Understanding Android App Piggybacking

The Android packaging model offers adequate opportunities for attackers to inject malicious code into popular benign apps, attempting to develop new malicious apps that can then be easily spread to a large user base. Despite the fact that the literature has already presented a number of tools to detect piggybacked apps, there is still lacking a comprehensive investigation on the piggybacking processes. To fill this gap, in this work, we collect a large set of benign/piggybacked app pairs that can be taken as benchmark apps for further investigation. We manually look into these benchmark pairs for understanding the characteristics of piggybacking apps and eventually we report 20 interesting findings. We expect these findings to initiate new research directions such as practical and scalable piggybacked app detection, explainable malware detection, and malicious code location

SimiDroid: Identifying and Explaining Similarities in Android Apps

App updates and repackaging are recurrent in the Android ecosystem, filling markets with similar apps that must be identified and analysed to accelerate user adoption, improve development efforts, and prevent malware spreading. Despite the existence of several approaches to improve the scalability of detecting repackaged/cloned apps, researchers and practitioners are eventually faced with the need for a comprehensive pairwise comparison to understand and validate the similarities among apps. This paper describes the design of SimiDroid, a framework for multi-level comparison of Android apps. SimiDroid is built with the aim to support the understanding of similarities/changes among app versions and among repackaged apps. In particular, we demonstrate the need and usefulness of such a framework based on different case studies implementing different analysing scenarios for revealing various insights on how repackaged apps are built. We further show that the similarity comparison plugins implemented in SimiDroid yield more accurate results than the state-of-the-art

Leveraging the Use of API Call Traces for Mobile Security

The growing popularity of Android applications has generated increased concerns over the danger of piracy and the spread of malware. A popular way to distribute malware in the mobile world is through the repackaging of legitimate apps. This process consists of downloading, unpacking, manipulating, recompiling an application, and publishing it again in an app store. In this thesis, we conduct an empirical study of over 15,000 apps to gain insights into the factors that drive the spread of repackaged apps. We also examine the motivations of developers who publish repackaged apps and those of users who download them, as well as the factors that determine which apps are chosen for repackaging, and the ways in which the apps are modified during the repackaging process. We have also studied android applications structure to investigate the locations where malicious code are more probable to be embedded into legitimate applications. We observed that service components contain key characteristics that entice attackers to misuse them. Therefore, we have focus on studying the behavior of malicious and benign services. Whereas benign services tend to inform the user of the background operations, malicious services tend to do long running operations and have a loose connection with rest of the code. These findings lead us to propose an approach to detect malware by studying the services’ behavior. To model the services’ behavior, we used API calls as feature sets. We proposed a hybrid approach using static and dynamic analysis to extract the API calls through the service lifecycle. Finally, we used the list of API calls preponderantly present in both malware as well as benign services as the feature set. We applied machine learning algorithms to use the feature set to classify malicious services and benign services

Security Issues of Mobile and Smart Wearable Devices

Mobile and smart devices (ranging from popular smartphones and tablets to wearable fitness trackers equipped with sensing, computing and networking capabilities) have proliferated lately and redefined the way users carry out their day-to-day activities. These devices bring immense benefits to society and boast improved quality of life for users. As mobile and smart technologies become increasingly ubiquitous, the security of these devices becomes more urgent, and users should take precautions to keep their personal information secure. Privacy has also been called into question as so many of mobile and smart devices collect, process huge quantities of data, and store them on the cloud as a matter of fact. Ensuring confidentiality, integrity, and authenticity of the information is a cybersecurity challenge with no easy solution. Unfortunately, current security controls have not kept pace with the risks posed by mobile and smart devices, and have proven patently insufficient so far. Thwarting attacks is also a thriving research area with a substantial amount of still unsolved problems. The pervasiveness of smart devices, the growing attack vectors, and the current lack of security call for an effective and efficient way of protecting mobile and smart devices. This thesis deals with the security problems of mobile and smart devices, providing specific methods for improving current security solutions. Our contributions are grouped into two related areas which present natural intersections and corresponds to the two central parts of this document: (1) Tackling Mobile Malware, and (2) Security Analysis on Wearable and Smart Devices. In the first part of this thesis, we study methods and techniques to assist security analysts to tackle mobile malware and automate the identification of malicious applications. We provide threefold contributions in tackling mobile malware: First, we introduce a Secure Message Delivery (SMD) protocol for Device-to-Device (D2D) networks, with primary objective of choosing the most secure path to deliver a message from a sender to a destination in a multi-hop D2D network. Second, we illustrate a survey to investigate concrete and relevant questions concerning Android code obfuscation and protection techniques, where the purpose is to review code obfuscation and code protection practices. We evaluate efficacy of existing code de-obfuscation tools to tackle obfuscated Android malware (which provide attackers with the ability to evade detection mechanisms). Finally, we propose a Machine Learning-based detection framework to hunt malicious Android apps by introducing a system to detect and classify newly-discovered malware through analyzing applications. The proposed system classifies different types of malware from each other and helps to better understanding how malware can infect devices, the threat level they pose and how to protect against them. Our designed system leverages more complete coverage of apps’ behavioral characteristics than the state-of-the-art, integrates the most performant classifier, and utilizes the robustness of extracted features. The second part of this dissertation conducts an in-depth security analysis of the most popular wearable fitness trackers on the market. Our contributions are grouped into four central parts in this domain: First, we analyze the primitives governing the communication between fitness tracker and cloud-based services. In addition, we investigate communication requirements in this setting such as: (i) Data Confidentiality, (ii) Data Integrity, and (iii) Data Authenticity. Second, we show real-world demos on how modern wearable devices are vulnerable to false data injection attacks. Also, we document successful injection of falsified data to cloud-based services that appears legitimate to the cloud to obtain personal benefits. Third, we circumvent End-to-End protocol encryption implemented in the most advanced and secure fitness trackers (e.g., Fitbit, as the market leader) through Hardware-based reverse engineering. Last but not least, we provide guidelines for avoiding similar vulnerabilities in future system designs