Ransomware detection using machine learning algorithms-Reference-Cited by-同舟云学术

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Ransomware detection using machine learning algorithms

Published:2019-06-24 Issue:18 Volume:32 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Bae Seong Il¹,Lee Gyu Bin¹,Im Eul Gyu¹^ORCID

Affiliation:

1. Department of Computer Science Hanyang University Seoul South Korea

Funder

Korea Institute of Energy Technology Evaluation and Planning

National Research Foundation of Korea

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5422

Reference34 articles.

1. ZakariaWZA AbdollahMF MohdO AriffinAFM.The rise of ransomware. In: Proceedings of the 2017 International Conference on Software and e‐Business (ICSEB);2017;Hong Kong.

2. ArendsJ KerstinI.Malware analysis: tools and techniques.Sankt Augustin Germany:University of Applied Science Bonn‐Rhein‐Sieg;2018.

3. Malware analysis using APIs pattern mining;Obeis NT;Int J Eng Technol,2018

4. IwamotoK WasakiK.Malware classification based on extracted API sequences using static analysis. In: Proceedings of the Asian Internet Engineering Conference (AINTEC);2012;Bangkok Thailand.

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2. Enhancing Cybersecurity against Ransomware Attacks Using LSTM Deep Learning Method: A Case Study on Android Devices;Journal of Polytechnic;2024-08-09

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4. Enhanced Android Ransomware Detection Through Hybrid Simultaneous Swarm-Based Optimization;Cognitive Computation;2024-06-01

5. An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey;2024 IEEE International Conference on Electro Information Technology (eIT);2024-05-30

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