Deep Learning-based Malware Classification Methodology of Comprehensive Study

Author:

Depuru Sivakumar1,Santhi K2,Amala K3,Sakthivel M.4,Sivanantham S.5,Akshaya V.4

Affiliation:

1. Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College),Dept of CSSE,Tirupati,India

2. Sri Venkateswara College of Engineering (Autonomous),Dept. of CSE,Tirupati,India

3. Sri Venkateswara College of Engineering (Autonomous),Dept. of ECE,Tirupati,India

4. Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College),Dept of CSE,Tirupati,India

5. Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College),Dept of CSSE,Tirupati,AP,India

Publisher

IEEE

Reference25 articles.

1. IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture

2. Convolutional networks for images, speech, and time series;lecun;The Handbook of Brain Theory and Neural Networks,1995

3. Very deep convolutional networks for large-scale image recognition;simonyan;arXiv preprint arXiv 1409 1556,2014

4. Malware Classification with Improved Convolutional Neural Network Model

5. Visual Reverse Engineering of Binary and Data Files

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