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Malware Classification Using Transfer Learning [article]

Hikmat Farhat, Veronica Rammouz
<span title="2021-07-29">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Rapid malware classification is an important tools to combat that threat. One of the successful approaches to classification is based on malware images and deep learning.  ...  In this work we perform experiments on multiple well known, pre-trained, deep network architectures in the context of transfer learning.  ...  In this paper we investigate the efficacy of transfer learning for malware classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.13743v1">arXiv:2107.13743v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qi6yxnxgwbctng3irw77xa5mei">fatcat:qi6yxnxgwbctng3irw77xa5mei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210804015837/https://arxiv.org/pdf/2107.13743v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/70/86/70860747b9043ca97ce4a74178a3631ac91c495f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.13743v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

USING TRANSFER LEARNING FOR MALWARE CLASSIFICATION

B. Prima, M. Bouhorma
<span title="2020-11-23">2020</span> <i title="Copernicus GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i74shj7anreaxjo327fokng66m" style="color: black;">The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</a> </i> &nbsp;
In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets.  ...  Motivated by this success, we propose a CNN-based architecture for malware classification.  ...  So we can deduce that the transfer learning technique can be used for the classification of malwares.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-343-2020">doi:10.5194/isprs-archives-xliv-4-w3-2020-343-2020</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/45ytbxvtcjeltgmofzpkhcsnvy">fatcat:45ytbxvtcjeltgmofzpkhcsnvy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201127011941/https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-4-W3-2020/343/2020/isprs-archives-XLIV-4-W3-2020-343-2020.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b9/90/b990761bcea7507ec3b7b62d40dd817ac60816e4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/isprs-archives-xliv-4-w3-2020-343-2020"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

IVMCT: Image Visualization based Multiclass Malware Classification using Transfer Learning

Manish Goyal, Raman Kumar
<span title="2022-03-10">2022</span> <i title="Auricle Technologies, Pvt., Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/emkg5ysvsjdndhcbrvmd3nu2nu" style="color: black;">Mathematical Statistician and Engineering Applications</a> </i> &nbsp;
In the present study, an IVMCT framework is introduced which classifies malware using transfer learning.  ...  Traditionally signature-based techniques are used with machine learning algorithms to detect malware that is unable to catch its variants.  ...  Architecture of IVMCT The classification of malware is a challenging task. We have used a transfer learning-based hybrid model to classify malware.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17762/msea.v71i2.65">doi:10.17762/msea.v71i2.65</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3546nwghkbbizpjcevmmo7yzua">fatcat:3546nwghkbbizpjcevmmo7yzua</a> </span>
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Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models

Walid El-Shafai, Iman Almomani, Aala AlKhayer
<span title="2021-07-13">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
deep learning (DL)-based malware multi-classification approaches tested on the same malware dataset.  ...  This research aims to exploit the significant advantages of Transfer Learning (TL) and Fine-Tuning (FT) methods to introduce efficient malware detection in the context of imbalanced families without the  ...  However, to enhance the malware classification and detection, the gained knowledge out of CNNs can be transferred to a different learning task [11] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11146446">doi:10.3390/app11146446</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ayvvyjwatvboxgholhzxlucg7u">fatcat:ayvvyjwatvboxgholhzxlucg7u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210722050847/https://res.mdpi.com/d_attachment/applsci/applsci-11-06446/article_deploy/applsci-11-06446.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2e/ad/2ead39515eb6a19032231eb68a555ade50753c7c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11146446"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Deep Transfer Learning for Static Malware Classification [article]

Li Chen
<span title="2018-12-18">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose to apply deep transfer learning from computer vision to static malware classification.  ...  In the transfer learning scheme, we borrow knowledge from natural images or objects and apply to the target domain of static malware detection.  ...  The contributions of our paper are summarized as below: • We propose using transfer learning from computer vision to apply for static malware classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.07606v1">arXiv:1812.07606v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ukss5eezjrfqljd7n2i6ftumf4">fatcat:ukss5eezjrfqljd7n2i6ftumf4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200825180143/https://arxiv.org/pdf/1812.07606v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/82/54/8254d75426ce92e52a87507e14350b9ffbcc6b18.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.07606v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions [article]

