Context-free Attacks Using Keyboard Acoustic Emanations

Abstract

The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt contextbased approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2% of keystrokes can be successfully recovered.

Keystroke Recognition Using WiFi Signals

“…Other than the SDRs based keystroke tracking approach proposed in [21] which uses wireless signals to track keystrokes, researchers have proposed several keystrokes recognition schemes that are based on other sensing modalities such as acoustics [1][2][3]22], electromagnetic emissions [4], and video cameras [5]. Next, we give a brief overview of the other existing schemes that utilize these sensing modalities to recognize keystrokes.…”

“…They used cepstrum features [22] instead of FFT as keystroke features and used unsupervised learning with language model correction on the collected features before using them for supervised training and recognition of different keystrokes. Zhu et al proposed a context-free geometry-based approach for recognizing keystrokes that leverage the acoustic emanations from keystrokes to first calculate the time difference of keystroke arrival and then estimate the physical locations of the keystrokes to identify which keys are pressed [3].…”

“…ways to recognize keystrokes, which can be classified into three categories: acoustic emission based approaches, electromagnetic emission based approaches, and vision based approaches. Acoustic emission based approaches recognize keystrokes based on either the observation that different keys in a keyboard produce different typing sounds [1,2] or the observation that the acoustic emanations from different keys arrive at different surrounding smartphones at different time as the keys are located at different places in a keyboard [3]. Electromagnetic emission based approaches recognize keystrokes based on the observation that the electromagnetic emanations from the electrical circuit underneath different keys in a keyboard are different [4].…”

PatternListener

“…However, the proposed approach can only achieve 73% accuracy of inferring pattern and 43% accuracy of inferring PIN with only 50 PINs and 50 patterns. Acoustic Attacks and Tracking: Keystroke recognition based on the acoustic emanation has been studied in [8,13,19,30,38,39]. These approaches leverage the observation that the sound of keystrokes differs slightly from key to key or use time-difference of arrival measurements to identify multiple strokes of the same physical key.…”

Security and privacy on physical layer for wireless sensing: A survey

“…It will do great harm to personal security and privacy if the content user's input is stolen. Except attacking the user's computer to get the information, criminals can also use acoustic ways, (35)(36)(37) electromagnetic emission 38 or the vision way. 39 WiKey 9 is a system which has the ability of identifying user's keystroke remotely only using Wi-Fi signals.…”