Results 71 to 80 of about 3,123 (222)

Novel Multi-Classification Dynamic Detection Model for Android Malware Based on Improved Zebra Optimization Algorithm and LightGBM

Sensors
With the increasing popularity of Android smartphones, malware targeting the Android platform is showing explosive growth. Currently, mainstream detection methods use static analysis methods to extract features of the software and apply machine learning ...
Shuncheng Zhou, Honghui Li, Xueliang Fu, Daoqi Han, Xin He   +4 more
doaj   +1 more source

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

Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection [PDF]

, 2022
Niall McLaughlin
openalex   +1 more source

Backdoor Attack and Defense Methods for AI–Based IoT Intrusion Detection System

IET Information Security, Volume 2025, Issue 1, 2025.
The Internet of Things (IoT) is an emerging technology that has attracted significant attention and triggered a technical revolution in recent years. Numerous IoT devices are directly connected to the physical world, such as security cameras and medical equipment, making IoT security a critical issue.
Bowen Ma, Jiangwei Shi, Ning Zhu, Chen Fang, Yongjin Hu, Bohui Wang   +5 more
wiley   +1 more source

GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection

IEEE Access
The sophistication of Android malware poses significant threats to user security and privacy. Traditional detection methods struggle with rapid malware evolution and benign application diversity, leading to high false positive rates and limited ...
Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki   +3 more
doaj   +1 more source

Malware Detection in Android Applications

, 2019
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016.
Mr. Tushar Patil, Prof. Bharti Dhote
openaire   +1 more source

Feature Graph Construction With Static Features for Malware Detection

IET Information Security, Volume 2025, Issue 1, 2025.
Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion‐based detection methods generally overlook the correlation between features. And mere concatenation of features will reduce the model’s characterization ability, lead to low detection accuracy. Moreover, these
Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng, Chenxi Dang, Ying Liu, Jingzhang Sun, Stelvio Cimato   +7 more
wiley   +1 more source

Deep Belief Networks-based framework for malware detection in Android systems

Alexandria Engineering Journal, 2018
Malware is the umbrella term that denotes attacking any system by malicious software. During the last few years, the popularity of Android smartphones led to the sneak of several malware applications into different Android markets without any difficulty.
Dina Saif, S.M. El-Gokhy, E. Sallam
doaj   +1 more source

OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning

Sensors, 2020
Due to the openness of an Android system, many Internet of Things (IoT) devices are running the Android system and Android devices have become a common control terminal for IoT devices because of various sensors on them.
Weina Niu, Rong Cao, Xiaosong Zhang, Kangyi Ding, Kaimeng Zhang, Ting Li   +5 more
doaj   +1 more source

WinDroid: A Novel Framework for Windows and Android Malware Family Classification Using Hierarchical Ensemble Support Vector Machines With Multiview Handcrafted and Deep Learning Features

IET Information Security, Volume 2025, Issue 1, 2025.
The rapid growth and diversification of malware variants, driven by advanced code obfuscation, evasion, and antianalysis techniques, present a significant threat to cybersecurity. The inadequacy of traditional methods in accurately classifying these evolving threats highlights the need for effective and robust malware classification techniques.
K. Sundara Krishnan, S. Syed Suhaila, Hyun-A Park   +2 more
wiley   +1 more source