As a warm-blooded mammalian species, we humans routinely leave thermal residues on various objects with which we come in contact. This includes common input devices, such as keyboards, that are used for entering (among other things) secret information, such as passwords and PINs. Although thermal residue dissipates over time, there is always a certain time window during which thermal energy readings can be harvested from input devices to recover recently entered, and potentially sensitive, information. To-date, there has been no systematic investigation of thermal profiles of keyboards, and thus no efforts have been made to secure them. This serves as our main motivation for constructing a means for password harvesting from keyboard thermal emanations. Specifically, we introduce Thermanator, a new post factum insider attack based on heat transfer caused by a user typing a password on a typical external keyboard. We conduct and describe a user study that collected thermal residues from 30 users entering 10 unique passwords (both weak and strong) on 4 popular commodity keyboards. Results show that entire sets of key-presses can be recovered by non-expert users as late as 30 seconds after initial password entry, while partial sets can be recovered as late as 1 minute after entry. Furthermore, we find that Hunt-and-Peck typists are particularly vulnerable. We also discuss some Thermanator mitigation strategies. The main take-away of this work is three-fold: (1) using external keyboards to enter (already much-maligned) passwords is even less secure than previously recognized, (2) post factum (planned or impromptu) thermal imaging attacks are realistic, and finally (3) perhaps it is time to either stop using keyboards for password entry, or abandon passwords altogether.
@cite_15 investigated the influence of material composition (met al vs. plastic) and camera distance (14 vs. 28 inches) on PIN recovery, using a US @math 17,950 @math 14 @math 80 @math 40 @math US @math , attachable to a smartphone) to recover 4-digit PINs entered into rubber keypads. Analysis shows that the camera's distance from the keypad is important: from @math and @math centimeters, @math and @math digits can be identified, respectively up to @math seconds after entry.
@cite_15
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In this paper, we examine the potential of using a thermal camera to recover codes typed into keypads in a variety of scenarios. This attack has the advantage over using a conventional camera that the codes do not need to be captured while they are being typed and can instead be recovered for a short period afterwards. To get the broadest sense of how effective such an attack might be, we consider a number of variables: the material of the keypad, the user entering the code, the distance from the camera to the keypad, and the possible methods used to analyze the data. First, we present code recovery results from human review of our test data set; this provides us with a baseline for the overall effectiveness of thermal camera-based attacks. Second, using techniques from computer vision we automatically extract the code from raw camera data, thus demonstrating that this attack has the potential to scale well in practice. As we will see, both human and automated attacks are by and large successful in recovering the keys present in the code, even a full minute after they have been pressed; both methods are also able to determine the exact code (i.e., including the order in which the keys were pressed) for a smaller fraction of codes. Even without ordering, however, the search space of possible keys is still vastly reduced by knowing the keys pressed; for example, the search space is reduced from 10,000 possible codes to approximately 24 for a 4-digit code. In large-scale attacks involving many unique codes, such as on ATM PINs, our success rate indicates that an adversary can correctly recover enough codes to make such an attack economically viable. Today's smartphones provide services and uses that required a panoply of dedicated devices not so long ago. With them, we listen to music, play games or chat with our friends; but we also read our corporate email and documents, manage our online banking; and we have started to use them directly as a means of payment. In this paper, we aim to raise awareness of side-channel attacks even when strong isolation protects sensitive applications. Previous works have studied the use of the phone accelerometer and gyroscope as side channel data to infer PINs. Here, we describe a new side-channel attack that makes use of the video camera and microphone to infer PINs entered on a number-only soft keyboard on a smartphone. The microphone is used to detect touch events, while the camera is used to estimate the smartphone's orientation, and correlate it to the position of the digit tapped by the user. We present the design, implementation and early evaluation of PIN Skimmer, which has a mobile application and a server component. The mobile application collects touch-event orientation patterns and later uses learnt patterns to infer PINs entered in a sensitive application. When selecting from a test set of 50 4-digit PINs, PIN Skimmer correctly infers more than 30 of PINs after 2 attempts, and more than 50 of PINs after 5 attempts on android-powered Nexus S and Galaxy S3 phones. When selecting from a set of 200 8-digit PINs, PIN Skimmer correctly infers about 45 of the PINs after 5 attempts and 60 after 10 attempts. It turns out to be difficult to prevent such side-channel attacks, so we provide guidelines for developers to mitigate present and future side-channel attacks on PIN input. Recent researches have shown that motion sensors may be used as a side channel to infer keystrokes on the touchscreen of smartphones. However, the practicality of this attack is unclear. For example, does this attack work on different devices, screen dimensions, keyboard layouts, or keyboard types? Does this attack depend on specific users or is it user independent? To answer these questions, we conducted a user study where 21 participants typed a total of 47,814 keystrokes on four different mobile devices in six settings. Our results show that this attack remains effective even though the accuracy is affected by user habits, device dimension, screen orientation, and keyboard layout. On a number-only keyboard, after the attacker tries 81 4-digit PINs, the probability that she has guessed the correct PIN is 65 , which improves the accuracy rate of random guessing by 81 times. Our study also indicates that inference based on the gyroscope is more accurate than that based on the accelerometer. We evaluated two classification techniques in our prototype and found that they are similarly effective. The pervasive usage of mobile devices, i.e., smartphones and tablet computers, and their vast amount of sensors represent a plethora of side channels posing a serious threat to the user's privacy and security. In this paper, we propose a new type of side channel which is based on the ambient-light sensor employed in today's mobile devices. While recent advances in this area of research focused on the employed motion sensors and the camera as well as the sound, we investigate a less obvious source of information leakage, namely the ambient light. We successfully demonstrate that minor tilts and turns of mobile devices cause variations of the ambient-light sensor information. Furthermore, we show that these variations leak enough information to infer a user's personal identification number (PIN) input based on a set of known PINs. Our results clearly show that we are able to determine the correct PIN---out of a set of 50 random PINs---within the first ten guesses about 80 of the time. In contrast, the chance of finding the right PIN by randomly guessing ten PINs would be 20 . Since the data required to perform such an attack can be gathered without any specific permissions or privileges, the presented attack seriously jeopardizes the security and privacy of mobile-device owners.