An optimized and efficient android malware detection framework for future sustainable computing-Reference-Cited by-同舟云学术

An optimized and efficient android malware detection framework for future sustainable computing

Published:2022-12 Issue: Volume:54 Page:102852
ISSN:2213-1388
Container-title:Sustainable Energy Technologies and Assessments
language:en
Short-container-title:Sustainable Energy Technologies and Assessments

Author:

Smmarwar Santosh K.,Gupta Govind P.,Kumar Sanjay,Kumar Prabhat

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference46 articles.

1. A novel metaheuristics with deep learning enabled intrusion detection system for secured smart environment;Malibari;Sustain Energy Technol Assess,2022

2. An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks;Kumar;Comput Commun,2021

3. Stats, S. G. Mobile Operating System Market Share Worldwide. https://gs.statcounter.com/os-market-share/mobile/worldwide (Accessed: Oct. 19, 2021).

4. Imtiaz SI, ur Rehman S, Javed AR, Jalil Z, Liu X, Alnumay WS. DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network. Future Generation computer systems. 2021 Feb 1;115:844-56.

5. A survey of android application and malware hardening;Sihag;Comput Sci Rev,2021

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network;International Journal of Machine Learning and Cybernetics;2024-08-19

2. TMaD: Three‐tier malware detection using multi‐view feature for secure convergence ICT environments;Expert Systems;2024-07-19

3. DETECTION OF ANDROID MALWARE USING DEEP LEARNING ENSEMBLE WITH CHEETAH-OPTIMIZED FEATURE SELECTION;Advances and Applications in Discrete Mathematics;2024-06-06

4. TL‐GNN: Android Malware Detection Using Transfer Learning;Applied AI Letters;2024-05-10

5. Enhancing Zero-Day Attack Detection with XAI-Driven ML Models and SMOTE Analysis;2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT);2024-05-03