{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:38:49Z","timestamp":1773488329449,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10586-024-04755-2","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T16:02:50Z","timestamp":1728576170000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A lightweight machine learning methods for malware classification"],"prefix":"10.1007","volume":"28","author":[{"given":"Mahmoud E.","family":"Farfoura","sequence":"first","affiliation":[]},{"given":"Ibrahim","family":"Mashal","sequence":"additional","affiliation":[]},{"given":"Ahmad","family":"Alkhatib","sequence":"additional","affiliation":[]},{"given":"Radwan M.","family":"Batyha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"4755_CR1","doi-asserted-by":"crossref","unstructured":"Afzal, S., Asim, M., Javed, A.R., Beg, M.O., Baker, T.: URLdeepDetect: A deep learning approach for detecting malicious URLs using semantic vector models. J. Netw. Syst. Manage., 29, 3, p. 21, 202","DOI":"10.1007\/s10922-021-09587-8"},{"key":"4755_CR2","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.future.2021.01.022","volume":"118","author":"S ur Rehman","year":"2021","unstructured":"ur Rehman, S., Khaliq, M., Imtiaz, S.I., et al.: Diddos: An approach for detection and identification of distributed denial of service (ddos) cyberattacks using gated recurrent units (gru). Future Generation Comput. Syst. 118, 453\u2013466 (2021)","journal-title":"Future Generation Comput. Syst."},{"issue":"5","key":"4755_CR3","first-page":"1938","volume":"8","author":"S Mohurle andM","year":"2017","unstructured":"Mohurle andM, S., Patil: A brief study of wannacry threat: Ransomware attack 2017. Int. J. Adv. Res. Comput. Sci. 8(5), 1938\u20131940 (2017)","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"4755_CR4","first-page":"2007","volume":"48","author":"N Idika","year":"2007","unstructured":"Idika, N., Mathur, A.P.: A survey of malware detection techniques. Purdue Univ. 48, 2007\u20132002 (2007)","journal-title":"Purdue Univ."},{"issue":"9","key":"4755_CR5","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput. 29(9), 2352\u20132449 (2017)","journal-title":"Neural Comput."},{"key":"4755_CR6","unstructured":"Venable, M., Walenstein, A., Hayes, M., Thompson, C., Lakhotia, A., Vilo: A Shield in the Malware Variation Battle, pp. 5\u201310. Virus Bulletin (2007)"},{"key":"4755_CR7","doi-asserted-by":"crossref","unstructured":"Rafiq, H., Aslam, N., Aleem, M., Issac, B., Randhawa, R.H.: AndroMalPack: Enhancing the ML-based malware classification by detection and removal of repacked apps for android systems. Sci. Rep., 12, 1, 19534, pp. 1\u201318","DOI":"10.1038\/s41598-022-23766-w"},{"key":"4755_CR8","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.1007\/s10586-022-03604-4","volume":"25","author":"A Mughaid","year":"2022","unstructured":"Mughaid, A., AlZu\u2019bi, S., Hnaif, A.: An intelligent cyber security phishing detection system using deep learning techniques. Cluster Comput. 25, 3819\u20133828 (2022)","journal-title":"Cluster Comput."},{"key":"4755_CR9","doi-asserted-by":"crossref","unstructured":"Taylor, C., Alves-Foss, J.: Nate \u2013 network Analysis of Anomalous Traffic Events, a low-cost Approach. New Security Paradigms Workshop (2001)","DOI":"10.1145\/508185.508186"},{"key":"4755_CR10","doi-asserted-by":"publisher","unstructured":"Yang, L., Ciptadi, A., Laziuk, I., Ahmadzadeh, A., Wang, G.: BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware, 2021 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA, pp. 78\u201384, (2021). https:\/\/doi.org\/10.1109\/SPW53761.2021.00020","DOI":"10.1109\/SPW53761.2021.00020"},{"issue":"12","key":"4755_CR11","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.3844\/jcssp.2023.1549.1560","volume":"19","author":"HM Al-Mimi","year":"2023","unstructured":"Al-Mimi, H.M., Hamad, N.A., Abualhaj, M.M., Al-Khatib, S.N., Hiari, M.O.: Improved intrusion detection system to alleviate attacks on DNS service. J. Comput. Sci. 19(12), 1549\u20131560 (2023)","journal-title":"J. Comput. Sci."},{"issue":"13","key":"4755_CR12","doi-asserted-by":"publisher","first-page":"5843","DOI":"10.1016\/j.eswa.2014.02.053","volume":"41","author":"N Nissim","year":"2014","unstructured":"Nissim, N., Moskovitch, R., Rokach, L., Elovici, Y.: Novel active learning methods for enhanced pc malware detection in windows os. Expert Syst. Appl. 41(13), 5843\u20135857 (2014)","journal-title":"Expert Syst. Appl."},{"key":"4755_CR13","doi-asserted-by":"crossref","unstructured":"Bae, S.I., Lee, G.B., Im, E.G.: Ransomware detection using machine learning algorithms. Concurrency Computation: Pract. Experience, 32, 18, e5422, (2020)","DOI":"10.1002\/cpe.5422"},{"key":"4755_CR14","unstructured":"Brengel, M., Rossow, C.: Yarix: Scalable yara-based malware intelligence, USENIX Security Symposium, pp. 3541\u20133558, (2021)"},{"issue":"7","key":"4755_CR15","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1109\/TII.2017.2789219","volume":"14","author":"J Li","year":"2018","unstructured":"Li, J., Sun, L., Yan, Q., Li, Z., Srisa-an, W., Ye, H.: Significant permission identification for machine-learning-based android malware detection. IEEE Trans. Industrial Inf. vol. 14(7), 3216\u20133225 (2018)","journal-title":"IEEE Trans. Industrial Inf. vol"},{"key":"4755_CR16","doi-asserted-by":"publisher","first-page":"102513","DOI":"10.1016\/j.cose.2021.102513","volume":"112","author":"F Ou","year":"2022","unstructured":"Ou, F., Xu, J.: S3feature: A static sensitive subgraph-based feature for android malware detection. Computers Secur. 112, 102513 (2022)","journal-title":"Computers Secur."},{"key":"4755_CR17","doi-asserted-by":"publisher","first-page":"101743","DOI":"10.1016\/j.cose.2020.101743","volume":"92","author":"M Jerbi","year":"2020","unstructured":"Jerbi, M., Dagdia, Z.C., Bechikh, S., Said, L.B.: On the use of artificial malicious patterns for android malware detection. Computers Secur. 92, 101743 (2020)","journal-title":"Computers Secur."},{"key":"4755_CR18","doi-asserted-by":"crossref","unstructured":"Mahindru, A., Sangal, A.L.: Mldroid\u2014framework for android malware detection using machine learning techniques. Neural Comput. Appl. 33(10), 5183\u20135240 (May 2021)","DOI":"10.1007\/s00521-020-05309-4"},{"key":"4755_CR19","doi-asserted-by":"crossref","unstructured":"Jung, J., Kim, H., Shin, D., Lee, M., Lee, H., Cho, S., Suh, K.: Android malware detection based on useful api calls and machine learning, IEEE 1st International Conference on Artificial Intelligence and Knowledge Engineering, pp. 175\u2013178, (2018)","DOI":"10.1109\/AIKE.2018.00041"},{"key":"4755_CR20","unstructured":"Yu, H.: An android malware detection system based on machine learning, vol. 1864, no. 1, p. 020136, Aug. (2017)"},{"key":"4755_CR21","doi-asserted-by":"publisher","first-page":"4182","DOI":"10.1007\/s11227-021-04020-y","volume":"78","author":"S Li","year":"2022","unstructured":"Li, S., Zhou, Q., Zhou, R., Lv, Q.: Intelligent malware detection based on graph convolutional network. J. Supercomputing. 78, 4182\u20134198 (2022)","journal-title":"J. Supercomputing"},{"key":"4755_CR22","doi-asserted-by":"crossref","unstructured":"Garcia, J., Hammad, M., Malek, S.: Lightweight, obfuscation-resilient detection and family identification of android malware. ACM Trans. Softw. Eng. Methodol., 26, 3, (2018)","DOI":"10.1145\/3162625"},{"key":"4755_CR23","doi-asserted-by":"crossref","unstructured":"Karbab, E.B., Debbabi, M.: Petadroid: Adaptive android malware detection using deep learning, In: 18th International Conference, DIMVA, pp. 319\u2013340, Jul. (2021)","DOI":"10.1007\/978-3-030-80825-9_16"},{"key":"4755_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, Y., Zhong, M., Ding, D., Cao, Y., Zhang, Y., Zhang, M., Yang, M.: Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware, In: ACM SIGSAC Conference on Computer and Communications Security. pp. 757\u2013770, (2020)","DOI":"10.1145\/3372297.3417291"},{"key":"4755_CR25","doi-asserted-by":"crossref","unstructured":"Baptista, I., Shiaeles, S., Kolokotronis, N.: A novel malware detection system based on machine learning and binary visualization, In: IEEE International Conference on Communications Workshops. pp. 1\u20136, (2019)","DOI":"10.1109\/ICCW.2019.8757060"},{"key":"4755_CR26","doi-asserted-by":"crossref","unstructured":"Vu, D.L., Nguyen, T.K., Nguyen, T.V., Nguyen, T.N., Massacci, F., Phung, P.H.: Hit4mal: Hybrid image transformation for malware classification. Trans. Emerg. Telecommunications Technol., pp. 1\u201315, (2019)","DOI":"10.1002\/ett.3789"},{"key":"4755_CR27","doi-asserted-by":"publisher","first-page":"159262","DOI":"10.1109\/ACCESS.2021.3131713","volume":"9","author":"WK Wong","year":"2021","unstructured":"Wong, W.K., Juwono, F.H., Apriono, C.: Vision-based malware detection: A transfer learning approach using optimal ecoc-svm configuration. IEEE Access. 