Reliable Third-Party Library Detection in Android and its Security Applications

353 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Model-Reuse Attacks on Deep Learning Systems (2018, background): “Indeed, the risks of reusing external modules in software development have long been recognized by the security research communities [2, 6, 16].”
Backdoor attacks against learning systems (2017, background): “…risks of reusing external modules (e.g., libraries) in building software systems (e.g., [15], [16]), especially in Web and mobile applications [45], [11], [13], it is still challenging today even to reliably detect external modules [12], due to the ever-increasing system complexity and scale.”
On the security relevance of weights in deep learning. (2020, background): “The idea that trusting libraries can be recognized as a threat [9, 1, 18] has also been recognized in ML [23, 40].”
Edge Computing Security: State of the Art and Challenges (2019, background): “[133] developed a library detection technique that can resist the common code obfuscations and detect security vulnerabilities and malicious behaviors in Android libraries.”
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic (2020, background): “Studies have shown that apps are regularly updated with new versions, as frequently as once a month on average [11, 22].”
Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps (2014, background): “Recent work [9, 36, 38] o er approaches to detect third party libraries.”