Li Chen
<span title="2019-04-03">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Employing transfer learning of deep neural networks effective for large scale image classification to malware classification demonstrates superior classification efficacy compared with classical machine  ...  Via understanding the attack surfaces of machine learning models used for malware detection, we can greatly improve the robustness of the algorithms to combat malware adversaries in the wild.  ...  In [1] , I propose deep transfer learning for static malware classification, where I augment the grey-scale malware images into RGB-channels, and apply transfer learning on the malware dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.10504v1">arXiv:1904.10504v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qqu2nyn2wbfaroltdhqmu7lpv4">fatcat:qqu2nyn2wbfaroltdhqmu7lpv4</a> </span>
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MALWARE DETECTION SYSTEM BASED ON DEEP LEARNING TECHNIQUE

Zahraa Z. Edie, Ammar D. Jasim
<span title="2021-12-15">2021</span> <i title="College of Information Engineering - Al-Nahrain University"> Iraqi Journal of Information and Communications Technology </i> &nbsp;
In this paper, we propose a malware classification and detection framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets, we applied  ...  a deep Convolutional Neural Network (CNN) based on Xception model to perform malware image classification.  ...  As described, our method takes advantage of the transfer learning process to train our DCNN model on the malware classification task, we first use the Xception transferred convolutional layers to extract  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31987/ijict.1.1.177">doi:10.31987/ijict.1.1.177</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sknkjj35rbdjpmdr37hvoh6zmi">fatcat:sknkjj35rbdjpmdr37hvoh6zmi</a> </span>
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Hierarchical learning for automated malware classification

Shayok Chakraborty, Jack W. Stokes, Lin Xiao, Dengyong Zhou, Mady Marinescu, Anil Thomas
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5fjspjtqujectfcv6khzqy3mvq" style="color: black;">MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)</a> </i> &nbsp;
While there has been substantial research in automated malware classification, the aforementioned hierarchical structure, which can provide additional information to the classification models, has been  ...  In this paper, we propose the novel idea and study the performance of employing hierarchical learning algorithms for automated classification of malicious files.  ...  We therefore use the SVM to isolate the improvement of hierarchical learning for malware classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/milcom.2017.8170758">doi:10.1109/milcom.2017.8170758</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/milcom/ChakrabortySXZM17.html">dblp:conf/milcom/ChakrabortySXZM17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jydc5uok5reztlypmmzq3ll7s4">fatcat:jydc5uok5reztlypmmzq3ll7s4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321155128/https://www.microsoft.com/en-us/research/uploads/prod/2018/05/ChakrabortyMilcom2017-5b0d8d4ccf512.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9a/00/9a00218abe5e77d4b6bdd210b32ee156897a20b5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/milcom.2017.8170758"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Malware Classification Using Deep Boosted Learning [article]

Muhammad Asam, Saddam Hussain Khan, Tauseef Jamal, Umme Zahoora, Asifullah Khan
<span title="2021-07-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We also introduced the concept of transfer learning in a customized CNN architecture based malware classification framework through fine-tuning.  ...  Experimental comparisons were conducted by employing innovative, customized CNN, trained from scratch and fine-tuning the customized CNN using transfer learning.  ...  In case the labelled data in the target domain is limited, the concept of data augmentation and transfer learning is useful [33] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.04008v1">arXiv:2107.04008v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vwvybhmlgfbpfavzrb4oxqw2da">fatcat:vwvybhmlgfbpfavzrb4oxqw2da</a> </span>
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A Malware Detection Method of Code Texture Visualization Based on an Improved Faster RCNN Combining Transfer Learning

