Insider threat detection using supervised machine learning algorithms Insider threat detection using supervised machine learning algorithms
Insider threats refer to abnormal actions taken by individuals with privileged access, compromising system data’s confidentiality, integrity, and availability. They pose significant cybersecurity risks, leading to substantial losses for several organizations. Detecting insider threats is crucial due...
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| Vydané v: | Telecommunication systems Ročník 87; číslo 4; s. 899 - 915 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.12.2024
Springer Nature B.V |
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| ISSN: | 1018-4864, 1572-9451 |
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| Abstract | Insider threats refer to abnormal actions taken by individuals with privileged access, compromising system data’s confidentiality, integrity, and availability. They pose significant cybersecurity risks, leading to substantial losses for several organizations. Detecting insider threats is crucial due to the imbalance in their datasets. Moreover, the performance of existing works has been evaluated on various datasets and problem settings, making it challenging to compare the effectiveness of different algorithms and offer recommendations to decision-makers. Furthermore, no existing work investigates the impact of changing hyperparameters. This paper aims to objectively assess the performance of various supervised machine learning algorithms for detecting insider threats under the same setting. We precisely evaluate the performance of various supervised machine learning algorithms on a balanced dataset using the same feature extraction method. Additionally, we explore the impact of hyperparameter tuning on performance within the balanced dataset. Finally, we investigate the performance of different algorithms in the context of imbalanced datasets under various conditions. We conduct all the experiments in the publicly available CERT r4.2 dataset. The results show that supervised learning with a balanced dataset in RF obtains the best accuracy and F1-score of 95.9% compared with existing works, such as, DNN, LSTM Autoencoder and User Behavior Analysis. |
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| AbstractList | Insider threats refer to abnormal actions taken by individuals with privileged access, compromising system data’s confidentiality, integrity, and availability. They pose significant cybersecurity risks, leading to substantial losses for several organizations. Detecting insider threats is crucial due to the imbalance in their datasets. Moreover, the performance of existing works has been evaluated on various datasets and problem settings, making it challenging to compare the effectiveness of different algorithms and offer recommendations to decision-makers. Furthermore, no existing work investigates the impact of changing hyperparameters. This paper aims to objectively assess the performance of various supervised machine learning algorithms for detecting insider threats under the same setting. We precisely evaluate the performance of various supervised machine learning algorithms on a balanced dataset using the same feature extraction method. Additionally, we explore the impact of hyperparameter tuning on performance within the balanced dataset. Finally, we investigate the performance of different algorithms in the context of imbalanced datasets under various conditions. We conduct all the experiments in the publicly available CERT r4.2 dataset. The results show that supervised learning with a balanced dataset in RF obtains the best accuracy and F1-score of 95.9% compared with existing works, such as, DNN, LSTM Autoencoder and User Behavior Analysis. |
| Author | Yin, Jiao Manoharan, Phavithra Wang, Hua Ye, Wenjie Zhang, Yanchun |
| Author_xml | – sequence: 1 givenname: Phavithra surname: Manoharan fullname: Manoharan, Phavithra organization: School of Computer Science and Technology, Zhejiang Normal University – sequence: 2 givenname: Jiao surname: Yin fullname: Yin, Jiao email: jiao.yin@vu.edu.au organization: School of Computer Science and Technology, Zhejiang Normal University – sequence: 3 givenname: Hua surname: Wang fullname: Wang, Hua organization: School of Computer Science and Technology, Zhejiang Normal University – sequence: 4 givenname: Yanchun surname: Zhang fullname: Zhang, Yanchun organization: School of Computer Science and Technology, Zhejiang Normal University, Department of New Networks, Peng Cheng Laboratory, Institute for Sustainable Industries and Liveable Cities, Victoria University – sequence: 5 givenname: Wenjie surname: Ye fullname: Ye, Wenjie organization: School of Computer Science and Technology, Zhejiang Normal University |
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| CitedBy_id | crossref_primary_10_1109_ACCESS_2024_3369906 crossref_primary_10_3390_info16060499 crossref_primary_10_1007_s10462_024_10890_4 crossref_primary_10_1007_s10207_025_01002_6 crossref_primary_10_1007_s11280_024_01275_2 crossref_primary_10_3390_fi17020093 crossref_primary_10_1016_j_engappai_2025_111755 |
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| ContentType | Journal Article |
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| Snippet | Insider threats refer to abnormal actions taken by individuals with privileged access, compromising system data’s confidentiality, integrity, and availability.... |
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| SubjectTerms | Algorithms Artificial Intelligence Availability Business and Management Computer Communication Networks Cybersecurity Datasets IT in Business Machine learning Original Paper Performance evaluation Probability Theory and Stochastic Processes Supervised learning Threat evaluation |
| Subtitle | Insider threat detection using supervised machine learning algorithms |
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| Title | Insider threat detection using supervised machine learning algorithms |
| URI | https://link.springer.com/article/10.1007/s11235-023-01085-3 https://www.proquest.com/docview/3141981286 |
| Volume | 87 |
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