You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
More and more people are regularly using mobile and battery-powered handsets, such as smartphones and tablets. At the same time, thanks to the technological innovation and to the high user demands, those devices are integrating extensive functionalities and developers are writing battery-draining apps, which results in a surge of energy consumption of these devices. This scenario leads many people to often look for opportunities to charge their devices at public charging stations: the presence of such stations is already prominent around public areas such as hotels, shopping malls, airports, gyms and museums, and is expected to significantly grow in the future. While most of the time the power comes for free, there is no guarantee that the charging station is not maliciously controlled by an adversary, with the intention to exfiltrate data from the devices that are connected to it. In this paper, we illustrate for the first time how an adversary could leverage a maliciously controlled charging station to exfiltrate data from the smartphone via a USB charging cable (i.e., without using the data transfer functionality), controlling a simple app running on the device, and without requiring any permission to be granted by the user to send data out of the device. We show the feasibility of the proposed attack through a prototype implementation in Android, which is able to send out potentially sensitive information, such as IMEI, contacts' phone number, and pictures.

Cited Abstract(s):
[1]: Investigating network covert channels in smartphones has become increasingly important as smartphones have recently replaced the role of traditional computers. Smartphones are subject to traditional computer network covert channel techniques. Smartphones also introduce new sets of covert channel techniques as they add more capabilities and multiple network connections. This work presents a new network covert channel in smartphones. The research studies the ability to leak information from the smartphones applications by reaching the cellular voice stream, and it examines the ability to employ the cellular voice channel to be a potential medium of information leakage through carrying modulated speech-like data covertly. To validate the theory, an Android software audio modem has been developed and it was able to leak data successfully through the cellular voice channel stream by carrying modulated data with a throughput of 13 bps with 0.018 BER. Moreover, Android security policies are investigated and broken in order to implement a user-mode rootkit that opens the voice channels by stealthily answering an incoming voice call. Multiple scenarios are conducted to verify the effectiveness of the proposed covert channel. This study identifies a new potential smartphone covert channel, and discusses some security vulnerabilities in Android OS that allow the use of this channel demonstrating the need to set countermeasures against this kind of breach.

[2]: Due to the rapid advancement of mobile communication technology, mobile devices nowadays can support a variety of data services that are not traditionally available. With the growing popularity of mobile devices in the last few years, attacks targeting them are also surging. Existing mobile malware detection techniques, which are often borrowed from solutions to Internet malware detection, do not perform as effectively due to the limited computing resources on mobile devices. In this paper, we propose VirusMeter, a novel and general malware detection method, to detect anomalous behaviors on mobile devices. The rationale underlying VirusMeter is the fact that mobile devices are usually battery powered and any malicious activity would inevitably consume some battery power. By monitoring power consumption on a mobile device, VirusMeter catches misbehaviors that lead to abnormal power consumption. For this purpose, VirusMeter relies on a concise user-centric power model that characterizes power consumption of common user behaviors. In a real-time mode, VirusMeter can perform fast malware detection with trivial runtime overhead. When the battery is charging (referred to as a battery-charging mode), VirusMeter applies more sophisticated machine learning techniques to further improve the detection accuracy. To demonstrate its feasibility and effectiveness, we have implemented a VirusMeter prototype on Nokia 5500 Sport and used it to evaluate some real cellphone malware, including FlexiSPY and Cabir. Our experimental results show that VirusMeter can effectively detect these malware activities with less than 1.5 additional power consumption in real time.
As demonstrated by [1] , the adversary is further empowered as smartphones continue to have more computational power and extensive functionalities. The authors empirically showed that speech-like data can be sent over a cellular voice channel. The attack was successfully carried out with the help of a custom-built rootkit installed on Android devices. [2] , demonstrated the feasibility of covertly exfiltrating data via SMS and inaudible audio transmission, without the user's knowledge, to other mobile devices including laptops.