The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
A Machine Learning Framework & Development for Insider Cyber-crime Threats Detection
Abstract
Many organizations face a significant challenge with insider threats. As conventional security measures like intrusion detection systems and firewalls aren't always effective in detecting and preventing such threats. Insider threats often come from trusted individuals who possess knowledge of and access to important organizational assets. This work explores the use of machine learning to classify insider threat behaviors, specifically focusing on three approaches such that supervised, unsupervised, and reinforcement learning. The paper describes the development of an unsupervised machine learning system that analyzes data from multiple technical sources to detect malicious insider activity. The system, which is designed to be simple and easy to assemble, was tested with existing machine learning algorithms and showed moderate success in detecting malicious insider activity during the training phase and negligible success during the testing phase.These results suggest that while machine learning can be a useful tool for detecting insider threats, it should not be solely relied upon for threat detection. To improve the current system's performance, it is necessary to include additional features, such as file names, email subjects and headers, and website types. Furthermore, physical security, cybersecurity, psychological, and organizational factors must be considered when addressing insider threats. Future research should focus on acquiring real datasets, collecting insider threat scenarios and use cases, and testing different machine learning approaches from both technical and non-technical sources.
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In the citing paper
“At the same time, the shortcomings revolve around the lack of information on known malicious insiders and their actions.”
From A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods (2024) · cites this paper as [23]
In the citing paper
“Recent work [23] explores the application of unsupervised machine learning for insider threat detection, using a hybrid solution that leverages both misuse and anomaly detection.”
From A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods (2024) · cites this paper as [23]
In the citing paper
“The pros of this technique include its simplicity and the ability to detect unknown threats.”
From A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods (2024) · cites this paper as [23]
Citations
Cites
Python for Scientists and Engineers
Millman, K. J., & Aivazis, M. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.36 · Cited by 1,294
Computer Intrusion: Detecting Masquerades
DuMouchel, W., Ju, W., Karr, A. F., Schonlau, M., Theusan, M., & Vardi, Y. (2001) · Statistical Science · doi:10.1214/ss/998929476 · Cited by 378
Automated Insider Threat Detection System Using User and Role-Based Profile Assessment
Legg, P., Buckley, O. E., Goldsmith, M. H., & Creese, S. (2015) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2438442 · Cited by 162
Behavioral Based Insider Threat Detection Using Deep Learning
Nasir, R., Afzal, M., Latif, R., & Iqbal, W. (2021) · IEEE Access · doi:10.1109/access.2021.3118297 · Cited by 146
Böse, B., Avasarala, B., Tirthapura, S., Chung, Y.-Y., & Steiner, D. D. (2017) · IEEE Systems Journal · doi:10.1109/jsyst.2016.2558507 · Cited by 94
MovingPandas: Efficient Structures for Movement Data in Python
Graser, A. (2019) · GI_Forum · doi:10.1553/giscience2019_01_s54 · Cited by 89
Lindauer, B. (2020) · Figshare · doi:10.1184/r1/12841247 · Cited by 59
Punithavathani, D. S., Sujatha, K., & Jain, J. (2014) · Cluster Computing · doi:10.1007/s10586-014-0403-y · Cited by 44
Insider Threat Detection using Deep Learning: A Review
Raut, M., Dhavale, S., Singh, A., & Mehra, A. (2020) · 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9315932 · Cited by 27
An Enhanced Phishing Detection Tool Using Deep Learning From URL
Dawabsheh, A., Jazzar, M., Eleyan, A., Bejaoui, T., & Popoola, S. I. (2022) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets55823.2022.9993984 · Cited by 27
Self-citation
Research works printed but not linked: 6
- jj, 2019, Aurélien Géron, 1135
- drdsh, 2021, A Prediction Olive Diseases Using Machine Learning Models, Decision Tree and Naive Bayes Models, Journal of Theoretical and Applied Information Technology, 99
- odeh, 2021, A Survey of Social Engineering Attacks: Detection and Prevention Tools, Journal of Theoretical and Applied Information Technology, 99
- 10.1109/JSYST.2015.2438442 · DOI
- 10.1109/JSYST.2016.2558507 · DOI
- 10.1007/s10586-014-0403-y · DOI
Cited by
Sorted by citations
Alzaabi, F. R., & Mehmood, A. (2024) · IEEE Access · doi:10.1109/access.2024.3369906 · Cited by 204
“Recent work [23] explores the application of unsupervised machine learning for insider threat detection, using a hybrid solution that leverages both misuse and anomaly detection.”
BackgroundSaeed Alketbi, K., & Mehmood, A. (2025) · IEEE Access · doi:10.1109/access.2025.3587114 · Cited by 41
Double layer detection of internal threat in enterprise systems based on deep learning
He, D., Lv, X., Xu, X., Chan, S., & Choo, K.-K. R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3372771 · Cited by 31
Mitigating Insider Threat: A Neural Network Approach for Enhanced Security
Lavanya, P., Glory, H. A., & Sriram, V. S. S. (2024) · IEEE Access · doi:10.1109/access.2024.3404814 · Cited by 21
Lightweight Intrusion Detection System with GAN-Based Knowledge Distillation
Ali, T., Eleyan, A., Bejaoui, T., & Al-Khalidi, M. (2024) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets61466.2024.10577682 · Cited by 18
Self-citation
Metadata
- Authors
- Yousef, R., Jazzar, M., Eleyan, A., & Bejaoui, T.
- Affiliations
- Palestine Technical University - Kadoorie (PS)
- Manchester Metropolitan University (GB)
- University of Carthage (TN)
- Venue
- 2023 International Conference on Smart Applications, Communications and Networking (SmartNets)
- Publisher
- IEEE
- Published
- 2023-07-25
- Pages
- 1-6
- OpenAlex
- W4386066497
- Cited by
- 20
- References
- 18
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 8
- FWCI
- 6.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Smart Applications, Communications and Networking (SmartNets) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- insider threats, insider threat detection, unsupervised learning, supervised learning, reinforcement learning, multi-source data analysis, cybersecurity
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