{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T15:36:41Z","timestamp":1764949001598,"version":"3.46.0"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The Internet has been progressively more integrated into daily life through its evolutionary stages, ranging from web1.0 to the current development of web3.0. These continued integrations broaden the attack surface that cybercriminals aim to exploit. The prevalence of cybercrimes, particularly malware attacks, has become increasingly sophisticated and made more accessible through dark web marketplaces. Including artificial intelligence (AI) within anti-virus solutions has challenged the traditional dichotomy of malware detection schemes, offering more accurate and holistic detection capabilities. Research has shown that transforming malware files into textured images offers resistance to obfuscation and the potential to detect zero days. This paper explores the application of image quality assessment (IQA) techniques in enhancing visual malware dataset curation. We propose a novel framework that applies a no-reference IQA algorithm to evaluate current datasets and offer guidance in future dataset curation. Using multiple popular datasets, our evaluation demonstrates that the proposed MalScore framework effectively differentiates dataset quality\u2014for example, MalNet Tiny achieves the highest score of 95%, while the NARAD malicious-image subset scores 50%. Additionally, BRISQUE was the only IQA algorithm to exhibit a strong linear sensitivity to blur levels across datasets. These results highlight the practical utility of MalScore in assessing and ranking visual malware datasets and lay the groundwork for uniting IQA and visual malware detection in future research.<\/jats:p>","DOI":"10.3390\/fi17120554","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T15:11:24Z","timestamp":1764947484000},"page":"554","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MalScore: A Quality Assessment Framework for Visual Malware Datasets Using No-Reference Image Quality Metrics"],"prefix":"10.3390","volume":"17","author":[{"given":"Jakub","family":"Czaplicki","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, 113 W 60th Street, New York, NY 10023, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9701-5505","authenticated-orcid":false,"given":"Mohamed","family":"Rahouti","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, 113 W 60th Street, New York, NY 10023, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-6062","authenticated-orcid":false,"given":"Abdellah","family":"Chehri","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8952-1499","authenticated-orcid":false,"given":"Thaier","family":"Hayajneh","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, 113 W 60th Street, New York, NY 10023, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6098","DOI":"10.1109\/TCSS.2024.3382582","article-title":"Toward Web3 applications: Easing the access and transition","volume":"11","author":"Yu","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3351159","article-title":"The economics of cybercrime: The role of broadband and socioeconomic status","volume":"10","author":"Park","year":"2019","journal-title":"ACM Trans. Manag. Inf. Syst. (TMIS)"},{"key":"ref_3","unstructured":"Georgoulias, D., Yaben, R., and Vasilomanolakis, E. (September, January 29). Cheaper than you thought? a dive into the darkweb market of cyber-crime products. Proceedings of the 18th International Conference on Availability, Reliability and Security, Benevento, Italy."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"156285","DOI":"10.1109\/ACCESS.2024.3485593","article-title":"DeepImageDroid: A Hybrid Framework Leveraging Visual Transformers and Convolutional Neural Networks for Robust Android Malware Detection","volume":"12","author":"Rahouti","year":"2024","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"800","DOI":"10.3390\/jcp2040041","article-title":"A survey of the recent trends in deep learning based malware detection","volume":"2","author":"Tayyab","year":"2022","journal-title":"J. Cybersecur. Priv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nataraj, L., Karthikeyan, S., Jacob, G., and Manjunath, B.S. (2011, January 20). Malware images: Visualization and automatic classification. Proceedings of the 8th International Symposium on Visualization for Cyber Security, Pittsburgh, PA, USA.","DOI":"10.1145\/2016904.2016908"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"87936","DOI":"10.1109\/ACCESS.2021.3089586","article-title":"A new malware classification framework based on deep learning algorithms","volume":"9","author":"Aslan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/1687-5281-2014-40","article-title":"No-reference image and video quality assessment: A classification and review of recent approaches","volume":"2014","author":"Shahid","year":"2014","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., and Bovik, A. (2020, January 13\u201319). From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00363"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, K., and Kim, D.S. (2019, January 5\u20138). Malware detection with malware images using deep learning techniques. Proceedings of the 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications\/13th IEEE International Conference on Big Data Science and Engineering (TrustCom\/BigDataSE), Rotorua, New Zealand.","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00022"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"25696","DOI":"10.1109\/ACCESS.2022.3155695","article-title":"A malware detection approach using autoencoder in deep learning","volume":"10","author":"Xing","year":"2022","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, F., Al Hamadi, H., and Damiani, E. (2022, January 17\u201320). A visualized malware detection framework with CNN and conditional GAN. Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan.","DOI":"10.1109\/BigData55660.2022.10020534"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kasarapu, S., Shukla, S., Hassan, R., Sasan, A., Homayoun, H., and PD, S.M. (2022, January 6\u20138). Cad-fsl: Code-aware data generation based few-shot learning for efficient malware detection. Proceedings of the Great Lakes Symposium on VLSI 2022, Irvine, CA, USA.","DOI":"10.1145\/3526241.3530825"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Phan, T.D., Duc Luong, T., Hoang Quoc An, N., Nguyen Huu, Q., Nghi, H.K., and Pham, V.H. (2022, January 1\u20133). Leveraging reinforcement learning and generative adversarial networks to craft mutants of windows malware against black-box malware detectors. Proceedings of the 11th International Symposium on Information and Communication Technology, Hanoi, Vietnam.","DOI":"10.1145\/3568562.3568636"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s11416-023-00465-2","article-title":"Generative adversarial networks and image-based malware classification","volume":"19","author":"Nguyen","year":"2023","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211301","DOI":"10.1007\/s11432-019-2757-1","article-title":"Perceptual image quality assessment: A survey","volume":"63","author":"Zhai","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nataraj, L., Yegneswaran, V., Porras, P., and Zhang, J. (2011, January 21). A comparative assessment of malware classification using binary texture analysis and dynamic analysis. Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, Chicago, IL, USA.","DOI":"10.1145\/2046684.2046689"},{"key":"ref_18","unstructured":"Dinu, C. (2023). GitHub Repository for Malware Classification Using Convolutional Neural Networks, GitHub."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bozkir, A.S., Cankaya, A.O., and Aydos, M. (2019, January 24\u201326). Utilization and comparision of convolutional neural networks in malware recognition. Proceedings of the 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey.","DOI":"10.1109\/SIU.2019.8806511"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Freitas, S., Duggal, R., and Chau, D.H. (2022, January 17\u201321). MalNet: A large-scale image database of malicious software. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA.","DOI":"10.1145\/3511808.3557533"},{"key":"ref_21","unstructured":"Saridou, B., Rose, J., Shiaeles, S., and Papadopoulos, B. (2021). 48,240 Malware Samples and Binary Visualisation Images for Machine Learning Anomaly Detection, IEEE."},{"key":"ref_22","unstructured":"Fields, M. (2025, November 10). Malware as Images. Available online: https:\/\/www.kaggle.com\/datasets\/matthewfields\/malware-as-images."},{"key":"ref_23","unstructured":"Nativ, Y., and Lahad Ludar, S.S. (2025, November 10). theZoo\u2014A Live Malware Repository. [Online]. Available online: https:\/\/github.com\/ytisf\/theZoo."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","unstructured":"Wang, J., Chan, K.C., and Loy, C.C. (2023, January 7\u201314). Exploring clip for assessing the look and feel of images. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kang, L., Ye, P., Li, Y., and Doermann, D. (2014, January 23\u201328). Convolutional neural networks for no-reference image quality assessment. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.224"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TCSVT.2018.2886771","article-title":"Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network","volume":"30","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","unstructured":"Czaplicki, J. (2025, November 10). Capstone-Project. [Online]. Available online: https:\/\/github.com\/czaplickijakub\/Capstone-Project."},{"key":"ref_30","unstructured":"Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., and Ahmadi, M. (2018). Microsoft malware classification challenge. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bruzzese, R. (2024, January 2\u20135). Building visual malware dataset using virusshare data and comparing machine learning baseline model to CoAtNet for malware classification. Proceedings of the 2024 16th International Conference on Machine Learning and Computing, Shenzhen, China.","DOI":"10.1145\/3651671.3651735"},{"key":"ref_32","unstructured":"Makkawy, S.J., De Lucia, M.J., and Barner, K.E. (2025). MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification. arXiv."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/12\/554\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T15:33:49Z","timestamp":1764948829000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/12\/554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["fi17120554"],"URL":"https:\/\/doi.org\/10.3390\/fi17120554","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,1]]}}}