{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:10:35Z","timestamp":1774894235815,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806171"],"award-info":[{"award-number":["61806171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021RC15"],"award-info":[{"award-number":["2021RC15"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022QYJ06"],"award-info":[{"award-number":["2022QYJ06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Y2022185"],"award-info":[{"award-number":["Y2022185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan University of Science &amp; Engineering Talent Project","award":["61806171"],"award-info":[{"award-number":["61806171"]}]},{"name":"Sichuan University of Science &amp; Engineering Talent Project","award":["2021RC15"],"award-info":[{"award-number":["2021RC15"]}]},{"name":"Sichuan University of Science &amp; Engineering Talent Project","award":["2022QYJ06"],"award-info":[{"award-number":["2022QYJ06"]}]},{"name":"Sichuan University of Science &amp; Engineering Talent Project","award":["Y2022185"],"award-info":[{"award-number":["Y2022185"]}]},{"name":"Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computing of Sichuan Province Universities on Bridge Inspection and Engineering","award":["61806171"],"award-info":[{"award-number":["61806171"]}]},{"name":"Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computing of Sichuan Province Universities on Bridge Inspection and Engineering","award":["2021RC15"],"award-info":[{"award-number":["2021RC15"]}]},{"name":"Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computing of Sichuan Province Universities on Bridge Inspection and Engineering","award":["2022QYJ06"],"award-info":[{"award-number":["2022QYJ06"]}]},{"name":"Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computing of Sichuan Province Universities on Bridge Inspection and Engineering","award":["Y2022185"],"award-info":[{"award-number":["Y2022185"]}]},{"name":"Sichuan University of Science &amp; Engineering Graduate Student Innovation Fund","award":["61806171"],"award-info":[{"award-number":["61806171"]}]},{"name":"Sichuan University of Science &amp; Engineering Graduate Student Innovation Fund","award":["2021RC15"],"award-info":[{"award-number":["2021RC15"]}]},{"name":"Sichuan University of Science &amp; Engineering Graduate Student Innovation Fund","award":["2022QYJ06"],"award-info":[{"award-number":["2022QYJ06"]}]},{"name":"Sichuan University of Science &amp; Engineering Graduate Student Innovation Fund","award":["Y2022185"],"award-info":[{"award-number":["Y2022185"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the challenges of weak model generalization and limited model capacity adaptation in traditional malware detection methods, this article presents a novel malware detection approach based on stacked depthwise separable convolutions and self-attention, termed CoAtNet. This method combines the strengths of the self-attention module\u2019s robust model adaptation and the convolutional networks\u2019 powerful generalization abilities. The initial step involves transforming the malicious code into grayscale images. These images are subsequently processed using a detection model that employs stacked depthwise separable convolutions and an attention mechanism. This model effectively recognizes and classifies the images, automatically extracting essential features from malicious software images. The effectiveness of the method was validated through comparative experiments using both the Malimg dataset and the augmented Blended+ dataset. The approach\u2019s performance was evaluated against popular models, including XceptionNet, EfficientNetB0, ResNet50, VGG16, DenseNet169, and InceptionResNetV2. The experimental results highlight that the model surpasses other malware detection models in terms of accuracy and generalization ability. In conclusion, the proposed method addresses the limitations of traditional malware detection approaches by leveraging stacked depthwise separable convolutions and self-attention. Comprehensive experiments demonstrate its superior performance compared to existing models. This research contributes to advancing the field of malware detection and provides a promising solution for enhanced accuracy and robustness.<\/jats:p>","DOI":"10.3390\/s23167084","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:52:40Z","timestamp":1691664760000},"page":"7084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Malicious Code Detection Method Based on Stacked Depthwise Separable Convolutions and Attention Mechanism"],"prefix":"10.3390","volume":"23","author":[{"given":"Hong","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin 644002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin 644002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaolian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin 644002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin 644002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guotao","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin 644002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","unstructured":"(2023, July 03). Total Amount of Malware and PUA. Available online: https:\/\/portal.av-atlas.org\/malware."},{"key":"ref_2","unstructured":"(2023, July 03). IT Threat Evolution Q1 2023. Mobile Statistics. Available online: https:\/\/securelist.com\/it-threat-evolution-q1-2023-mobile-statistics\/109893\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jss.2014.10.031","article-title":"Profiling and classifying the behavior of malicious codes","volume":"100","author":"Alazab","year":"2015","journal-title":"J. Syst. Softw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1728303","DOI":"10.1155\/2018\/1728303","article-title":"Use of data visualisation for zero-day malware detection","volume":"2018","author":"Venkatraman","year":"2018","journal-title":"Secur. Commun. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6349","DOI":"10.1007\/s12652-022-04407-6","article-title":"Intrusion detection for the internet of things (IoT) based on the emperor penguin colony optimization algorithm","volume":"14","author":"Alweshah","year":"2023","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alzubi, O.A., Alzubi, J.A., Alazab, M., Alrabea, A., Awajan, A., and Qiqieh, I. (2022). Optimized Machine Learning-Based Intrusion Detection System for Fog and Edge Computing Environment. Electronics, 11.","DOI":"10.3390\/electronics11193007"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shafin, S.S., Karmakar, G., and Mareels, I. (2023). Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications. Sensors, 23.","DOI":"10.3390\/s23115348"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.future.2022.12.034","article-title":"HCL-Classifier: CNN and LSTM based hybrid malware classifier for Internet of Things (IoT)","volume":"142","author":"Abdullah","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e7679","DOI":"10.1002\/cpe.7679","article-title":"An innovative malware detection methodology employing the amalgamation of stacked BiLSTM and CNN+LSTM-based classification networks with the assistance of Mayfly metaheuristic optimization algorithm in cyber-attack","volume":"35","author":"Srinivasan","year":"2023","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1007\/s10586-022-03686-0","article-title":"Fusion of deep learning based cyberattack detection and classification model for intelligent systems","volume":"26","author":"Alzubi","year":"2023","journal-title":"Cluster Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_12","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 23\u201328). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Columbus, OH, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_15","first-page":"1142","article-title":"Malicious code detection based on multi-channel image deep learning","volume":"4","author":"Jiang","year":"2021","journal-title":"J. Comput. Appl."},{"key":"ref_16","first-page":"162","article-title":"Classification of malicious code variants based on VGGNet","volume":"1","author":"Wang","year":"2020","journal-title":"J. Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1109\/TSE.2017.2655046","article-title":"Semantics-Based Obfuscation-Resilient Binary Code Similarity Comparison with Applications to Software and Algorithm Plagiarism Detecion","volume":"43","author":"Luo","year":"2017","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_18","first-page":"2339","article-title":"A Method of Extracting Malware Features Based on Probabilistic Topic Model","volume":"56","author":"Liu","year":"2019","journal-title":"J. Comput. Res. Dev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.comcom.2019.01.003","article-title":"Analysis and classification of context-based malware behavior","volume":"136","author":"Alaeiyan","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1049\/iet-ifs.2017.0430","article-title":"Malware classification based on API calls and behaviour analysis","volume":"12","author":"Acarman","year":"2018","journal-title":"IET Inf. Secur."},{"key":"ref_21","first-page":"1","article-title":"Dynamic Malware Analysis in the Modern Era\u2014A State of the Art Survey","volume":"52","author":"Nissim","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Taher, F., AlFandi, O., AI-kfairy, M., AI Hamadi, H., and Alrabaee, S. (2023). DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection. Appl. Sci., 13.","DOI":"10.20944\/preprints202305.0333.v1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3073559","article-title":"A Survey on Malware Detection Using Data Mining Techniques","volume":"50","author":"Ye","year":"2017","journal-title":"ACM Comput. Surv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"148853","DOI":"10.1109\/ACCESS.2019.2946482","article-title":"A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"166630","DOI":"10.1109\/ACCESS.2020.3022722","article-title":"A Malware Detection Method of Code Texture Visualization Based on an Improved Faster RCNN Combining Transfer Learning","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/MSP.2015.2507185","article-title":"SPAM: Signal Precessing to Analyze Malware [Applications Corner]","volume":"33","author":"Nataraj","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_27","first-page":"366","article-title":"A Survey of Malware Detection Techniques based on Machine Learning","volume":"10","author":"Hajraoui","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.compeleceng.2019.03.015","article-title":"Identification of malicious code variants based on image visualization","volume":"76","author":"Naeem","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"16946","DOI":"10.1109\/JIOT.2021.3075694","article-title":"CNN-Based Malware Variants Detection Method for Internet of Things","volume":"8","author":"Li","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sudhakar, and Kumar, S (2021). MCFT-CNN: Malware classification with finetune convolution neural networks using traditional and transfer learning in Internet of Things. Future Gener. Comput. Syst., 125, 334\u2013351.","DOI":"10.1016\/j.future.2021.06.029"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101748","DOI":"10.1016\/j.cose.2020.101748","article-title":"Image-Based malware classification using ensemble of CNN architectures (IMCEC)","volume":"92","author":"Vasan","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_33","unstructured":"Tan, M., and Le, Q.V. (2019, January 10\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Maaten, L. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3187","DOI":"10.1109\/TII.2018.2822680","article-title":"Detection of Malicious Code Variants Based on Deep Learning","volume":"14","author":"Cui","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_37","first-page":"377","article-title":"A hybrid deep learning image-based analysis for effective malware detection","volume":"47","author":"Venkatraman","year":"2019","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107138","DOI":"10.1016\/j.comnet.2020.107138","article-title":"IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture","volume":"171","author":"Vasan","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101895","DOI":"10.1016\/j.cose.2020.101895","article-title":"Multiclass malware classification via first- and secind-order texture statistics","volume":"97","author":"Verma","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_40","first-page":"6658842","article-title":"A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet","volume":"2021","author":"Wang","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1070586","DOI":"10.1155\/2021\/1070586","article-title":"Malicious Code Variant Identification Based on Multiscale Frature Fusion CNNs","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Intell. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7084\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:30:33Z","timestamp":1760128233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7084"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":41,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167084"],"URL":"https:\/\/doi.org\/10.3390\/s23167084","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]}}}