Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification Model-Reference-Cited by-同舟云学术

Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification Model

Published:2023-11-22 Issue: Volume:22 Page:1555-1560
ISSN:
Container-title:2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA)
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Author:

A Arthi.¹,Aggarwal Kapil²,Karthikeyan R³,Kayalvili S.⁴,S Srimathi⁵,Srivastava Aviral⁶

Affiliation:

1. Rajalakshmi Institute of Technology,Department of Artificial Intelligence and Data Science,Chennai

2. Koneru Lakshmaiah Education Foundation (Deemed to be University),Department of Computer Science & Engineering,Vaddeswaram, Guntur,Andhra Pradesh,India,522302

3. Dr NGP Institute of Technology,Department of Artificial Intelligence and Data Science,Coimbatore

4. Artificial Intelligence College Name & Address: Kongu Engineering College,Perundurai

5. Saveetha School of Engineering, SIMATS,Department of Bio Technology,Chennai,Tamilnadu,India

6. Amity University Rajasthan Amity University Rajasthan,Jaipur,300202

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10394672/10394681/10394838.pdf?arnumber=10394838

Reference20 articles.

1. Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT

2. Efficiency of Malware Detection in Android System: A Survey

3. Feature Subset Selection for Malware Detection in Smart IoT Platforms

4. Feature engineering and evaluation for android malware detection scheme;Jung;Journal of Internet Technology,2021

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

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