Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN-Reference-Cited by-同舟云学术

Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN

Published:2023-06 Issue:2 Volume:17 Page:2756-2766
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

A. Dhanya K.¹^ORCID,P. Vinod²^ORCID,Yerima Suleiman Y.³^ORCID,Bashar Abul⁴^ORCID,David Anwin⁵,T. Abhiram⁵,Antony Alan⁵,Shavanas Ashil K.⁵,T. Gireesh Kumar⁶^ORCID

Affiliation:

1. TIFAC-CORE in Cyber Security, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Ettimadai, India

2. Department of Computer Applications, Cochin University of Science and Technology, Kochi, India

3. Cyber Technology Institute, De Montfort University, Leicester, U.K.

4. College of Computer Engineering Science, Prince Mohammad Bin Fahd University, Dhahran, Saudi Arabia

5. Department of Computer Science Engineering, SCMS School of Engineering Technology, Ernakulam, India

6. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Ettimadai, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/10146391/10040557.pdf?arnumber=10040557

Reference57 articles.

1. Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks

2. TESSERACT: Eliminating experimental bias in malware classification across space and time;pendlebury;Proc 28th Usenix Secur Symp Usenix,0

3. Obfuscapk: An open-source black-box obfuscation tool for Android apps

4. Longitudinal performance analysis of machine learning based Android malware detectors

5. Deep Learning Techniques for Android Botnet Detection

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