Understanding Android App Piggybacking:A Systematic Study of Malicious Code Grafting

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has produced approaches and tools to identify piggybacked apps, the literature lacks a comprehensive investigation into such phenomenon. We fill this gap by 1) systematically building a large set of piggybacked and benign apps pairs, which we release to the community, 2) empirically studying the characteristics of malicious piggybacked apps in comparison with their benign counterparts, and 3) providing insights on piggybacking processes. Among several findings providing insights, analysis techniques should build upon to improve the overall detection and classification accuracy of piggybacked apps, we show that piggybacking operations not only concern app code but also extensively manipulates app resource files, largely contradicting common beliefs. We also find that piggybacking is done with little sophistication, in many cases automatically, and often via library code

Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned

Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malware has evolved over time, focusing on repackaging malware. In this type of threats different innocuous apps are piggybacked with a malicious payload (rider), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of malicious activity performed by the malicious samples and in the level of obfuscation used by malware to avoid detection. We then show that our framework can aid analysts who attempt to study unknown malware families. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community.Accepted manuscrip

A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall

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

Survey And New Approach In Service Discovery And Advertisement For Mobile Ad Hoc Networks.

Service advertisement and discovery is an important component for mobile adhoc communications and collaboration in ubiquitous computing environments. The ability to discover services offered in a mobile adhoc network is the major prerequisite for effective usability of these networks. This paper aims to classify and compare existing Service Discovery (SD) protocols for MANETs by grouping them based on their SD strategies and service information accumulation strategies, and to propose an efficient approach for addressing the inherent issues

Exploitation and Detection of a Malicious Mobile Application

Mobile devices are increasingly being embraced by both organizations and individuals in today’s society. Specifically, Android devices have been the prominent mobile device OS for several years. This continued amalgamation creates an environment that is an attractive attack target. The heightened integration of these devices prompts an investigation into the viability of maintaining non-compromised devices. Hence, this research presents a preliminary investigation into the effectiveness of current commercial anti-virus, static code analysis and dynamic code analysis engines in detecting unknown repackaged malware piggybacking on popular applications with excessive permissions. The contribution of this paper is two-fold. First, it provides an initial assessment of the effectiveness of anti-virus and analysis tools in detecting malicious applications and behavior in Android devices. Secondly, it provides process for inserting code injection attacks to stimulate a zero-day repackaged malware that can be used in future research efforts