AdDroid: Rule-Based Machine Learning Framework for Android Malware Analysis
Sign Up to like & getrecommendations! Published in 2020 at "Mobile Networks and Applications"
DOI: 10.1007/s11036-019-01248-0
Abstract: Recent years have witnessed huge growth in Android malware development. Colossal reliance on Android applications for day to day working and their massive development dictates for an automated mechanism to distinguish malicious applications from benign… read more here.
Keywords: android; android malware; based machine; machine learning ... See more keywords
IoT-based green city architecture using secured and sustainable android services
Sign Up to like & getrecommendations! Published in 2020 at "Environmental Technology and Innovation"
DOI: 10.1016/j.eti.2020.101091
Abstract: Abstract Green and smart cities deliver services to their residents using mobile applications that make daily life more convenient. The privacy and security of these applications are significant in providing sustainable services in a green… read more here.
Keywords: system; city; android; city architecture ... See more keywords
Graph-augmented multi-modal learning framework for robust android malware detection
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-22169-x
Abstract: The widespread adoption of Android has made it a primary target for increasingly sophisticated malware, posing a significant challenge to mobile security. Traditional static or behavioural approaches often struggle with obfuscation and lack contextual integration… read more here.
Keywords: malware; modal learning; git guardnet; android ... See more keywords
A Review of Android Malware Detection Approaches Based on Machine Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3006143
Abstract: Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and… read more here.
Keywords: android malware; android; machine learning; detection ... See more keywords
Performance-Oriented and Sustainability-Oriented Design of an Effective Android Malware Detector
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3486094
Abstract: Effective Android malware detection is a complex problem because of the rapidly-evolving, complicated, and diverse nature of malware. The design of malware detectors should prioritise high detection rate, efficient use of computational resources, and sustainability.… read more here.
Keywords: malware; effective android; android malware; android ... See more keywords
A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Consumer Electronics"
DOI: 10.1109/tce.2025.3577905
Abstract: Federated Learning (FL) is gaining traction in Android-based consumer electronics, enabling collaborative model training across decentralized devices while preserving data privacy. However, the increasing adoption of FL in these devices exposes them to adversarial attacks… read more here.
Keywords: attack; genetic algorithm; android; android based ... See more keywords
An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing
Sign Up to like & getrecommendations! Published in 2020 at "Computational and Mathematical Methods in Medicine"
DOI: 10.1155/2020/3658795
Abstract: Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary. The Android platform has developed rapidly because of its good user experience… read more here.
Keywords: machine; methodology; android; android malware ... See more keywords
MULBER: Effective Android Malware Clustering Using Evolutionary Feature Selection and Mahalanobis Distance Metric
Sign Up to like & getrecommendations! Published in 2022 at "Symmetry"
DOI: 10.3390/sym14102221
Abstract: Symmetric and asymmetric patterns are fascinating phenomena that show a level of co-existence in mobile application behavior analyses. For example, static phenomena, such as information sharing through collaboration with known apps, is a good example… read more here.
Keywords: mulber; android; mahalanobis distance; malware ... See more keywords