Yuntao Zhao, Wenjie Cui, Shengnan Geng, Bo Bo, Yongxin Feng, Wenbo Zhang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Experiment 2: An Improved Faster RCNN Combining Transfer Learning We used the ImageNet image set to pre-train Faster RCNN, and then transferred the model to a malware classification.  ...  MALWARE DETECTION METHOD BASED ON TRANSFE LEARNING 1) TRANSFER LEARNING Transfer learning is an important method of machine learning.  ...  We also compared three traditional malicious code classification methods. The improved method of Faster RCNN with transfer learning is better than other methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3022722">doi:10.1109/access.2020.3022722</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/25pitdb6grepncay32v4phmuvy">fatcat:25pitdb6grepncay32v4phmuvy</a> </span>
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Toward Identifying APT Malware through API System Calls

Chaoxian Wei, Qiang Li, Dong Guo, Xiangyu Meng, Angel M. Del Rey
<span title="2021-12-09">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sdme5pnua5auzcsjgqmqefb66m" style="color: black;">Security and Communication Networks</a> </i> &nbsp;
Thus, we used transfer learning to perform multiple classifications of the APT family.  ...  and transfer learning by collecting malware used in several famous APT groups in public.  ...  Figure 14 :Figure 15 : 1415 Figure 14: Whether to use transfer learning comparison for multiple classifications.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/8077220">doi:10.1155/2021/8077220</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3ycfzgdhajfeldpkjp7ccertoe">fatcat:3ycfzgdhajfeldpkjp7ccertoe</a> </span>
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To believe or not to believe: Validating explanation fidelity for dynamic malware analysis [article]

Li Chen, Carter Yagemann, Evan Downing
<span title="2019-04-30">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
For both case studies, we first train deep learning models via transfer learning on malware images, demonstrate high classification effectiveness, apply an explanation method on the images, and correlate  ...  In this work, via two case studies of dynamic malware classification, we extend the local interpretable model-agnostic explanation algorithm to explain image-based dynamic malware classification and examine  ...  domain expertise, on dynamic image-based malware classification. • We use deep transfer learning on dynamic malware images generated from instruction sequence predictions, API existence, API sequence,  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.00122v1">arXiv:1905.00122v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pxnv4gvxdnfjpjeod2paodnrtm">fatcat:pxnv4gvxdnfjpjeod2paodnrtm</a> </span>
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Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning [article]

Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B.S. Manjunath
<span title="2021-01-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A shallow neural network is trained for classification, and its accuracy is compared with deep-network architectures such as ResNet that are trained using transfer learning.  ...  We propose a novel method to detect and visualize malware through image classification.  ...  ResNet models (as shown in Figure 3b ) to effectively use transfer learning-based classification technique that is well known in literature.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.10578v1">arXiv:2101.10578v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zh5cxopdgjghzd7n5gfl7yfwja">fatcat:zh5cxopdgjghzd7n5gfl7yfwja</a> </span>
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Malware Detection Using Deep Transferred Generative Adversarial Networks [chapter]

Jin-Young Kim, Seok-Jun Bu, Sung-Bae Cho
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper, we propose a transferred generative adversarial network (tGAN) for automatic classification and detection of the zero-day attack.  ...  The proposed model gets the best performance compared with the conventional machine learning algorithms.  ...  is generated better than when the transfer learning is not used.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-70087-8_58">doi:10.1007/978-3-319-70087-8_58</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/drrlwp37yjewnpiqvkxmn6wkem">fatcat:drrlwp37yjewnpiqvkxmn6wkem</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190220135745/http://pdfs.semanticscholar.org/249f/b17f09f9d4b45fd74403962326b7788ce8c3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/24/9f/249fb17f09f9d4b45fd74403962326b7788ce8c3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-70087-8_58"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection [article]

Li Chen, Salmin Sultana, Ravi Sahita
<span title="2018-01-08">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering.  ...  The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution.  ...  Existing deep learning approaches use static or dynamic features to construct neural networks for malware detection or classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.02318v1">arXiv:1801.02318v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5lnx2pj7sjggnbdmbliatqizla">fatcat:5lnx2pj7sjggnbdmbliatqizla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191020191853/https://arxiv.org/pdf/1801.02318v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b3/e0/b3e04836a8f1a1efda32d15296ff9435ab8afd86.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.02318v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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