Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review-Reference-Cited by-同舟云学术

Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

Published:2024-06 Issue: Volume:14 Page:100130
ISSN:2772-5030
Container-title:Telematics and Informatics Reports
language:en
Short-container-title:Telematics and Informatics Reports

Author:

Smmarwar Santosh K.^ORCID,Gupta Govind P.,Kumar Sanjay

Publisher

Elsevier BV

Reference145 articles.

1. P. Kumar, R. Tripathi, and G.P. Gupta, “A Review on Intrusion Detection System and Cyber Threat Intelligence for Secure IoT-enabled Network: Challenges and Directions.” [Online]. Available: https://www.researchgate.net/publication/359370843.

2. A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network;Kumar;J. Parallel Distrib. Comput.,2022

3. A metaheuristic-based ensemble feature selection framework for cyber threat detection in IoT-enabled networks;Dey;Decis. Anal. J.,2023

4. A Distributed framework for detecting DDoS attacks in smart contract-based Blockchain-IoT Systems by leveraging Fog computing;Kumar;Trans. Emerg. Telecommun. Technol.,2021

5. BLADE: Robust malware detection against obfuscation in android;Sihag;Forensic Sci. Int. Digit. Investig.,2021

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