|
|
|
|
|
Jeonggeun Jo, Jaeik Cho and Jongsub Moon
Artificial intelligence (AI) is increasingly being utilized in cybersecurity, particularly for detecting malicious applications. However, the black-box nature of AI models presents a significant challenge. This lack of transparency makes it difficult to ...
ver más
|
|
|
|
|
|
|
|
|
Mohammed N. AlJarrah, Qussai M. Yaseen and Ahmad M. Mustafa
The Android platform has become the most popular smartphone operating system, which makes it a target for malicious mobile apps. This paper proposes a machine learning-based approach for Android malware detection based on application features. Unlike man...
ver más
|
|
|
|
|
|
|
|
|
Vasileios Kouliaridis and Georgios Kambourakis
Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malwa...
ver más
|
|
|
|
|
|
|
|
|
Xin Su, Lijun Xiao, Wenjia Li, Xuchong Liu, Kuan-Ching Li and Wei Liang
Recently, security incidents such as sensitive data leakage and video/audio hardware control caused by Android malware have raised severe security issues that threaten Android users, so thus behavior analysis and detection research researches of maliciou...
ver más
|
|
|
|
|
|
|
|
|
Abikoye Oluwakemi Christianah,Benjamin Aruwa Gyunka,Akande Noah Oluwatobi
Pág. pp. 61 - 78
Android operating system has become very popular, with the highest market share, amongst all other mobile operating systems due to its open source nature and users friendliness. This has brought about an uncontrolled rise in malicious applications target...
ver más
|
|
|
|