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. 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. 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. Thus, we are the first to show that this sensor leaks sensitive information. Furthermore, we demonstrate 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 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 side channel seriously jeopardizes the security and privacy of mobile-device owners.
The main drawback of our attack is that users are not allowed to walk around while entering the PINs because the data is only exploitable for one specific environment. Hence, the data cannot be reused for multiple attacks as in case of Aviv al @cite_47 . Though, their results indicate a rather low success rate of 20 The work of Simon and Anderson @cite_31 additionally requires the permission which potentially gains the user's suspicion. In addition, their attack must deal with the problem of audio-visual feedback, , the shutter sound or the LED, while capturing the required data. Compared to their work we do not restrict our study to specific input methods as long as the user holds the device while operating it. Furthermore, they need to transfer image data to the server, which cannot be represented as compact as sensor values.
@cite_47 @cite_31
2150097065 110254507
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. 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.