9, 159262\u2013159270 (2021)","journal-title":"IEEE Access."},{"key":"4755_CR28","doi-asserted-by":"publisher","first-page":"102420","DOI":"10.1016\/j.cose.2021.102420","volume":"110","author":"M Xiao","year":"2021","unstructured":"Xiao, M., Guo, C., Shen, G., Cui, Y., Jiang, C.: Image-based malware classification using section distribution information. Computers Secur. 110, 102420 (2021)","journal-title":"Computers Secur."},{"key":"4755_CR29","doi-asserted-by":"crossref","unstructured":"Xu, Z., Ren, K., Qin, S., Craciun, F.: Cdgdroid: Android malware detection based on deep learning using cfg and dfg, In: Sun, J., Sun, M. (eds.) Formal Methods and Software Engineering, (2018)","DOI":"10.1007\/978-3-030-02450-5_11"},{"issue":"7","key":"4755_CR30","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1007\/s42452-020-3132-2","volume":"2","author":"HM \u00dcnver","year":"2020","unstructured":"\u00dcnver, H.M., Bakour, K.: Android malware detection based on image-based features and machine learning techniques. SN Appl. Sci. 2(7), 1299 (2020)","journal-title":"SN Appl. Sci."},{"key":"4755_CR31","doi-asserted-by":"crossref","unstructured":"Hao, J., Luo, S., Pan, L.: EII-MBS: Malware Family Classification via Enhanced Instruction-level Behavior Semantic Learning, Computer Security, vol. 112. no. C (2022)","DOI":"10.1016\/j.cose.2022.102905"},{"key":"4755_CR32","doi-asserted-by":"publisher","first-page":"95970","DOI":"10.1109\/ACCESS.2022.3202952","volume":"10","author":"Q Lu","year":"2022","unstructured":"Lu, Q., Zhang, H., Kinawi, H., Niu, D.: Self-attentive models for real-time malware classification. IEEE Access. 10, 95970\u201395985 (2022)","journal-title":"IEEE Access."},{"key":"4755_CR33","doi-asserted-by":"crossref","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. Priv. Secur., 22, 2, (2019)","DOI":"10.1145\/3313391"},{"key":"4755_CR34","doi-asserted-by":"crossref","unstructured":"Samuel, A.L.: Some Studies in Machine Learning Using the Game of Checkers, IBM J. Res. Dev., vol. 3, no. 3, pp. 210\u2013229, Jul. (1959)","DOI":"10.1147\/rd.33.0210"},{"key":"4755_CR35","unstructured":"John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers, ArXivPrepr. ArXiv13024964, 2013."},{"key":"4755_CR36","doi-asserted-by":"publisher","DOI":"10.1037\/e471672008-001","author":"E Fix","year":"1951","unstructured":"Fix, E., Hodges, J.L.: Discriminatory analysis: Nonparametric discrimination: Consistency properties. Am. Psychol. Association. (1951). https:\/\/doi.org\/10.1037\/e471672008-001","journal-title":"Am. Psychol. Association"},{"key":"4755_CR37","doi-asserted-by":"crossref","unstructured":"Joachims, T.: Making large-scale support vector machine learning practical, advances in Kernel methods. Support Vector Learn., (1999)","DOI":"10.7551\/mitpress\/1130.003.0015"},{"key":"4755_CR38","doi-asserted-by":"crossref","unstructured":"Ho, T.K.: Random decision forests, In Proceedings of 3rd international conference on document analysis and recognition, vol. 1, pp. 278\u2013282","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"4755_CR39","first-page":"3146","volume":"30","author":"G Ke","year":"2017","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Liu, T.-Y.: Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural. Inf. Process. Syst. 30, 3146\u20133154 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04755-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04755-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04755-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T15:06:23Z","timestamp":1736521583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04755-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["4755"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04755-2","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"10 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":4,"name":"first_online","label":"First Online","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":"Competing interests"}}],"article-number":"1"}}