A Survey on Malware Detection and Classification-Reference-Cited by-同舟云学术

A Survey on Malware Detection and Classification

Published:2020-08-11 Issue:3 Volume:16 Page:390-420
ISSN:1936-1610
Container-title:Journal of Applied Security Research
language:en
Short-container-title:Journal of Applied Security Research

Author:

Komatwar Rupali¹,Kokare Manesh²

Affiliation:

1. Department of Computer Engineering, Government Polytechnic, Arvi, India

2. Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India

Publisher

Informa UK Limited

Subject

Law,Safety Research,Safety, Risk, Reliability and Quality,Education

Link

https://www.tandfonline.com/doi/pdf/10.1080/19361610.2020.1796162

Reference84 articles.

1. A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

2. Agarap, A. F. M. (2019). Towards building an intelligent anti-malware system: A deep learning approach using support vector machine (SVM) for malware classification. Computer Science. https://arxiv.org/abs/1801.00318.

3. DroidNative: Automating and optimizing detection of Android native code malware variants

4. An improved Android malware detection scheme based on an evolving hybrid neuro-fuzzy classifier (EHNFC) and permission-based features

5. Entropy analysis to classify unknown packing algorithms for malware detection

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