Artificial intelligence analysis in cyber domain: A review
The application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. In this study, we develop a repeatable procedure for detecting cyber-attacks in an accurate, scalable...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221084882 |
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| _version_ | 1850227740176809984 |
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| author | Liguo Zhao Derong Zhu Wasswa Shafik S Mojtaba Matinkhah Zubair Ahmad Lule Sharif Alisa Craig |
| author_facet | Liguo Zhao Derong Zhu Wasswa Shafik S Mojtaba Matinkhah Zubair Ahmad Lule Sharif Alisa Craig |
| author_sort | Liguo Zhao |
| collection | DOAJ |
| description | The application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. 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. |
| format | Article |
| id | doaj-art-1f0e965a77fc4b739f3744db6f761eb8 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-1f0e965a77fc4b739f3744db6f761eb82025-08-20T02:04:44ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-04-011810.1177/15501329221084882Artificial intelligence analysis in cyber domain: A reviewLiguo Zhao0Derong Zhu1Wasswa Shafik2S Mojtaba Matinkhah3Zubair Ahmad4Lule Sharif5Alisa Craig6School of Computer and Information Engineering, Luoyang Instiute of Science and Technology, Henan, ChinaSchool of Intelligent Manufacturing, Luoyang Institute of Science and Technology, Luoyang, ChinaIntelligent Connectivity Research Laboratory, Department of Computer Engineering, Yazd University, Yazd, IranIntelligent Connectivity Research Laboratory, Department of Computer Engineering, Yazd University, Yazd, IranDepartment of Statistics, Yazd University, Yazd, IranDepartment of Management Studies, Islamic University in Uganda, Kampala, UgandaDepartment of Statistics, Pennsylvania State University, State College, PA, USAThe application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. 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.https://doi.org/10.1177/15501329221084882 |
| spellingShingle | Liguo Zhao Derong Zhu Wasswa Shafik S Mojtaba Matinkhah Zubair Ahmad Lule Sharif Alisa Craig Artificial intelligence analysis in cyber domain: A review International Journal of Distributed Sensor Networks |
| title | Artificial intelligence analysis in cyber domain: A review |
| title_full | Artificial intelligence analysis in cyber domain: A review |
| title_fullStr | Artificial intelligence analysis in cyber domain: A review |
| title_full_unstemmed | Artificial intelligence analysis in cyber domain: A review |
| title_short | Artificial intelligence analysis in cyber domain: A review |
| title_sort | artificial intelligence analysis in cyber domain a review |
| url | https://doi.org/10.1177/15501329221084882 |
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