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In this study, we develop a repeatable procedure for detecting cyber-attacks in an accurate, scalable, and timely manner. An in-depth learning algorithm is utilized for training a neural network for detecting suspicious user activities. The proposed system architecture was implemented with the help of Splunk Enterprise Edition 6.42. A data set of average feature counts has been executed through a Splunk search command in 1-min intervals. All the data sets consisted of a minute trait total derived from a sparkling file. The attack patterns that were not anonymized or were indicative of the vulnerability of cyber-attack were denoted with yellow. The rule-based method dispensed a low quantity of irregular illustrations in contrast with the Partitioning Around Medoids method. The results in this study demonstrated that using a proportional collection of instances trained with the deep learning algorithm, a classified data set can accurately detect suspicious behavior. This method permits for the allocation of multiple log source types through a sliding time window and provides a scalable solution, which is a much-needed function. <\/jats:p>","DOI":"10.1177\/15501329221084882","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:57:09Z","timestamp":1650999429000},"page":"155013292210848","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":45,"title":["Artificial intelligence analysis in cyber domain: A review"],"prefix":"10.1177","volume":"18","author":[{"given":"Liguo","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Luoyang Instiute of Science and Technology, Henan, China"}]},{"given":"Derong","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing, Luoyang Institute of Science and Technology, Luoyang, 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