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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mohd Ariffin, Muhammad Azizi
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Computer Science & IT Engineering

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Empowering anomaly detection algorithm: a review Iqbal Basheer, Muhammad Yunus; Mohd Ali, Azliza; Osman, Rozianawaty; Abdul Hamid, Nurzeatul Hamimah; Nordin, Sharifalillah; Mohd Ariffin, Muhammad Azizi; Iglesias Martínez, José Antonio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp9-22

Abstract

Detecting anomalies in a data stream relevant to domains like intrusion detection, fraud detection, security in sensor networks, or event detection in internet of things (IoT) environments is a growing field of research. For instance, the use of surveillance cameras installed everywhere that is usually governed by human experts. However, when many cameras are involved, more human expertise is needed, thus making it expensive. Hence, researchers worldwide are trying to invent the best-automated algorithm to detect abnormal behavior using real-time data. The designed algorithm for this purpose may contain gaps that could differentiate the qualities in specific domains. Therefore, this study presents a review of anomaly detection algorithms, introducing the gap that presents the advantages and disadvantages of these algorithms. Since many works of literature were reviewed in this review, it is expected to aid researchers in closing this gap in the future.
Co-Authors Abdul Hamid, Nurzeatul Hamimah Iglesias Martínez, José Antonio Iqbal Basheer, Muhammad Yunus Mohd Ali, Azliza Nordin, Sharifalillah Osman, Rozianawaty