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

Alazab, 2015, Profiling and classifying the behavior of malicious codes, J. Syst. Softw., 10.1016/j.jss.2014.10.031

Azmoodeh, 2018, Detecting crypto-ransomware in IOT networks based on energy consumption footprint, J. Ambient Intell. Humaniz. Comput., 10.1007/s12652-017-0558-5

Bayer, 2009, Scalable, behavior-based malware clustering, 16th Symp. Netw. Distrib. Syst. Secur.

N. Bhodia, P. Prajapati, F. Di Troia, and M. Stamp, “Transfer learning for image-based malware classification,” 2018.

Beek, 2017, McAfee labs threats report: April 2017, McAfee Labs Rep., 1

Bianco, 2018, Benchmark analysis of representative deep neural network architectures, IEEE Access, 10.1109/ACCESS.2018.2877890

Bishop, 2006, Pattern recognition and machine learning (Information science and statistics), 1st edn. 2006. corr. 2nd printing edn, Mach. Learn.

Cetinic, 2018, Fine-tuning convolutional neural networks for fine art classification, Expert Syst. Appl., 114, 107, 10.1016/j.eswa.2018.07.026

Chang, 2017, A method for classifying medical images using transfer learning: a pilot study on histopathology of breast cancer

Chen, 2017, An ensemble of convolutional neural networks for image classification based on LSTM, Proc. - 2017 Int. Conf. Green Informatics, ICGI 2017, 21, 217

Choi, 2017, Malware detection using malware image and deep learning, 1193

Chollet, 2015, Keras: deep learning library for theano and tensorflow, GitHub Repos.

Cohen, 2014, Fast effective rule induction

Conti, 2008, Visual reverse engineering of binary and data files

Cui, 2019, Malicious code detection based on CNNs and multi-objective algorithm, J. Parallel Distrib. Comput., 129, 50, 10.1016/j.jpdc.2019.03.010

Cui, 2018, Detection of malicious code variants based on deep learning, IEEE Trans. Ind. Informatics, 14, 3187, 10.1109/TII.2018.2822680

Damodaran, 2017, A comparison of static, dynamic, and hybrid analysis for malware detection, J. Comput. Virol. Hacking Tech., 10.1007/s11416-015-0261-z

Dean, 2015, TensorFlow - Google's latest machine learning system, open sourced for everyone, Google Res. Blog

Desai, 2011, A highly metamorphic virus generator, Int. J. Multimed. Intell. Secur.

Farivar, 2019, Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber physical systems and industrial IOT, IEEE Trans. Ind. Informatics, PP, 1

Fraz, 2012, An ensemble classification-based approach applied to retinal blood vessel segmentation, IEEE Trans. Biomed. Eng., 10.1109/TBME.2012.2205687

Han, 2014, Malware analysis using visualized image matrices, Sci. World J., 10.1155/2014/132713

He, 2016, Deep residual learning for image recognition

J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” 2019, doi: 10.18653/v1/p18-1031.

Huda, 2016, Hybrids of support vector machine wrapper and filter based framework for malware detection, Futur. Gener. Comput. Syst., 10.1016/j.future.2014.06.001

Hutt, 2017

Imran, 2016, Using hidden markov model for dynamic malware analysis: first impressions

Jung, 2020, Packer identification method based on byte sequences, Concurr. Comput. Pract. Exp., 10.1002/cpe.5082

Kancherla, 2013, Image visualization based malware detection

A. Karpathy and F.-.F. Li, “Convolutional neural networks for visual recognition,” Available in http://cs231n.github.io/convolutional-networks, 2015.

Kaya, 2019, Analysis of transfer learning for deep neural network based plant classification models, Comput. Electron. Agric., 158, 20, 10.1016/j.compag.2019.01.041

Khan, 2018, Analysis of resnet and googlenet models for malware detection, J. Comput. Virol. Hacking Tech.

Kim, 2010, Design and performance evaluation of binary code packing for protecting embedded software against reverse engineering

Kolter, 2006, Learning to detect and classify malicious executables in the wild, J. Mach. Learn. Res.

Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks

Kumar, 2017, An ensemble of fine-tuned convolutional neural networks for medical image classification, IEEE J. Biomed. Heal. Inform., 10.1109/JBHI.2016.2635663

R. Kumar, Z. Xiaosong, R.U. Khan, I. Ahad, and J. Kumar, “Malicious code detection based on image processing using deep learning,” pp. 81–85, 2018, doi: 10.1145/3194452.3194459.

Lab, 2016, What is a keylogger? | Definition | Kaspersky lab US, Kaspersky Lab

Li, 2018, Learning without forgetting, IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2017.2773081

Lim, 2015, Stable nonpolar solvent droplet generation using a poly(dimethylsiloxane) microfluidic channel coated with poly-p-xylylene for a nanoparticle growth, Biomed. Microdevices, 10.1007/s10544-015-9974-5

Lin, 2011, Hunting for undetectable metamorphic viruses, J. Comput. Virol., 10.1007/s11416-010-0148-y

Lindorfer, 2015, MARVIN: efficient and comprehensive mobile app classification through static and dynamic analysis

Long, 2018, Transferable representation learning with deep adaptation networks, IEEE Trans. Pattern Anal. Mach. Intell.

