Abstract-Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices.In this paper, we propose a new categorization system for sidechannel attacks, which is necessary as side-channel attacks have evolved significantly since their scientific investigations during the smart card era in the 1990s. Our proposed classification system allows to analyze side-channel attacks systematically, and facilitates the development of novel countermeasures. Besides this new categorization system, the extensive survey of existing attacks and attack strategies provides valuable insights into the evolving field of side-channel attacks, especially when focusing on mobile devices. We conclude by discussing open issues and challenges in this context and outline possible future research directions.

Android application uses permission system to regulate the access to system resources and users' privacy-relevant information. Existing work have demonstrated several techniques to study the required permissions declared by the developers, but few attention has been paid for used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. In addition, we used a benchmark malware dataset and collected a set of 1227 clean applications to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns.

More and more people are regularly using mobile and batterypowered 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.

No abstract

The Internet of Things (IoT) is a ubiquitous system that incorporates not only the current Internet of computers, but also smart objects and sensors. IoT technologies often rely on centralised architectures that follow the current business models. This makes e cient data collection and processing possible, which can be beneficial from a business perspective, but has many ramifications for users privacy. As communication within the IoT happens among many devices from various contexts, they need to authenticate each other to know that they talk to the intended party. Authentication, typically including identification, is the proof of identity information. However, transactions linked to the same identifier are traceable, and ultimately make people also traceable, hence their privacy is threatened. We propose a framework to counter this problem. We argue that applying attribute-based (AB) authentication in the context of IoT empowers users to maintain control over what data their devices disclose. At the same time AB authentication provides the possibility of data minimisation and unlinkability of user transactions. Therefore, this approach improves substantially user privacy in the IoT.