Image representation and deep inception-attention for file-type and malware classification
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| Title: | Image representation and deep inception-attention for file-type and malware classification |
|---|---|
| Authors: | Wang, Yi, Wu, Kejun, Liu, Wenyang, Yap, Kim-Hui, Chau, Lap-Pui |
| Contributors: | School of Electrical and Electronic Engineering, 2023 IEEE International Symposium on Circuits and Systems (ISCAS) |
| Publication Year: | 2023 |
| Collection: | DR-NTU (Digital Repository at Nanyang Technological University, Singapore) |
| Subject Terms: | Computer and Information Science, Image representation, Self-attention, Memory forensics, File-type classification, Malware analysis |
| Description: | File-type classification aims to recognize the file types of files/fragments without file-system metadata, which is essential for memory forensics and data recovery. In this paper, we introduce an image representation and deep inception-attention manner for file-type classification. Specifically, we consider file-type classification as an image classification problem. Raw data sequences in the memory block are converted to 2D binary images, enriching the representation ability and visualization while retaining the completeness of the bitstream. With binary images as inputs, we propose a deep inception-attention network to extract discriminate horizontal features and re-calibrate the weights of feature maps, and finally, predict file types. Experiments on a large-scale benchmark show the superiority of the proposed model. Moreover, our method can be extended to a similar application, like malware classification, and achieve outstanding performance. ; National Research Foundation (NRF) ; Submitted/Accepted version ; This research / project is supported by the National Research Foundation, Singapore, and Cyber Security Agency of Singapore under its National Cybersecurity R&D Programme (NRF2018NCR-NCR009-0001). |
| Document Type: | conference object |
| File Description: | application/pdf |
| Language: | English |
| ISBN: | 978-1-66545-109-3 1-66545-109-2 |
| Relation: | NRF2018NCRNCR009-0001; https://hdl.handle.net/10356/174535 |
| DOI: | 10.1109/ISCAS46773.2023.10181598 |
| Availability: | https://hdl.handle.net/10356/174535 https://doi.org/10.1109/ISCAS46773.2023.10181598 |
| Rights: | © 2023 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at https://doi.org/10.1109/ISCAS46773.2023.10181598. |
| Accession Number: | edsbas.B090CC65 |
| Database: | BASE |
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