-c372c0d0173aa824fd561135adf03c34b7593ed921addc12520ceeaf3b28e0480 (KEY)0128448720170000035000501175recognizingkeystrokesusingwifidevices DE-627 ger DE-627 rakwb eng 620 DE-600 53.00 bkl Ali, Kamran verfasserin aut Recognizing Keystrokes Using WiFi Devices 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. Shape Keystroke recognition Wireless fidelity human computer interaction (HCI) Activity recognition Privacy wireless sensing Wireless communication wireless security Receivers Keyboards Liu, Alex X oth Wang, Wei oth Shahzad, Muhammad oth Enthalten in IEEE journal on selected areas in communications New York, NY : IEEE, 1983 35(2017), 5, Seite 1175-1190 (DE-627)130399868 (DE-600)605072-4 (DE-576)015903427 0733-8716 nnns volume:35 year:2017 number:5 pages:1175-1190 http://dx.doi.org/10.1109/JSAC.2017.2680998 Volltext http://ieeexplore.ieee.org/document/7875144 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2061 53.00 AVZ AR 35 2017 5 1175-1190 |
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10.1109/JSAC.2017.2680998 doi PQ20170901 (DE-627)OLC1994911999 (DE-599)GBVOLC1994911999 (PRQ)i943-c372c0d0173aa824fd561135adf03c34b7593ed921addc12520ceeaf3b28e0480 (KEY)0128448720170000035000501175recognizingkeystrokesusingwifidevices DE-627 ger DE-627 rakwb eng 620 DE-600 53.00 bkl Ali, Kamran verfasserin aut Recognizing Keystrokes Using WiFi Devices 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. Shape Keystroke recognition Wireless fidelity human computer interaction (HCI) Activity recognition Privacy wireless sensing Wireless communication wireless security Receivers Keyboards Liu, Alex X oth Wang, Wei oth Shahzad, Muhammad oth Enthalten in IEEE journal on selected areas in communications New York, NY : IEEE, 1983 35(2017), 5, Seite 1175-1190 (DE-627)130399868 (DE-600)605072-4 (DE-576)015903427 0733-8716 nnns volume:35 year:2017 number:5 pages:1175-1190 http://dx.doi.org/10.1109/JSAC.2017.2680998 Volltext http://ieeexplore.ieee.org/document/7875144 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2061 53.00 AVZ AR 35 2017 5 1175-1190 |
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In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. 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Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. |
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Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. |
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Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy. |
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