Performance Analysis of Unsupervised Machine Learning Methods for Mobile Malware Detection-Reference-Cited by-同舟云学术

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Performance Analysis of Unsupervised Machine Learning Methods for Mobile Malware Detection

Published:2022-03-23 Issue: Volume: Page:
ISSN:
Container-title:2022 9th International Conference on Computing for Sustainable Global Development (INDIACom)
language:
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Author:

Padmavathi G.¹,Shanmugapriya D.²,Roshni A.³

Affiliation:

1. Avinashilingam Inst. for Home Science and Higher Education for Women,Department of Computer Science,Coimbatore,India

2. Avinashilingam Inst. for Home Science and Higher Education for Women,Department of Information Technology,Coimbatore,India

3. Centre for Cyber Intelligence Avinashilingam Inst. for Home Science and Higher Education for Women,Coimbatore,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9763090/9763103/09763180.pdf?arnumber=9763180

Reference21 articles.

1. Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection

2. RansomDroid: Forensic analysis and detection of Android Ransomware using unsupervised machine learning technique

3. A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

4. Towards an interpretable deep learning model for mobile malware detection and family identification

5. MLDroid—framework for Android malware detection using machine learning techniques

Cited by 4 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. Ensemble Machine Learning and Feature Selection for Effective Malware Detection;2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT);2024-04-06

2. Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques;Computers, Materials & Continua;2024

3. Machine learning and deep learning techniques for detecting malicious android applications: An empirical analysis;Proceedings of the Indian National Science Academy;2023-06-21

4. Data science leverage and big data analysis for Internet of Things energy systems;IoT Enabled Multi-Energy Systems;2023

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