A survey on deep learning for cybersecurity: Progress, challenges, and opportunities

M Macas, C Wu, W Fuertes - Computer Networks, 2022 - Elsevier
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Deep learning for android malware defenses: a systematic literature review

Y Liu, C Tantithamthavorn, L Li, Y Liu - ACM Computing Surveys, 2022 - dl.acm.org
Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

Unblind your apps: Predicting natural-language labels for mobile gui components by deep learning

J Chen, C Chen, Z **ng, X Xu, L Zhu, G Li… - Proceedings of the ACM …, 2020 - dl.acm.org
According to the World Health Organization (WHO), it is estimated that approximately 1.3
billion people live with some forms of vision impairment globally, of whom 36 million are …

A performance-sensitive malware detection system using deep learning on mobile devices

R Feng, S Chen, X **e, G Meng… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Currently, Android malware detection is mostly performed on server side against the
increasing number of malware. Powerful computing resource provides more exhaustive …

A survey of deep learning on mobile devices: Applications, optimizations, challenges, and research opportunities

T Zhao, Y **e, Y Wang, J Cheng, X Guo… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Deep learning (DL) has demonstrated great performance in various applications on
powerful computers and servers. Recently, with the advancement of more powerful mobile …

[HTML][HTML] DL-AMDet: Deep learning-based malware detector for android

AR Nasser, AM Hasan, AJ Humaidi - Intelligent Systems with Applications, 2024 - Elsevier
The Android operating system, with its market share leadership and open-source nature in
smartphones, has become the primary target of malware. However, detecting malicious …

Why an android app is classified as malware: Toward malware classification interpretation

B Wu, S Chen, C Gao, L Fan, Y Liu, W Wen… - ACM Transactions on …, 2021 - dl.acm.org
Machine learning–(ML) based approach is considered as one of the most promising
techniques for Android malware detection and has achieved high accuracy by leveraging …

Core: Automating review recommendation for code changes

JK Siow, C Gao, L Fan, S Chen… - 2020 IEEE 27th …, 2020 - ieeexplore.ieee.org
Code review is a common process that is used by developers, in which a reviewer provides
useful comments or points out defects in the submitted source code changes via pull …

IntDroid: Android malware detection based on API intimacy analysis

D Zou, Y Wu, S Yang, A Chauhan, W Yang… - ACM Transactions on …, 2021 - dl.acm.org
Android, the most popular mobile operating system, has attracted millions of users around
the world. Meanwhile, the number of new Android malware instances has grown …

An empirical assessment of security risks of global android banking apps

S Chen, L Fan, G Meng, T Su, M Xue, Y Xue… - Proceedings of the …, 2020 - dl.acm.org
Mobile banking apps, belonging to the most security-critical app category, render massive
and dynamic transactions susceptible to security risks. Given huge potential financial loss …