Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques-Reference-Cited by-同舟云学术

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques

Published:2024-02-28 Issue: Volume:8 Page:1754-1760
ISSN:
Container-title:2024 11th International Conference on Computing for Sustainable Global Development (INDIACom)
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Author:

Rani D Sandhya¹,Gnaneshwar K²,Sampurnima Pattem K¹,Sekhar Soma³,Krishna G Bala¹,Kakarla Shirisha²

Affiliation:

1. CVR College of Engineering Ibrahimpatnam,Department of Computer Science Engineering,Rangareddy,India

2. Sreenidhi Institute of Science and Technology,Department of Computer Science Engineering-Cyber Security,Hyderabad,India

3. Vardhaman College of Engineering,Department of Computer Science Engineering,Hyderabad,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10498128/10498148/10498302.pdf?arnumber=10498302

Reference20 articles.

1. An Optimized KNN Model for Signature-Based Malware Detection;Assegie;Int. J. Comput. Eng. Res. Trends,2021

2. Cyber Threat Security System Using Artificial Intelligence for Android-Operated Mobile Devices

3. ELC-PPW: Ensemble Learning and Classification (LC) by Positional Patterns Weights (PPW) of API Calls as Dynamic n-Grams for Malware Perception

4. A Review on Machine Learning-based Malware Detection Techniques for Internet of Things (IoT) Environments

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