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检索条件"主题词=Android Malware Detection"
217 条 记 录,以下是21-30 订阅
排序:
LLM-MalDetect: A Large Language Model-Based Method for android malware detection
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IEEE ACCESS 2025年 13卷 81347-81364页
作者: Feng, Ruirui Chen, Hui Wang, Shuo Karim, Md Monjurul Jiang, Qingshan Hebei Univ Coll Math & Informat Sci Baoding 071002 Peoples R China Shenzhen Polytech Univ Sch Artificial Intelligence Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab High Performance Data Min Shenzhen 518055 Peoples R China
android malware poses a significant cybersecurity threat, enabling unauthorized data access, financial fraud, and device compromise. Although deep learning methods are widely used for malware detection, they often str... 详细信息
来源: 评论
GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced android malware detection
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IEEE ACCESS 2025年 13卷 81326-81346页
作者: Sharma, Yogesh Kumar Tomar, Deepak Singh Pateriya, R. K. Solanki, Surendra Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal 462003 Madhya Pradesh India Manipal Univ Jaipur Dept Artificial Intelligence & Machine Learning Jaipur 303007 Rajasthan India
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... 详细信息
来源: 评论
A Novel Approach for android malware detection Based on Intelligent Computing
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Computers, Materials & Continua 2024年 第12期81卷 4371-4396页
作者: Manh Vu Minh Cho Do Xuan Faculty of Information Security Posts and Telecommunications Institute of TechnologyHanoi100000Vietnam
Detecting malware on mobile devices using the android operating system has become a critical challenge in the field of cybersecurity,in the context of the rapid increase in the number of malware variants and the frequ... 详细信息
来源: 评论
A new approach to android malware detection using fuzzy logic-based simulated annealing and feature selection
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MULTIMEDIA TOOLS AND APPLICATIONS 2024年 第4期83卷 10525-10549页
作者: Seyfari, Yousef Meimandi, Akbar Univ Maragheh Fac Engn Maragheh Iran
The use of smartphones with the android operating system has been high in the last decade, with the transformation of works and services from traditional shape to mechanized and digitally, the percentage of use of sma... 详细信息
来源: 评论
Application of Symbolic Classifiers and Multi-Ensemble Threshold Techniques for android malware detection
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BIG DATA AND COGNITIVE COMPUTING 2025年 第2期9卷 27-27页
作者: Andelic, Nikola Segota, Sandi Baressi Mrzljak, Vedran Univ Rijeka Fac Engn Dept Automat & Elect Rijeka 51000 Croatia Univ Rijeka Fac Engn Dept Thermodynam & Energy Engn Rijeka 51000 Croatia
android malware detection using artificial intelligence today is a mandatory tool to prevent cyber attacks. To address this problem in this paper the proposed methodology consists of the application of genetic program... 详细信息
来源: 评论
Static analysis framework for permission-based dataset generation and android malware detection using machine learning
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EURASIP JOURNAL ON INFORMATION SECURITY 2024年 第1期2024卷 33页
作者: Pathak, Amarjyoti Kumar, Th. Shanta Barman, Utpal Guwahati Assam Sci & Technol Univ GIMT Gauhati Assam India Girijananda Chowdhury Univ Dept CSE Gauhati Assam India Assam Down Town Univ Fac Comp Technol Gauhati Assam India
Since android is the popular mobile operating system worldwide, malicious attackers seek out android smartphones as targets. The android malware can be identified through a number of established detection techniques. ... 详细信息
来源: 评论
DeepCatra: Learning flow- and graph-based behaviours for android malware detection
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IET INFORMATION SECURITY 2023年 第1期17卷 118-130页
作者: Wu, Yafei Shi, Jian Wang, Peicheng Zeng, Dongrui Sun, Cong Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Palo Alto Networks Santa Clara CA USA
As android malware grows and evolves, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and multi-view learning. However, they use onl... 详细信息
来源: 评论
MsDroid: Identifying Malicious Snippets for android malware detection
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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年 第3期20卷 2025-2039页
作者: He, Yiling Li, Yiping Wu, Lei Yang, Ziqi Ren, Kui Qin, Zhan Zhejiang Univ Dept Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China
Machine learning has shown promise for improving the accuracy of android malware detection in the literature. However, it is challenging to (1) stay robust towards real-world scenarios and (2) provide interpretable ex... 详细信息
来源: 评论
android malware detection method based on graph attention networks and deep fusion of multimodal features
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EXPERT SYSTEMS WITH APPLICATIONS 2024年 第PartC期237卷
作者: Chen, Shaojie Lang, Bo Liu, Hongyu Chen, Yikai Song, Yucai Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Zhongguancun Lab Beijing Peoples R China
Currently, android malware detection methods always focus on one kind of app feature, such as structural, semantic, or other statistical features. This paper proposes a novel android malware detection method that inte... 详细信息
来源: 评论
An effective deep learning scheme for android malware detection leveraging performance metrics and computational resources
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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年 第1期18卷 33-55页
作者: Wajahat, Ahsan He, Jingsha Zhu, Nafei Mahmood, Tariq Nazir, Ahsan Ullah, Faheem Qureshi, Sirajuddin Osman, Musa Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Lasbela Univ Agr Water & Marine Sci Dept Comp Sci Lasebla Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia Univ Educ Fac Informat Sci Vehari Pakistan
With the rise in the use of android smartphones, there has been a proportional surge in the proliferation of malicious applications (apps). As mobile phone users are at a heightened risk of data theft, detecting malwa... 详细信息
来源: 评论