{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:41:10Z","timestamp":1776919270931,"version":"3.51.2"},"reference-count":27,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This study aims to develop a machine learning model that can accurately detect cyberattacks. We compare the performance of Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) in predicting cyberattacks. Furthermore, we investigate whether using Information Gain Attribute Evaluation (IGAE) for feature selection improves the performance of the algorithms. This work provides a clear comparison of the algorithms and shows the most suitable one for classifying cyberattacks. In addition, this study combines LR and RF using a voting classifier along with IGAE and compares its performance with that of the rest of the algorithms. We investigate whether combining algorithms increases the accuracy of the results. The results show that the most accurate algorithm is RF, followed by LR and SVM. Contrary to initial expectations, the findings further indicate that the application of IGAE marginally reduces algorithm accuracy across the tested classifiers, suggesting that feature selection through information gain is not universally beneficial in cyberattack detection tasks. These findings contribute to the growing body of knowledge on effective machine learning methodologies for cybersecurity applications.<\/jats:p>","DOI":"10.3390\/computers15040248","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T15:14:12Z","timestamp":1776352452000},"page":"248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Machine Learning Classifiers in Detecting Cyberattacks"],"prefix":"10.3390","volume":"15","author":[{"given":"Mustafa","family":"Hammad","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Mutah University, Mutah 61710, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Almahmood","sequence":"additional","affiliation":[{"name":"College of Information Technology, University of Bahrain, Sakhir P.O. Box 32038, Bahrain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maen","family":"Hammad","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2232-9387","authenticated-orcid":false,"given":"Bassam A. Y.","family":"Alqaralleh","sequence":"additional","affiliation":[{"name":"College of Business Administration, American University of the Middle East, Egaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4900-497X","authenticated-orcid":false,"given":"Aymen I.","family":"Zreikat","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fraley, J.B., and Cannady, J. (April, January 30). The promise of machine learning in Cybersecurity. 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