Results 91 to 100 of about 4,196 (220)

AEDroid: Adaptive Enhanced Android Malware Detection‐Based on Interpretability of Deep Learning

IET Information Security, Volume 2025, Issue 1, 2025.
As the most widely used operating system in the world, Android has naturally become the main target of malicious hackers. The current research on Android malware detection relies on manually defined sensitive API feature sets. With the continuous innovation and change of malicious behavior, new threats and attack methods have emerged.
Pengfei Liu, Guangquan Xu, Jian Sun, Wenxia Wang, Jie Chen, Peican Zhu   +5 more
wiley   +1 more source

AMALGAN: Image‐Based Android Malware Classification Using Generative Adversarial Network

The Journal of Engineering
The Android malware detection process requires analysing numerous files to ensure system security. Malware can also be embedded in media files and images.
Zahid Hussain Qaisar, Muhammad Faheem, Muhammad Waqar Ashraf   +2 more
doaj   +1 more source

Clustering android malware families by Http traffic

, 2015
Due to its popularity and open-source nature, Android is the mobile platform that has been targeted the most by malware that aim to steal personal information or to control the users??? devices. More specifically, mobile botnets are malware that allow an
Davide Maiorca, MAIORCA, DAVIDE, Marco Aresu, Giorgio Giacinto, ARESU, MARCO, ARIU, DAVIDE, Davide Ariu, GIACINTO, GIORGIO, Mansour Ahmadi, AHMADI, MANSOUR   +9 more
core   +1 more source

API Sequences based Malware Detection for Android

, 2015
To mitigate security problem brought by Android malware, various work has been proposed such as behavior based malware detection and data mining based malware detection.
Zhong Chen, Zhi Guan, Jiawei Zhu, Wu, Zhengang, Zhengang Wu, Chen, Zhong, Guan, Zhi, Zhu, Jiawei   +7 more
core   +1 more source

CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES

Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ
Android malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking.
abdullah alsraratee, Ahmed Al-Azawei
doaj   +1 more source

Evaluation of Ensemble Learning for Android Malware Family Identification

, 2020
Every Android malware sample generally belongs to a specific family that performs a similar set of actions and characteristics. Having the ability to effectively identify Android malware families can assist in addressing the damage caused by malware ...
Wylie, Jordan, Al-Dubai, Ahmed, Wang, Jianzhen, Tan, Zhiyuan   +3 more
core  

Challenges in Android Malware Analysis.

ERCIM News, 2016
The best protection against malware is to execute it: a security paradox.
Viet Triem Tong, Valérie, Lalande, Jean-François, Leslous, Mourad   +2 more
openaire   +2 more sources

Android Malware Detection

International Journal of Innovative Research in Advanced Engineering
Machine learning based detection system of Android malware and analysis of features which are static. The deigned system extracts non executable features such as permissions, intents, activities, and API calls from Android APK files. We analyze non-executable APK features and classify them using a Random Forest model deployed on a Flask server.
Aaditya Vikram Saravana Bhavan, Srujana Golla, Yuktha Poral, Alan S. Paul, Prasad B. Honnavalli, S. Supreetha   +5 more
openaire   +2 more sources

HTTP behavior characteristics generation and extraction approach for Android malware

Dianxin kexue, 2016
Growing of Android malware，not only seriously endangered the security of the Android market，but also brings challenges for detection.A generation and extraction approach of automatic Android malware behavioral signatures was proposed based on HTTP ...
Yaling LUO, Wenwei LI, Xin SU
doaj   +2 more sources

A Malware Detection System For Android

, 2015
Android security is built upon a permission-based mechanism, which restricts access of third-party Android applications to critical resources on an Android device.
Tchakounté, Franklin
core