Back close

Deep Learning Framework and Visualization for Malware Classification

Publication Type : Conference Paper

Publisher : Institute of Electrical and Electronics Engineers Inc

Source : 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, Institute of Electrical and Electronics Engineers Inc., p.1059-1063 (2019)

Url : https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85067911208&doi=10.1109%2fICACCS.2019.8728471&partnerID=40&md5=80e541d78c619d4082369742ec895918

ISBN : 9781538695333

Keywords : Computer crime, Convolutional neural network, Cost-sensitive learning, Deep learning, Hyper-parameter, Image processing, Imbalanced Data-sets, Learning architectures, Learning frameworks, Learning rates, Learning systems, Long short-term memory, malware, Malware classifications, Network architecture

Campus : Amritapuri, Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : CISAI, Electronics and Communication

Year : 2019

Abstract : In this paper we propose a deep learning framework for classification of malware. There has been an enormous increase in the volume of malware generated lately which represents a genuine security danger to organizations and people. So as to battle the expansion of malwares, new strategies are needed to quickly identify and classify malware. Malimg dataset, a publicly available benchmark data set was used for the experimentation. The architecture used in this work is a hybrid cost-sensitive network of one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network which obtained an accuracy of 94.4%, an increase in performance compared to work done by [1] which got 84.9%. Hyper parameter tuning is done on deep learning architecture to set the parameters. A learning rate of 0.01 was taken for all experiments. Train-test split of 70-30% was done during experimentation. This facilitates to find how well the models perform on imbalanced data sets. Usual methods like disassembly, decompiling, de-obfuscation or execution of the binary need not be done in this proposed method. The source code and the trained models are made publicly available for further research. © 2019 IEEE.

Cite this Research Publication : S. Akarsh, Simran, K., Poornachandran, P., Menon, V. K., and Dr. Soman K. P., “Deep Learning Framework and Visualization for Malware Classification”, in 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 2019, pp. 1059-1063.

Admissions Apply Now