Maćkiewicz, 1993, Principal components analysis (PCA), Comput. Geosci., 10.1016/0098-3004(93)90090-R

Mohamed Shakeel, 2018, Maintaining security and privacy in health care system using learning based deep-q-networks, J. Med. Syst., 10.1007/s10916-018-1045-z

Namanya, 2019, Similarity hash based scoring of portable executable files for efficient malware detection in iot, Futur. Gener. Comput. Syst.

Nataraj, 2011, Malware images: visualization and automatic classification, Proc. 8th Int. Symp. Vis. Cyber Secur., 4

Nataraj, 2011, A comparative assessment of malware classification using binary texture analysis and dynamic analysis

Ng, 2015, Deep learning for emotion recognition on small datasets using transfer learning

Ni, 2018, Malware identification using visualization images and deep learning, Comput. Secur., 77, 871, 10.1016/j.cose.2018.04.005

Özbulak, 2016, How transferable are CNN-based features for age and gender classification?

Pascanu, 2015, Malware classification with recurrent networks

Radenovic, 2019, Fine-Tuning CNN image retrieval with no human annotation, IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2018.2846566

Reyes, 2015, Fine-tuning deep convolutional networks for plant recognition

Rezende, 2018, Malicious software classification using transfer learning of RESNET-50 deep neural network

Rezende, 2018, Malicious software classification using VGG16 deep neural network's bottleneck features, 10.1007/978-3-319-77028-4_9

Rieck, 2011, Automatic analysis of malware behavior using machine learning, J. Comput. Secur., 10.3233/JCS-2010-0410

Saxe, 2015, Deep neural network based malware detection using two dimensional binary program features, 11

Schultz, 2002, Data mining methods for detection of new malicious executables, 38

Shabtai, 2012, Detecting unknown malicious code by applying classification techniques on OpCode patterns, Secur. Inform., 10.1186/2190-8532-1-1

Shaha, 2018, Transfer learning for image classification, 656

Shelhamer, 2017, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2016.2572683

Shijo, 2015, Integrated static and dynamic analysis for malware detection, Procedia. Comput. Sci., 10.1016/j.procs.2015.02.149

K. Simonyan and A. Zisserman, “VGG-16,” arXiv Prepr., 2014, doi: 10.1016/j.infsof.2008.09.005.

Singh, 2016, Support vector machines and malware detection, J. Comput. Virol. Hacking Tech., 10.1007/s11416-015-0252-0

Su, 2018, Lightweight classification of IOT malware based on image recognition, 2, 664

Sun, 2018, Fast object detection based on binary deep convolution neural networks, CAAI Trans. Intell. Technol., 10.1049/trit.2018.1026

Szegedy, 2015, Going deeper with convolutions

Szegedy, 2017, the impact of residual connections on learning

Tajbakhsh, 2016, Convolutional neural networks for medical image analysis: full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 1299, 10.1109/TMI.2016.2535302

Varoquaux, 2017, Scikit-learn, GetMobile Mob. Comput. Commun.

Vinayakumar, 2019, Deep learning approach for intelligent intrusion detection system, IEEE Access

Wang, 2018, Survey on person re-identification based on deep learning, CAAI Trans. Intell. Technol., 10.1049/trit.2018.1001

Wang, 2014, Support vector machines

Wen, 2019, A transfer convolutional neural network for fault diagnosis based on resnet-50, Neural Comput. Appl., 0123456789

Williams, 2003, Learning with kernels: support vector machines, regularization, optimization, and beyond, J. Am. Stat. Assoc., 10.1198/jasa.2003.s269

Yajamanam, 2018, Deep learning versus gist descriptors for image-based malware classification, Icissp, 553

Yan, 2018, Detecting malware with an ensemble method based on deep neural network, Secur. Commun. Networks, 2018, 1

I. Yoo, “Visualizing windows executable viruses using self-organizing maps,” 2005, doi: 10.1145/1029208.1029222.

Yuan, 2016, Droiddetector: android malware characterization and detection using deep learning, Tsinghua Sci. Technol., 10.1109/TST.2016.7399288

S. Yue, “Imbalanced malware images classification: a CNN based approach,” 2017.

YusirwanS, 2015, Implementation of malware analysis using static and dynamic analysis method, Int. J. Comput. Appl.

Zhou, 2017, Fine-tuning convolutional neural networks for biomedical image analysis, Cvf

Zhou, 2017, Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally, Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, 2017, 4761, 10.1109/CVPR.2017.506

Zhou, 2009, 270

Zolkipli, 2011, An approach for malware behavior identification and classification