{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:51:49Z","timestamp":1776811909924,"version":"3.51.2"},"reference-count":17,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2024,6,17]]},"abstract":"<jats:p>Conventional brute-force attacks can now be detected and identified based on statistical analysis of logs and traffic data. However, they fail to detect low-frequency and distributed brute-force attack behaviors. To address different attack methods, new detection techniques have emerged. This study compares various machine learning algorithms and selects two methods, namely the clustering algorithm k-means and bdscan, as well as the decision tree algorithm for data learning. In one approach, normal user login data is integrated with enterprise email log data. The data is first statistically analyzed and filtered, followed by quantifying data characteristics using information entropy. Subsequently, machine learning algorithms are employed for classification, and the results are visualized for display. In another approach, labeled raw data is used to train a model using the decision tree algorithm. By comparing the two analysis results, a more accurate model can be obtained. These analytical methods can help enterprises strengthen email security and defend against low-frequency and distributed brute-force attacks.<\/jats:p>","DOI":"10.3233\/jcm-247147","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T11:35:38Z","timestamp":1718710538000},"page":"1379-1393","source":"Crossref","is-referenced-by-count":1,"title":["Research on techniques for detecting brute-force attacks on corporate email"],"prefix":"10.66113","volume":"24","author":[{"given":"Xiaomei","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuewei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"S1","key":"10.3233\/JCM-247147_ref1","first-page":"116","article-title":"Abnormal behavior detection for campus email systems based on big data analysis","volume":"39","author":"Jia","year":"2018","journal-title":"Journal of Communications."},{"key":"10.3233\/JCM-247147_ref2","first-page":"396","article-title":"Research on Network Traffic Intrusion Detection Based on Machine Learning","author":"Qing","year":"2022","journal-title":"University of Electronic Science and Technology."},{"key":"10.3233\/JCM-247147_ref3","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1504\/IJCSE.2021.117022","article-title":"Research on advertising content recognition based on convolutional neural network and recurrent neural network","author":"Mei","year":"2021","journal-title":"International Journal of Computational Science and Engineering."},{"key":"10.3233\/JCM-247147_ref4","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.3233\/JCM-226663","article-title":"Real-time anomaly attack detection based on an improved variable length model","author":"Mei","year":"2023","journal-title":"Journal of Computational Methods in Sciences and Engineering."},{"key":"10.3233\/JCM-247147_ref5","first-page":"115","article-title":"A survey of Random forest algorithms","author":"Yan","year":"2019","journal-title":"Journal of the Hebei Academy of Sciences."},{"key":"10.3233\/JCM-247147_ref6","first-page":"45","article-title":"A method for detecting brute force password cracking attacks on login accounts based on traffic characteristics","author":"Fang","year":"2017","journal-title":"Journal of Southwest University."},{"issue":"6","key":"10.3233\/JCM-247147_ref7","doi-asserted-by":"crossref","first-page":"32677","DOI":"10.1109\/ACCESS.2018.2844548","article-title":"T2FA: Transparent Two-Factor Authentication","author":"Zhang","year":"2018","journal-title":"IEEE Access."},{"key":"10.3233\/JCM-247147_ref8","doi-asserted-by":"crossref","unstructured":"Dev JA. On the Imminent Advent of Botnet Powered Cracking. 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC), Pittsburgh, PA, 2016: pp. 188-195.","DOI":"10.1109\/CIC.2016.035"},{"key":"10.3233\/JCM-247147_ref9","first-page":"223","article-title":"A Survey of Random forest Algorithms in Ensemble learning","author":"Sen","year":"2018","journal-title":"Information and Communication Technology."},{"key":"10.3233\/JCM-247147_ref10","doi-asserted-by":"crossref","unstructured":"Izrailov KE, Zhukovskaya PE, Kurta PA, Chechulin A. Investigation of the method of determination of password resistance to brute force based on an artificial neural network. Journal of Physics. 2021; 1864(1).","DOI":"10.1088\/1742-6596\/1864\/1\/012123"},{"key":"10.3233\/JCM-247147_ref11","doi-asserted-by":"crossref","unstructured":"Abhishek J, Mohammad W, Goudar RH. An Efficient Cryptographic Scheme for Text Message Protection Against Brute Force and Cryptanalytic Attacks. 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Attacks and vulnerability analysis of e-mail as a password reset point. 2018 Fourth International Conference on Mobile and Secure Services (MobiSecServ), Miami Beach, FL. 2018: 1-15.","DOI":"10.1109\/MOBISECSERV.2018.8311443"},{"issue":"1","key":"10.3233\/JCM-247147_ref15","first-page":"304","article-title":"Analysis of two anomaly detection methods in intrusion detection system","author":"Xian","year":"2009","journal-title":"Network Security Technology and Application."},{"key":"10.3233\/JCM-247147_ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904330"},{"key":"10.3233\/JCM-247147_ref17","doi-asserted-by":"publisher","DOI":"10.1109\/OJSP.2020.3036276"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-247147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:07:29Z","timestamp":1776809249000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-247147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,17]]},"references-count":17,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jcm-247147","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,17]]}}}