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Malicious Code Detection based on Image Processing Using Deep Learning

Published: 12 March 2018 Publication History

Abstract

In this study, we have used the Image Similarity technique to detect the unknown or new type of malware using CNN ap- proach. CNN was investigated and tested with three types of datasets i.e. one from Vision Research Lab, which contains 9458 gray-scale images that have been extracted from the same number of malware samples that come from 25 differ- ent malware families, and second was benign dataset which contained 3000 different kinds of benign software. Benign dataset and dataset vision research lab were initially exe- cutable files which were converted in to binary code and then converted in to image files. We obtained a testing ac- curacy of 98% on Vision Research dataset.

References

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Vision reseach lab malimg dataset http://old.vision.ece.ucsb.edu/spam/malimg.shtml.
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Bennasar, H., Bendahmane, A. and Essaaidi, M., 2017, April. An Overview of the State-of-the-Art of Cloud Computing Cyber-Security. In International Conference on Codes, Cryptology, and Information Security (pp. 56--67). Springer, Cham.
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Gavriluţ, D., Cimpoeşu, M., Anton, D. and Ciortuz, L., 2009, October. Malware detection using machine learning. In Computer Science and Information Technology, 2009. IMCSIT'09. International Multiconference on (pp. 735--741). IEEE.
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    cover image ACM Other conferences
    ICCAI '18: Proceedings of the 2018 International Conference on Computing and Artificial Intelligence
    March 2018
    156 pages
    ISBN:9781450364195
    DOI:10.1145/3194452
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 12 March 2018

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    Author Tags

    1. Convolutional Neural Network
    2. Deep Learning
    3. Mal- ware Classification
    4. Malware Detection

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    • (2024)A Novel Deep Ensemble Framework for IoT Malware Variant Detection2024 IEEE International Conference on Smart Internet of Things (SmartIoT)10.1109/SmartIoT62235.2024.00076(457-462)Online publication date: 14-Nov-2024
    • (2024)A Model Based on GCN and TCN for Malicious Code Detection in Power Information System2024 4th International Conference on Neural Networks, Information and Communication (NNICE)10.1109/NNICE61279.2024.10499045(276-280)Online publication date: 19-Jan-2024
    • (2024)A Study on Intrusion Detection Using Multi-Scale Convolutional Neural Network2024 4th International Conference on Neural Networks, Information and Communication (NNICE)10.1109/NNICE61279.2024.10498663(264-269)Online publication date: 19-Jan-2024
    • (2024)Transfer learning based multi-class malware classification using VGG16 feature extractor and SVM classifier2024 10th International Conference on Communication and Signal Processing (ICCSP)10.1109/ICCSP60870.2024.10543784(999-1004)Online publication date: 12-Apr-2024
    • (2024)Enhanced Malware Image Classification through Ensemble model2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)10.1109/ICAAIC60222.2024.10575553(1421-1425)Online publication date: 5-Jun-2024
    • (2024)Optimizing Spyware Detection with Combined Neural Networks and Random Forest Models2024 4th Asian Conference on Innovation in Technology (ASIANCON)10.1109/ASIANCON62057.2024.10837999(1-5)Online publication date: 23-Aug-2024
    • (2024)A Systematic Literature Review on AI-Based Methods and Challenges in Detecting Zero-Day AttacksIEEE Access10.1109/ACCESS.2024.345541012(144150-144163)Online publication date: 2024
    • (2024)Deep learning applications on cybersecurityNeurocomputing10.1016/j.neucom.2023.126904563:COnline publication date: 1-Jan-2024
    • (2024)IMCNN:Intelligent Malware Classification using Deep Convolution Neural Networks as Transfer learning and ensemble learning in honeypot enabled organizational networkComputer Communications10.1016/j.comcom.2023.12.036216(16-33)Online publication date: Feb-2024
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