MeMalDet: A memory analysis-based malware detection framework using deep autoencoders and stacked ensemble under temporal evaluations-Reference-Cited by-同舟云学术

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MeMalDet: A memory analysis-based malware detection framework using deep autoencoders and stacked ensemble under temporal evaluations

Published:2024-07 Issue: Volume:142 Page:103864
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Maniriho Pascal^ORCID,Mahmood Abdun Naser^ORCID,Chowdhury Mohammad Jabed Morshed^ORCID

Publisher

Elsevier BV

Reference111 articles.

1. Behavior-based ransomware classification: A particle swarm optimization wrapper-based approach for feature selection;Abbasi;Appl. Soft Comput.,2022

2. Malware dynamic analysis evasion techniques: A survey;Afianian;ACM Comput. Surv.,2019

3. Peeler: Profiling kernel-level events to detect ransomware;Ahmed,2021

4. A system call refinement-based enhanced minimum redundancy maximum relevance method for ransomware early detection;Ahmed;J. Netw. Comput. Appl.,2020

5. Mitigating adversarial evasion attacks of ransomware using ensemble learning;Ahmed;Comput. Electr. Eng.,2022

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1. A recommendation attack detection approach integrating CNN with Bagging;Computers & Security;2024-11

2. Hybrid Deep Learning Approach Based on LSTM and CNN for Malware Detection;Wireless Personal Communications;2024-06

3. Explainable Obfuscated Memory Malware Detector Using Sparse Projections with Isolated Family Distinction Paradigm;2024

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