Image-Based Malware Detection Using α-Cuts and Binary Visualisation
-
Published:2023-04-06
Issue:7
Volume:13
Page:4624
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Saridou Betty1ORCID, Moulas Isidoros2ORCID, Shiaeles Stavros3ORCID, Papadopoulos Basil1ORCID
Affiliation:
1. Lab of Mathematics and Informatics (ISCE), Faculty of Mathematics, Programming and General Courses, Department of Civil Engineering, School of Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece 2. School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK 3. Centre for Cybercrime and Economic Crime, University of Portsmouth, Portsmouth PO1 2UP, UK
Abstract
Image conversion of malicious binaries, or binary visualisation, is a relevant approach in the security community. Recently, it has exceeded the role of a single-file malware analysis tool and has become a part of Intrusion Detection Systems (IDSs) thanks to the adoption of Convolutional Neural Networks (CNNs). However, there has been little effort toward image segmentation for the converted images. In this study, we propose a novel method that serves a dual purpose: (a) it enhances colour and pattern segmentation, and (b) it achieves a sparse representation of the images. According to this, we considered the R, G, and B colour values of each pixel as respective fuzzy sets. We then performed α-cuts as a defuzzification method across all pixels of the image, which converted them to sparse matrices of 0s and 1s. Our method was tested on a variety of dataset sizes and evaluated according to the detection rates of hyperparameterised ResNet50 models. Our findings demonstrated that for larger datasets, sparse representations of intelligently coloured binary images can exceed the model performance of unprocessed ones, with 93.60% accuracy, 94.48% precision, 92.60% recall, and 93.53% f-score. This is the first time that α-cuts were used in image processing and according to our results, we believe that they provide an important contribution to image processing for challenging datasets. Overall, it shows that it can become an integrated component of image-based IDS operations and other demanding real-time practices.
Funder
European Union’s Horizon 2020 research innovation programme
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference105 articles.
1. Sahin, M., and Bahtiyar, S. (2020, January 4–7). A Survey on Malware Detection with Deep Learning. Proceedings of the 13th International Conference on Security of Information and Networks, Merkez, Turkey. 2. An enhancement for image-based malware classification using machine learning with low dimension normalized input images;Son;J. Inf. Secur. Appl.,2022 3. Image-Based malware classification using ensemble of CNN architectures (IMCEC);Vasan;Comput. Secur.,2020 4. Stupka, V., Horák, M., and Husák, M. (September, January 29). Protection of personal data in security alert sharing platforms. Proceedings of the 12th International Conference on Availability, Reliability and Security, Reggio Calabria, Italy. 5. Guidelines for stego/malware detection tools: Achieving GDPR compliance;Pawlicka;IEEE Technol. Soc. Mag.,2020
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Image-Based Malware Classification: A Systematic Literature Review;2023 IEEE International Conference on Cryptography, Informatics, and Cybersecurity (ICoCICs);2023-08-22
|
|