{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T01:10:48Z","timestamp":1785805848982,"version":"3.56.0"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2023YFE0110200"],"award-info":[{"award-number":["2023YFE0110200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Research Foundation of South Africa","award":["151178"],"award-info":[{"award-number":["151178"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML\u2013DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM\u2013Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust.<\/jats:p>","DOI":"10.3390\/sym17101704","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:13:27Z","timestamp":1760361207000},"page":"1704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Application of Machine Learning and Deep Learning Techniques for Enhanced Insider Threat Detection in Cybersecurity: Bibliometric Review"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2930-2749","authenticated-orcid":false,"given":"Hillary Kwame","family":"Ofori","sequence":"first","affiliation":[{"name":"Computer Science Department, Ghana Communication Technology University, Accra North PMB 100, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8203-3436","authenticated-orcid":false,"given":"Kwame","family":"Bell-Dzide","sequence":"additional","affiliation":[{"name":"Computer Science Department, Ghana Communication Technology University, Accra North PMB 100, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9679-5976","authenticated-orcid":false,"given":"William Leslie","family":"Brown-Acquaye","sequence":"additional","affiliation":[{"name":"Faculty of Computing & Information Systems, Ghana Communication Technology University, Accra North PMB 100, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6329-012X","authenticated-orcid":false,"given":"Forgor","family":"Lempogo","sequence":"additional","affiliation":[{"name":"Department of Mobile & Pervasive Computing, Ghana Communication Technology University, Accra North PMB 100, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2209-9924","authenticated-orcid":false,"given":"Samuel O.","family":"Frimpong","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Decision Sciences, University of Energy and Natural Resources, Sunyani P.O. Box 214, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5718-4494","authenticated-orcid":false,"given":"Israel Edem","family":"Agbehadji","sequence":"additional","affiliation":[{"name":"Faculty of Computing & Information Systems, Ghana Communication Technology University, Accra North PMB 100, Ghana"},{"name":"Faculty of Accounting and Informatics, Durban University of Technology, Durban 4001, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-9615","authenticated-orcid":false,"given":"Richard C.","family":"Millham","sequence":"additional","affiliation":[{"name":"ICT and Society Research Group, Durban University of Technology, Durban 4001, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,11]]},"reference":[{"key":"ref_1","unstructured":"(2025, July 15). 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