Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector

Lecture Notes in Networks and Systems (LNNS,volume 882); The 13th Conference on Information Technology and Its Applications (CITA 2024) ; pp: 523-535.

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Những tác giả chính:	Luu, Nguyen Cong Minh, Le, Trong Nhan, To, Trong Nghia, Hoang, Khoa Nghi, Phan, The Duy, Pham, Van Hau
Format:	Bài viết
Jezik:	English
Izdano:	Springer Nature 2024
Teme:	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector Windows malware detectors Common antivirus platform VirusTotal
Online dostop:	https://elib.vku.udn.vn/handle/123456789/4305 https://doi.org/10.1007/978-3-031-74127-2_42
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spelling	oai:elib.vku.udn.vn:123456789-43052024-12-09T03:36:16Z Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector Luu, Nguyen Cong Minh Le, Trong Nhan To, Trong Nghia Hoang, Khoa Nghi Phan, The Duy Pham, Van Hau Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector Windows malware detectors Common antivirus platform VirusTotal Lecture Notes in Networks and Systems (LNNS,volume 882); The 13th Conference on Information Technology and Its Applications (CITA 2024) ; pp: 523-535. As data driven-based Windows malware detectors become increasingly prevalent, the need for robust evaluation and enhancement of adversarial malware generation techniques also becomes imperative, as malicious actors will adapt and enhance their malware to evade detection. There are numerous works that introduce new techniques or enhancements for adversarial malware. One of these approaches is to leverage an iterative process, dynamically modifying adversarial malware with populations of injections based on feedback from a machine learning-based detector, aiming to enhance evasion capabilities through successive iterations. It is obvious that the effectiveness of a robust adversarial malware is influenced not only by the quality of the manipulation payload injected into the malware, but also by the capabilities and strength of the detector that interacts with the manipulated malware. In this paper, we introduce a multimodal approach to generate adversarial malware with robustness specifically fortified through the feedback of a deep learning (DL) detector with multiple modalities in the progress of adversaries generation. We evaluate the effectiveness of our approach in comparison to the implementation of conventional unimodal detectors such as MalConv in previous works with our proper adaptation in manipulation technique. We also consider the malware detection performance of the common antivirus platform VirusTotal with adversarial samples, and notably that the robust adversarial malware were able to evade up to average 3 detection programs more than the initial malware does. 2024-12-09T03:34:45Z 2024-12-09T03:34:45Z 2024-11 Working Paper 978-3-031-74126-5 https://elib.vku.udn.vn/handle/123456789/4305 https://doi.org/10.1007/978-3-031-74127-2_42 en application/pdf Springer Nature
institution	Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng
collection	DSpace
language	English
topic	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector Windows malware detectors Common antivirus platform VirusTotal
spellingShingle	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector Windows malware detectors Common antivirus platform VirusTotal Luu, Nguyen Cong Minh Le, Trong Nhan To, Trong Nghia Hoang, Khoa Nghi Phan, The Duy Pham, Van Hau Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector
description	Lecture Notes in Networks and Systems (LNNS,volume 882); The 13th Conference on Information Technology and Its Applications (CITA 2024) ; pp: 523-535.
format	Working Paper
author	Luu, Nguyen Cong Minh Le, Trong Nhan To, Trong Nghia Hoang, Khoa Nghi Phan, The Duy Pham, Van Hau
author_facet	Luu, Nguyen Cong Minh Le, Trong Nhan To, Trong Nghia Hoang, Khoa Nghi Phan, The Duy Pham, Van Hau
author_sort	Luu, Nguyen Cong Minh
title	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector
title_short	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector
title_full	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector
title_fullStr	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector
title_full_unstemmed	Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector
title_sort	multimodal deep learning feedback for generating evasive malware samples against malware detector
publisher	Springer Nature
publishDate	2024
url	https://elib.vku.udn.vn/handle/123456789/4305 https://doi.org/10.1007/978-3-031-74127-2_42
_version_	1849199897413156864

Multimodal Deep Learning Feedback for Generating Evasive Malware Samples Against Malware Detector

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