A Longitudinal Study of Cryptographic API: a Decade of Android Malware
May 11, 2022 Β· Declared Dead Β· π International Conference on Security and Cryptography
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Adam Janovsky, Davide Maiorca, Dominik Macko, Vashek Matyas, Giorgio Giacinto
arXiv ID
2205.05573
Category
cs.CR: Cryptography & Security
Citations
3
Venue
International Conference on Security and Cryptography
Last Checked
4 months ago
Abstract
Cryptography has been extensively used in Android applications to guarantee secure communications, conceal critical data from reverse engineering, or ensure mobile users' privacy. Various system-based and third-party libraries for Android provide cryptographic functionalities, and previous works mainly explored the misuse of cryptographic API in benign applications. However, the role of cryptographic API has not yet been explored in Android malware. This paper performs a comprehensive, longitudinal analysis of cryptographic API in Android malware. In particular, we analyzed $603\,937$ Android applications (half of them malicious, half benign) released between $2012$ and $2020$, gathering more than 1 million cryptographic API expressions. Our results reveal intriguing trends and insights on how and why cryptography is employed in Android malware. For instance, we point out the widespread use of weak hash functions and the late transition from insecure DES to AES. Additionally, we show that cryptography-related characteristics can help to improve the performance of learning-based systems in detecting malicious applications.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted