{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:10:00Z","timestamp":1773799800485,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Sichuan Local technological development Program","award":["24YRGZN0010"],"award-info":[{"award-number":["24YRGZN0010"]}]},{"name":"Tibet Science and Technology Program","award":["XZ202401YD0023"],"award-info":[{"award-number":["XZ202401YD0023"]}]},{"name":"Supported by The Innovation Fund of Postgraduate, Sichuan University of Science & Engineering.","award":["Y2024131"],"award-info":[{"award-number":["Y2024131"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10586-024-04865-x","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T01:29:20Z","timestamp":1740446960000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["FEdroid: a lightweight and interpretable machine learning-based android malware detection system"],"prefix":"10.1007","volume":"28","author":[{"given":"Hong","family":"Huang","sequence":"first","affiliation":[]},{"given":"Weitao","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yinghang","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Wengang","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Yunfei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"4865_CR1","unstructured":"Wikipedia contributors: Android (operating system)\u2014Wikipedia, The Free Encyclopedia. Available at https:\/\/en.wikipedia.org\/wiki\/Android_(operating_system) (2024)"},{"key":"4865_CR2","unstructured":"Josh, M.: iPhone vs Android User Stats (2024 Data). Available at https:\/\/explodingtopics.com\/blog\/iphone-android-users (2024)"},{"key":"4865_CR3","unstructured":"ANTON, K.: IT threat evolution in Q1 2024. Mobile statistics. Available at https:\/\/securelist.com\/it-threat-evolution-q1-2024-mobile-statistics\/112750\/ (2024)"},{"key":"4865_CR4","unstructured":"David, C.: Google Play Store Statistics (2024). Available at https:\/\/www.businessofapps.com\/data\/google-play-statistics\/ (2024)"},{"key":"4865_CR5","unstructured":"Alanna, T.: Google Play malware clocks up more than 600 million downloads in 2023. Available at https:\/\/usa.kaspersky.com\/blog\/malware-in-google-play-2023\/29356\/ (2024)"},{"key":"4865_CR6","doi-asserted-by":"publisher","unstructured":"Akour, M., Alsmadi, I., Alazab, M.: The malware detection challenge of accuracy. In: 2016 2nd International Conference on Open Source Software Computing (OSSCOM), pp. 1\u20136 (2016). https:\/\/doi.org\/10.1109\/OSSCOM.2016.7863676","DOI":"10.1109\/OSSCOM.2016.7863676"},{"issue":"2","key":"4865_CR7","doi-asserted-by":"publisher","first-page":"235","DOI":"10.5755\/j01.itc.48.2.21457","volume":"48","author":"AT Kabakus","year":"2019","unstructured":"Kabakus, A.T.: What static analysis can utmost offer for android malware detection. Inf. Technol. Control 48(2), 235\u2013249 (2019)","journal-title":"Inf. Technol. Control"},{"key":"4865_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.05.026","volume":"535","author":"S Jeon","year":"2020","unstructured":"Jeon, S., Moon, J.: Malware-detection method with a convolutional recurrent neural network using opcode sequences. Inf. Sci. 535, 1\u201315 (2020)","journal-title":"Inf. Sci."},{"issue":"4","key":"4865_CR9","doi-asserted-by":"publisher","first-page":"608","DOI":"10.3390\/math10040608","volume":"10","author":"WC Lin","year":"2022","unstructured":"Lin, W.C., Yeh, Y.R.: Efficient malware classification by binary sequences with one-dimensional convolutional neural networks. Mathematics 10(4), 608 (2022)","journal-title":"Mathematics"},{"issue":"2","key":"4865_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3313391","volume":"22","author":"L Onwuzurike","year":"2019","unstructured":"Onwuzurike, L., Mariconti, E., Andriotis, P., Cristofaro, E.D., Ross, G., Stringhini, G.: MaMaDroid: detecting android malware by building Markov chains of behavioral models (extended version). ACM Trans. Privacy Secur. (TOPS) 22(2), 1\u201334 (2019)","journal-title":"ACM Trans. Privacy Secur. (TOPS)"},{"key":"4865_CR11","doi-asserted-by":"crossref","unstructured":"Nataraj, L., Karthikeyan, S., Jacob, G., Manjunath, B.S.: Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security, pp. 1\u20137 (2011)","DOI":"10.1145\/2016904.2016908"},{"key":"4865_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107138","volume":"171","author":"D Vasan","year":"2020","unstructured":"Vasan, D., Alazab, M., Wassan, S., Naeem, H., Safaei, B., Zheng, Q.: IMCFN: image-based malware classification using fine-tuned convolutional neural network architecture. Comput. Netw. 171, 107138 (2020)","journal-title":"Comput. Netw."},{"key":"4865_CR13","doi-asserted-by":"crossref","unstructured":"Daoudi, N., Samhi, J., Kabore, A.K., Allix, T.F. K.and\u00a0Bissyand\u00e9, Klein, J.: Dexray: a simple, yet effective deep learning approach to android malware detection based on image representation of bytecode. In: Deployable Machine Learning for Security Defense: Second International Workshop, MLHat 2021, Virtual Event, August 15, 2021, Proceedings 2, pp. 81\u2013106. Springer, Berlin (2021)","DOI":"10.1007\/978-3-030-87839-9_4"},{"key":"4865_CR14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.cose.2016.11.007","volume":"65","author":"A Feizollah","year":"2017","unstructured":"Feizollah, A., Anuar, N.B., Salleh, R., Suarez-Tangil, G., Furnell, S.: Androdialysis: analysis of android intent effectiveness in malware detection. Comput. Secur. 65, 121\u2013134 (2017)","journal-title":"Comput. Secur."},{"key":"4865_CR15","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1016\/j.compeleceng.2017.11.028","volume":"69","author":"ZU Rehman","year":"2018","unstructured":"Rehman, Z.U., Khan, S.N., Muhammad, K., Lee, J.W., Lv, Z., Baik, S.W., Shah, P.A., Awan, K., Mehmood, I.: Machine learning-assisted signature and heuristic-based detection of malwares in android devices. Comput. Electr. Eng. 69, 828\u2013841 (2018)","journal-title":"Comput. Electr. Eng."},{"issue":"18","key":"4865_CR16","doi-asserted-by":"publisher","first-page":"11499","DOI":"10.1007\/s00521-021-05816-y","volume":"33","author":"K Bakour","year":"2021","unstructured":"Bakour, K., \u00dcnver, H.M.: Deepvisdroid: android malware detection by hybridizing image-based features with deep learning techniques. Neural Comput. Appl. 33(18), 11499\u201311516 (2021)","journal-title":"Neural Comput. Appl."},{"key":"4865_CR17","doi-asserted-by":"publisher","first-page":"85127","DOI":"10.1109\/ACCESS.2022.3198072","volume":"10","author":"T Van Dao","year":"2022","unstructured":"Van Dao, T., Sato, H., Kubo, M.: An attention mechanism for combination of CNN and VAE for image-based malware classification. IEEE Access 10, 85127\u201385136 (2022)","journal-title":"IEEE Access"},{"issue":"7","key":"4865_CR18","doi-asserted-by":"publisher","first-page":"8170","DOI":"10.1016\/j.eswa.2010.12.160","volume":"38","author":"A Arauzo-Azofra","year":"2011","unstructured":"Arauzo-Azofra, A., Aznarte, J.L., Ben\u00edtez, J.M.: Empirical study of feature selection methods based on individual feature evaluation for classification problems. Expert Syst. Appl. 38(7), 8170\u20138177 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"4865_CR19","doi-asserted-by":"publisher","first-page":"86","DOI":"10.37934\/araset.33.3.8697","volume":"33","author":"NAM Ariffin","year":"2023","unstructured":"Ariffin, N.A.M., Casinto, H.P.: Android malware detection using permission based static analysis. J. Adv. Res. Appl. Sci. Eng. Technol. 33(3), 86\u201397 (2023)","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"4865_CR20","doi-asserted-by":"crossref","unstructured":"\u015eah\u0131n, D.\u00d6., Kural, O.E., Akleylek, S., Kili\u00e7, E.: New results on permission based static analysis for android malware. In: 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1\u20134. IEEE (2018)","DOI":"10.1109\/ISDFS.2018.8355377"},{"issue":"15","key":"4865_CR21","doi-asserted-by":"publisher","first-page":"11373","DOI":"10.1007\/s00521-023-08303-8","volume":"35","author":"M Chaudhary","year":"2023","unstructured":"Chaudhary, M., Masood, A.: Realmalsol: real-time optimized model for android malware detection using efficient neural networks and model quantization. Neural Comput. Appl. 35(15), 11373\u201311388 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"4865_CR22","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10207-022-00626-2","volume":"22","author":"PG Balikcioglu","year":"2023","unstructured":"Balikcioglu, P.G., Sirlanci, M., Kucuk, O.A., Ulukapi, B., Turkmen, R.K., Acarturk, C.: Malicious code detection in android: the role of sequence characteristics and disassembling methods. Int. J. Inf. Secur. 22(1), 107\u2013118 (2023)","journal-title":"Int. J. Inf. Secur."