When machine learning meets privacy in 6G: A survey
The rapid-develo** Artificial Intelligence (AI) technology, fast-growing network traffic, and
emerging intelligent applications (eg, autonomous driving, virtual reality, etc.) urgently …
emerging intelligent applications (eg, autonomous driving, virtual reality, etc.) urgently …
Malicious application detection in android—a systematic literature review
Context: In last decade, due to tremendous usage of smart phones it seems that these
gadgets became an essential necessity of day-to-day life. People are using new …
gadgets became an essential necessity of day-to-day life. People are using new …
[HTML][HTML] DL-Droid: Deep learning based android malware detection using real devices
The Android operating system has been the most popular for smartphones and tablets since
2012. This popularity has led to a rapid raise of Android malware in recent years. The …
2012. This popularity has led to a rapid raise of Android malware in recent years. The …
Android HIV: A study of repackaging malware for evading machine-learning detection
Machine learning-based solutions have been successfully employed for the automatic
detection of malware on Android. However, machine learning models lack robustness to …
detection of malware on Android. However, machine learning models lack robustness to …
Android malware familial classification and representative sample selection via frequent subgraph analysis
The rapid increase in the number of Android malware poses great challenges to anti-
malware systems, because the sheer number of malware samples overwhelms malware …
malware systems, because the sheer number of malware samples overwhelms malware …
Droidfusion: A novel multilevel classifier fusion approach for android malware detection
Android malware has continued to grow in volume and complexity posing significant threats
to the security of mobile devices and the services they enable. This has prompted increasing …
to the security of mobile devices and the services they enable. This has prompted increasing …
A multimodal malware detection technique for Android IoT devices using various features
Internet of things (IoT) is revolutionizing this world with its evolving applications in various
aspects of life such as sensing, healthcare, remote monitoring, and so on. Android devices …
aspects of life such as sensing, healthcare, remote monitoring, and so on. Android devices …
Deep learning for effective Android malware detection using API call graph embeddings
A Pektaş, T Acarman - Soft Computing, 2020 - Springer
High penetration of Android applications along with their malicious variants requires efficient
and effective malware detection methods to build mobile platform security. API call …
and effective malware detection methods to build mobile platform security. API call …
SEDMDroid: An enhanced stacking ensemble framework for Android malware detection
The popularity of the Android platform in smartphones and other Internet-of-Things devices
has resulted in the explosive of malware attacks against it. Malware presents a serious …
has resulted in the explosive of malware attacks against it. Malware presents a serious …
Learning features from enhanced function call graphs for Android malware detection
Analyzing the runtime behaviors of Android apps is crucial for malware detection. In this
paper, we attempt to learn the behavior level features of an app from function calls. The …
paper, we attempt to learn the behavior level features of an app from function calls. The …