Biblio

The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a security analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

The current security mechanisms for Android apps, both static and dynamic analysis approaches, are insufficient for detection and prevention of the increasingly dynamic and sophisticated security attacks. Static analysis approaches suffer from false positives whereas dynamic analysis approaches suffer from false negatives. Moreover, they all lack the ability to efficiently analyze systems with incremental changes—such as adding/removing apps, granting/revoking permissions, and dynamic components’ communications. Each time the system changes, the entire analysis needs to be repeated, making the existing approaches inefficient for practical use. To mitigate their shortcomings, we have developed SALMA, a novel self-protecting Android software system that monitors itself and adapts its behavior at runtime to prevent a wide-range of security risks. SALMA maintains a precise architectural model, represented as a Multiple-Domain-Matrix, and incrementally and efficiently analyzes an Android system in response to incremental system changes. The maintained architecture is used to reason about the running Android system. Every time the system changes, SALMA determines (1) the impacted part of the system, and (2) the subset of the security analyses that need to be performed, thereby greatly improving the performance of the approach. Our experimental results on hundreds of real-world apps corroborate SALMA’s scalability and efficiency as well as its ability to detect and prevent security attacks at runtime with minimal disruption.