},{"key":"4865_CR23","doi-asserted-by":"crossref","unstructured":"Liu, H., Gong, L.Y., Mo, X.L., Dong, G.Z., Yu, J.: LTAChecker: lightweight android malware detection based on Dalvik opcode sequences using attention temporal networks. IEEE Internet Things J. (2024)","DOI":"10.1109\/JIOT.2024.3394555"},{"issue":"11","key":"4865_CR24","doi-asserted-by":"publisher","first-page":"4772","DOI":"10.3390\/app14114772","volume":"14","author":"\u00d6 Kiraz","year":"2024","unstructured":"Kiraz, \u00d6., Do\u011fru, \u0130A.: Visualising static features and classifying android malware using a convolutional neural network approach. Appl. Sci. 14(11), 4772 (2024)","journal-title":"Appl. Sci."},{"key":"4865_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119593","volume":"218","author":"H Zhu","year":"2023","unstructured":"Zhu, H., Wei, H., Wang, L., Xu, Z., Sheng, V.S.: An effective end-to-end android malware detection method. Expert Syst. Appl. 218, 119593 (2023)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"4865_CR26","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1109\/TDSC.2022.3168285","volume":"20","author":"YL He","year":"2022","unstructured":"He, Y.L., Liu, Y.P., Wu, L., Yang, Z.Q., Ren, K., Qin, Z.: MsDroid: identifying malicious snippets for android malware detection. IEEE Trans. Dependable Secur. Comput. 20(3), 2025\u20132039 (2022)","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"4865_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103807","volume":"140","author":"JT Gu","year":"2024","unstructured":"Gu, J.T., Zhu, H.L., Han, Z.W., Li, X.Y., Zhao, J.J.: GSEDroid: GNN-based android malware detection framework using lightweight semantic embedding. Compute. Secur. 140, 103807 (2024)","journal-title":"Compute. Secur."},{"issue":"1","key":"4865_CR28","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s42400-023-00139-y","volume":"6","author":"HHR Manzil","year":"2023","unstructured":"Manzil, H.H.R., Manohar Naik, S.: Android malware category detection using a novel feature vector-based machine learning model. Cybersecurity 6(1), 6 (2023)","journal-title":"Cybersecurity"},{"issue":"1","key":"4865_CR29","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s10515-023-00378-w","volume":"30","author":"Y Cui","year":"2023","unstructured":"Cui, Y., Sun, Y., Lin, Z.: DroidHook: a novel API-hook based android malware dynamic analysis sandbox. Autom. Softw. Eng. 30(1), 10 (2023)","journal-title":"Autom. Softw. Eng."},{"issue":"1","key":"4865_CR30","doi-asserted-by":"publisher","first-page":"3093","DOI":"10.1038\/s41598-023-30028-w","volume":"13","author":"S Aurangzeb","year":"2023","unstructured":"Aurangzeb, S., Aleem, M.: Evaluation and classification of obfuscated android malware through deep learning using ensemble voting mechanism. Sci. Rep. 13(1), 3093 (2023)","journal-title":"Sci. Rep."},{"issue":"2","key":"4865_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.30564\/jcsr.v6i2.6632","volume":"6","author":"SG Xiong","year":"2024","unstructured":"Xiong, S.G., Zhang, H.T.: A multi-model fusion strategy for android malware detection based on machine learning algorithms. J. Comput. Sci. Res. 6(2), 1\u201311 (2024)","journal-title":"J. Comput. Sci. Res."},{"issue":"3","key":"4865_CR32","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1109\/TDSC.2022.3168285","volume":"20","author":"Y He","year":"2022","unstructured":"He, Y., Liu, Y., Wu, L., Yang, Z., Ren, K., Qin, Z.: MsDroid: identifying malicious snippets for android malware detection. IEEE Trans. Dependable Secur. Comput. 20(3), 2025\u20132039 (2022)","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"4865_CR33","unstructured":"Desnos, A.: Androguard. Available at https:\/\/github.com\/androguard\/androguard (2020)"},{"key":"4865_CR34","doi-asserted-by":"crossref","unstructured":"Au, K.W.Y., Zhou, Y.F., Huang, Z., Lie, D.: Pscout: analyzing the android permission specification. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 217\u2013228 (2012)","DOI":"10.1145\/2382196.2382222"},{"key":"4865_CR35","unstructured":"Backes, M., Bugiel, S., Derr, E., McDaniel, P., Octeau, D., Weisgerber, S.: On demystifying the android application framework: $$\\{$$Re-Visiting$$\\}$$ android permission specification analysis. In: 25th USENIX Security Symposium (USENIX Security 16), pp. 1101\u20131118 (2016)"},{"issue":"2","key":"4865_CR36","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TCYB.2017.2777960","volume":"49","author":"SY Yerima","year":"2018","unstructured":"Yerima, S.Y., Sezer, S.: Droidfusion: a novel multilevel classifier fusion approach for android malware detection. IEEE Trans. Cybern. 