Covert channels can be used to circumvent system and network policies by establishing communications that have not been considered in the design of the computing system. We construct a covert channel between different computing systems that utilizes audio modulation demodulation to exchange data between the computing systems over the air medium. The underlying network stack is based on a communication system that was originally designed for robust underwater communication. We adapt the communication system to implement covert and stealthy communications by utilizing the ultrasonic frequency range. We further demonstrate how the scenario of covert acoustical communication over the air medium can be extended to multi-hop communications and even to wireless mesh networks. A covert acoustical mesh network can be conceived as a meshed botnet or malnet that is accessible via inaudible audio transmissions. Different applications of covert acoustical mesh networks are presented, including the use for remote keylogging over multiple hops. It is shown that the concept of a covert acoustical mesh network renders many conventional security concepts useless, as acoustical communications are usually not considered. Finally, countermeasures against covert acoustical mesh networks are discussed, including the use of lowpass filtering in computing systems and a host-based intrusion detection system for analyzing audio input and output in order to detect any irregularities.
For eavesdropping on a computer user's keystrokes by using a computers's physical emanations, several techniques have been proposed. Halevi and Saxena @cite_4 present a study on acoustical emanations from keyboards where the keyboard input is recovered from the sound of a keystroke. Raguram, White, Goswami, Monrose and Frahm @cite_15 discuss a setup where typed input is recovered from environmental reflections, while Frankland @cite_39 describes information leakage over keyboard LEDs that are manipulated to carry an optical signal. Moreover, Balzarotti, Cova and Vigna @cite_26 show that keystrokes can be automatically captured by recording the keyboard with a camera and analyzing the video stream. We also gather keystrokes from a computer user, but we record the keystrokes directly on the infected victim (i.e., the user's computer) where a malware prototype needs to be placed in preparation of an attack. This malware prototype contains both a keylogger software and a network stack for covert acoustic communication in order to spread the recorded keystrokes over the covert network.
@cite_15 @cite_26 @cite_4 @cite_39
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Computer keyboards are often used to transmit confidential data such as passwords. Since they contain electronic components, keyboards eventually emit electromagnetic waves. These emanations could reveal sensitive information such as keystrokes. The technique generally used to detect compromising emanations is based on a wide-band receiver, tuned on a specific frequency. However, this method may not be optimal since a significant amount of information is lost during the signal acquisition. Our approach is to acquire the raw signal directly from the antenna and to process the entire captured electromagnetic spectrum. Thanks to this method, we detected four different kinds of compromising electromagnetic emanations generated by wired and wireless keyboards. These emissions lead to a full or a partial recovery of the keystrokes. We implemented these sidechannel attacks and our best practical attack fully recovered 95 of the keystrokes of a PS 2 keyboard at a distance up to 20 meters, even through walls. We tested 12 different keyboard models bought between 2001 and 2008 (PS 2, USB, wireless and laptop). They are all vulnerable to at least one of the four attacks. We conclude that most of modern computer keyboards generate compromising emanations (mainly because of the manufacturer cost pressures in the design). Hence, they are not safe to transmit confidential information. Eavesdropping on electronic communication is usually prevented by using cryptography-based mechanisms. However, these mechanisms do not prevent one from obtaining private information through side channels, such as the electromagnetic emissions of monitors or the sound produced by keyboards. While extracting the same information by watching somebody typing on a keyboard might seem to be an easy task, it becomes extremely challenging if it has to be automated. However, an automated tool is needed in the case of long-lasting surveillance procedures or long user activity, as a human being is able to reconstruct only a few characters per minute. This paper presents a novel approach to automatically recovering the text being typed on a keyboard, based solely on a video of the user typing. As part of the approach, we developed a number of novel techniques for motion tracking, sentence reconstruction, and error correction. The approach has been implemented in a tool, called ClearShot, which has been tested in a number of realistic settings where it was able to reconstruct a substantial part of the typed information. Attacks that use side channels, such as sound and electromagnetic emanation, to infer keystrokes on physical keyboards are ineffective on smartphones without physical keyboards. We describe a new side channel, motion, on touch screen smartphones with only soft keyboards. Since typing on different locations on the screen causes different vibrations, motion data can be used to infer the keys being typed. To demonstrate this attack, we developed TouchLogger, an Android application that extracts features from device orientation data to infer keystrokes. TouchLogger correctly inferred more than 70 of the keys typed on a number-only soft keyboard on a smartphone. We hope to raise the awareness of motion as a significant side channel that may leak confidential data. In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.