{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:05:21Z","timestamp":1775837121819,"version":"3.50.1"},"reference-count":109,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.<\/jats:p>","DOI":"10.3390\/s23115340","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:57:47Z","timestamp":1685933867000},"page":"5340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey"],"prefix":"10.3390","volume":"23","author":[{"given":"Sehar","family":"Zehra","sequence":"first","affiliation":[{"name":"FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan"},{"name":"College Education & Literacy Department, Khursheed Government Girls Degree College, Government of Sindh, Karachi 75230, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ummay","family":"Faseeha","sequence":"additional","affiliation":[{"name":"FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan"},{"name":"Department of Computer Science, Main Campus, Jinnah University For Women, Karachi 74600, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1834-1810","authenticated-orcid":false,"given":"Hassan Jamil","family":"Syed","sequence":"additional","affiliation":[{"name":"FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan"},{"name":"Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia"},{"name":"Cyber Security Research Lab, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3833-2644","authenticated-orcid":false,"given":"Fahad","family":"Samad","sequence":"additional","affiliation":[{"name":"FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5526-3623","authenticated-orcid":false,"given":"Ashraf Osman","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia"},{"name":"Creative Advanced Machine Intelligence Research Centre, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3277-4505","authenticated-orcid":false,"given":"Anas W.","family":"Abulfaraj","sequence":"additional","affiliation":[{"name":"Department of Information Systems, King Abdulaziz University, Rabigh 21911, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wamda","family":"Nagmeldin","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108288","DOI":"10.1016\/j.comnet.2021.108288","article-title":"NFV security survey in 5G networks: A three-dimensional threat taxonomy","volume":"197","author":"Madi","year":"2021","journal-title":"Comput. 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