49(2), 453\u2013466 (2018)","journal-title":"IEEE Trans. Cybern."},{"key":"4865_CR37","doi-asserted-by":"crossref","unstructured":"Lashkari, A.H., Kadir, A.F.A., Taheri, L., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark android malware datasets and classification. In: 2018 International Carnahan Conference on Security Technology (ICCST), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/CCST.2018.8585560"},{"key":"4865_CR38","doi-asserted-by":"crossref","unstructured":"Mahdavifar, S., Kadir, A.F.A., Fatemi, R., Alhadidi, D., Ghorbani, A.A.: Dynamic android malware category classification using semi-supervised deep learning. In: 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech), pp. 515\u2013522. IEEE (2020)","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094"},{"issue":"1","key":"4865_CR39","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s10922-021-09634-4","volume":"30","author":"S Mahdavifar","year":"2022","unstructured":"Mahdavifar, S., Alhadidi, D., Ghorbani, A.A.: Effective and efficient hybrid android malware classification using pseudo-label stacked auto-encoder. J. Netw. Syst. Manag. 30(1), 22 (2022)","journal-title":"J. Netw. Syst. Manag."},{"key":"4865_CR40","doi-asserted-by":"crossref","unstructured":"Allix, K., Bissyand\u00e9, T.F., Klein, J., Le\u00a0Traon, Y.: Androzoo: collecting millions of android apps for the research community. In: Proceedings of the 13th International Conference on Mining Software Repositories, pp. 468\u2013471 (2016)","DOI":"10.1145\/2901739.2903508"},{"key":"4865_CR41","doi-asserted-by":"crossref","unstructured":"El\u00a0Fiky, A.H., Elshenawy, A., Madkour, M.A.: Detection of android malware using machine learning. In: 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 9\u201316. IEEE (2021)","DOI":"10.1109\/MIUCC52538.2021.9447661"},{"key":"4865_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102264","volume":"106","author":"H Gao","year":"2021","unstructured":"Gao, H., Cheng, S.Y., Zhang, W.M.: Gdroid: android malware detection and classification with graph convolutional network. Comput. Secur. 106, 102264 (2021)","journal-title":"Comput. Secur."},{"issue":"8","key":"4865_CR43","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1007\/s00521-020-05195-w","volume":"33","author":"K Bakour","year":"2021","unstructured":"Bakour, K., \u00dcnver, H.M.: Visdroid: android malware classification based on local and global image features, bag of visual words and machine learning techniques. Neural Comput. Appl. 33(8), 3133\u20133153 (2021)","journal-title":"Neural Comput. Appl."},{"key":"4865_CR44","doi-asserted-by":"publisher","first-page":"15471","DOI":"10.1109\/ACCESS.2023.3244656","volume":"11","author":"E Odat","year":"2023","unstructured":"Odat, E., Yaseen, Q.M.: A novel machine learning approach for android malware detection based on the co-existence of features. IEEE Access 11, 15471\u201315484 (2023)","journal-title":"IEEE Access"},{"key":"4865_CR45","unstructured":"Yilmaz, E.K., Bakir, H.: Hyperparameter tunning and feature selection methods for malware detection. Politeknik Dergisi, 1\u20131 (2023)"},{"key":"4865_CR46","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in neural information processing systems, vol. 30 (2017)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04865-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04865-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04865-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:31:59Z","timestamp":1757140319000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04865-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["4865"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04865-x","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]},"assertion":[{"value":"16 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised: The authors Weitao Huang and Hong Huang photos were inadvertently swapped in the author biography section, the author photos are corrected now.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"224"}}