A review of android malware detection approaches based on machine learning
K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are developing rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …
malware is also emerging in an endless stream. Many researchers have studied the …
Machine learning techniques applied to cybersecurity
J Martínez Torres, C Iglesias Comesaña… - International Journal of …, 2019 - Springer
Abstract Machine learning techniques are a set of mathematical models to solve high non-
linearity problems of different topics: prediction, classification, data association, data …
linearity problems of different topics: prediction, classification, data association, data …
A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
Generating adversarial malware examples for black-box attacks based on GAN
W Hu, Y Tan - International Conference on Data Mining and Big Data, 2022 - Springer
Abstract Machine learning has been used to detect new malware in recent years, while
malware authors have strong motivation to attack such algorithms. Malware authors usually …
malware authors have strong motivation to attack such algorithms. Malware authors usually …
Machine learning aided Android malware classification
N Milosevic, A Dehghantanha, KKR Choo - Computers & Electrical …, 2017 - Elsevier
The widespread adoption of Android devices and their capability to access significant
private and confidential information have resulted in these devices being targeted by …
private and confidential information have resulted in these devices being targeted by …
Motivating the rules of the game for adversarial example research
Advances in machine learning have led to broad deployment of systems with impressive
performance on important problems. Nonetheless, these systems can be induced to make …
performance on important problems. Nonetheless, these systems can be induced to make …
Malware detection on highly imbalanced data through sequence modeling
We explore the task of Android malware detection based on dynamic analysis of application
activity sequences using deep learning techniques. We show that analyzing a sequence of …
activity sequences using deep learning techniques. We show that analyzing a sequence of …
Security threat mitigation for smart contracts: A comprehensive survey
The blockchain technology, initially created for cryptocurrency, has been re-purposed for
recording state transitions of smart contracts—decentralized applications that can be …
recording state transitions of smart contracts—decentralized applications that can be …
Marvin: Efficient and comprehensive mobile app classification through static and dynamic analysis
M Lindorfer, M Neugschwandtner… - 2015 IEEE 39th annual …, 2015 - ieeexplore.ieee.org
Android dominates the smartphone operating system market and consequently has attracted
the attention of malware authors and researchers alike. Despite the considerable number of …
the attention of malware authors and researchers alike. Despite the considerable number of …
Deep learning for android malware defenses: a systematic literature review
Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …
threat to developers and end-users. Numerous research efforts have been devoted to …