This paper presents a privacy-preserving federated learning (PPFL) system for the detection
of android malware. The proposed PPFL allows mobile devices to collaborate together for …

As Android is a popular a mobile operating system, Android malware is on the rise, which
poses a great threat to user privacy and security. Considering the poor detection effects of …

An android application requires specific permissions from the user to access the system
resources and perform required functionalities. Recently, the android market has …

To relieve the burden of security analysts, Android malware detection and its family
classification need to be automated. There are many previous works focusing on using …

The security of crypto wallets is a major concern in light of the recent prevalence of thefts.
Aiming at the problem that there is no complete and reliable security detection model for …

Adware is the most common type of malware. While considered not harmful in nature, it
disrupts the user experience and generates unwanted revenue. Adware is also difficult to …

With the dramatic growth in smartphone usage, the number of new malicious mobile
applications has increased rapidly. Identifying malicious applications in large-scale datasets …

Nowadays, the security exploitations against online systems and mobile applications (apps)
are increasing tremendously. Due to the new norm, most of the meetings were conducted …

The advent of smart homes has revolutionized residential living, integrating advanced
technologies and intelligent devices to create secure, comfortable, and efficient …

This work proposes a method for detecting Android malware by leveraging static
permissions and machine learning algorithms. A dataset of 398 Android applications was …