Keyword

malware classification

Description: Malware classification is the cybersecurity task of assigning malicious software to categories or families using code, behavior, or other features.

Primary topic: Advanced Malware Detection Techniques (91%)

OpenAlex ID: keywords/malware-classification

Malware images

2011 · L. Nataraj, S. Karthikeyan, et al. · 1,224

Automatic analysis of malware behavior using machine learning

2011 · Konrad Rieck, Philipp Trinius, et al. · Journal of Computer Security · 729

Deep neural network based malware detection using two dimensional binary program features

2015 · Joshua Saxe, Konstantin Berlin · International Conference on Malicious and Unwanted Software · 711

DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android

2013 · Yousra Aafer, Wenliang Du, et al. · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · 686

The rise of machine learning for detection and classification of malware: Research developments, trends and challenges

2020 · Daniel Gibert, Carles Mateu, et al. · Journal of Network and Computer Applications · 620

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Significant Permission Identification for Machine-Learning-Based Android Malware Detection

2018 · Jin Li, Lichao Sun, et al. · IEEE Transactions on Industrial Informatics · 607

Learning and Classification of Malware Behavior

2008 · Konrad Rieck, Thorsten Holz, et al. · Lecture notes in computer science · 589

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Robust Intelligent Malware Detection Using Deep Learning

2019 · R. Vinayakumar, Mamoun Alazab, et al. · IEEE Access · 577

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Survey of machine learning techniques for malware analysis

2018 · Daniele Ucci, Leonardo Aniello, et al. · Computers & Security · 570

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Deep Learning for Classification of Malware System Call Sequences

2016 · Bojan Kolosnjaji, Apostolis Zarras, et al. · Lecture notes in computer science · 515

Automated Classification and Analysis of Internet Malware

2007 · Michael Bailey, Jon Oberheide, et al. · Lecture notes in computer science · 513

Deep Android Malware Detection

2017 · Niall McLaughlin, Jesus Martinez del Rincon, et al. · 493

A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting

2018 · Hamed HaddadPajouh, Ali Dehghantanha, et al. · Future Generation Computer Systems · 445

Large-scale malware classification using random projections and neural networks

2013 · George E. Dahl, Jack W. Stokes, et al. · IEEE International Conference on Acoustics Speech and Signal Processing · 439

Malware classification with recurrent networks

2015 · Razvan Pascanu, Jack W. Stokes, et al. · IEEE International Conference on Acoustics Speech and Signal Processing · 424

Malware Analysis and Classification: A Survey

2014 · Ekta Gandotra, Divya Bansal, et al. · Journal of Information Security · 416

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Malware Classification with Deep Convolutional Neural Networks

2018 · Mahmoud Kalash, Mrigank Rochan, et al. · IFIP International Conference on New Technologies, Mobility and Security (NTMS) · 408

Image-Based malware classification using ensemble of CNN architectures (IMCEC)

2020 · Danish Vasan, Mamoun Alazab, et al. · Computers & Security · 401

Deep Ground Truth Analysis of Current Android Malware

2017 · Fengguo Wei, Yuping Li, et al. · Lecture notes in computer science · 398

AVclass: A Tool for Massive Malware Labeling

2016 · Marcos Sebastián, Richard Rivera, et al. · Lecture notes in computer science · 385

Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning

2018 · Amin Azmoodeh, Ali Dehghantanha, et al. · IEEE Transactions on Sustainable Computing · 378

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A Review of Android Malware Detection Approaches Based on Machine Learning

2020 · Kaijun Liu, Shengwei Xu, et al. · IEEE Access · 374

Machine learning aided Android malware classification

2017 · Nikola Milosevic, Ali Dehghantanha, et al. · Computers & Electrical Engineering · 347

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Autoencoder-based feature learning for cyber security applications

2017 · Mahmood Yousefi-Azar, Vijay Varadharajan, et al. · 333

On the (Statistical) Detection of Adversarial Examples

2017 · Grosse, Kathrin, Manoharan, Praveen, et al. · arXiv (Cornell University) · 330

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Works count: 7,313

Citation count: 87,650