A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysis

Stamp, Mark

doi:10.1007/978-3-030-62582-5_1

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Algorithm 1: Align Byte By Width [30,196] Input: data, width Output: aligned_byte, byte data had been aligned by width 1 size ← len(data) ; / * get length of byte data * / 2 if size is 0 then This collection of simulations aims at developing a model that is able to detect whether a file is malware or not. This project trains a malware classifier first and then detects files' Studies showed that fine-tuned and transferred learning achieve good results in image-based malware classification [31]- [33] but also outperform other traditional networks [34].…”

Section: F Deep Ocean Malware Detectormentioning

confidence: 99%

A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System

Hai,

Van Thieu,

Duong

et al. 2023

IEEE Access

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Section: F Deep Ocean Malware Detectormentioning

confidence: 99%

A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System

Hai,

Van Thieu,

Duong

et al. 2023

IEEE Access

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“…In this section, we provide a brief introduction to each of the learning techniques considered in this paper. Additional details on most of the learning techniques discussed here can be found in [27], which includes examples of relevant applications of the techniques. We provide additional references for the techniques discussed below that are not considered in [27].…”

Section: Learning Techniquesmentioning

confidence: 99%

An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

Stamp¹

2021

Preprint

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In this paper, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Amongst our CNN experiments, transfer learning plays a prominent rolespecifically, we test the VGG-19 and ResNet152 models. As compared to previous work, the results presented in this paper are based on a larger and more diverse malware dataset, we consider a wider array of features, and we experiment with a much greater variety of learning techniques. Consequently, our results are the most comprehensive and complete that have yet been published.

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A Study of Malware Datasets and Techniques to Detect the Malware using Deep Learning Approach

Jeyalakshmi

Jayapriya

Nallaperumal

2022

2022 6th International Conference on Trends in Electronics and Informatics (ICOEI)

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A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysis

Cited by 3 publications

References 23 publications

A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System

A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System

An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

A Study of Malware Datasets and Techniques to Detect the Malware using Deep Learning Approach

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