RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification-Reference-Cited by-同舟云学术

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RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification

Published:2024 Issue: Volume:12 Page:82622-82635
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Puneeth S.¹^ORCID,Lal Shyam¹^ORCID,Pratap Singh Mahendra²^ORCID,Raghavendra B. S.¹^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka (NITK) at Surathkal, Surathkal, India

2. Department of Computer Science and Engineering, National Institute of Technology Karnataka (NITK) at Surathkal, Surathkal, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10535155.pdf?arnumber=10535155

Reference42 articles.

1. Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security

2. Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics

3. Deep Learning Models for Cyber Security in IoT Networks

4. A state-of-the-art survey of malware detection approaches using data mining techniques

5. Internet of Things is a revolutionary approach for future technology enhancement: a review

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