Insider Threat Identification From Accessed Website Content Optimized by Modified Metaheuristic
Abstract
In today’s tech-driven landscape, safeguarding organizational security is imperative. Threats can arise externally or internally, necessitating preemptive measures. Despite the prevalence of insider risks like data breaches and intellectual property theft, literature lacks exploration of artificial intelligence (AI) potential in detecting malicious insiders. This study proposes leveraging natural language processing (NLP) and powerful classifiers to analyze employee behavior, offering an efficient means to mitigate insider threats. Effective classifiers depend on precise parameter selection, requiring optimization to align algorithms with specific problems. However, hyperparameter tuning faces NP-hard challenges due to extensive search spaces. Specialized algorithms like metaheuristic optimizers are commonly employed but require careful pairing with problems. In order to identify insider risks, this study presents a unique method that combines extreme gradient boosting (XGBoost) with bidirectional encoder representations using transformers (BERT) to analyze personnel HTTP content access. Leveraging NLP enhances robustness against common firewall evasion tactics. Additionally, a modified reptile search algorithm (RSA) algorithm is proposed for hyperparameter selection, achieving accuracy exceeding 97% on simulated security datasets. This framework offers a promising avenue for preempting insider threats and bolstering organizational security.
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Jovanovic et al. (2024) proposed a method combining NLP with XGBoost and BERT to analyze employee behaviour, achieving accuracy exceeding 97% on simulated security datasets.
“Jovanovic et al. (2024) [53] proposed a method combining NLP with Extreme Gradient Boosting (XGBoost) and BERT to analyze employee behaviour, enhancing robustness against common firewall evasion tactics and achieving accuracy exceeding 97% on simulated security datasets using a modified reptile search algorithm for hyperparameter selection.”
From A systematic review on insider threat detection using natural language processing (2025) · cites this paper as [53]
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A systematic review on insider threat detection using natural language processing
Kundiya, K., & Haribhakta, Y. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01145-6 · Cited by 9 · see citing works
A Survey of Large Language Models for Insider Threat Detection
Haidar, A., Lin, Y.-Z., Shi, Q., Yang, Z., Amadigwe, D., Terry, B., Battu, S. K., Satam, P., & Shao, S. (2025) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars67163.2025.11337474 · Cited by 2 · see citing works
- Authors
- Jovanovic, L., Kaljevic, J., Zivkovic, M., Bacanin, N., Antonijevic, M., & Cajic, M.
- Affiliations
- Singidunum University,Faculty of Informatics and Computing,Belgrade,Serbia
- Singidunum University,Faculty of Health and Business Studies,Belgrade,Serbia
- Singidunum University (RS)
- Univerzitet Union Nikola Tesla (RS)
- Venue
- 2024 International Conference on Circuit, Systems and Communication (ICCSC)
- Publisher
- IEEE
- Published
- 2024-06-28
- Pages
- 1-6
- OpenAlex
- W4401361017
- Cited by
- 2
- References
- 34
- Citations by year
- 2025: 2
- FWCI
- 0.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Circuit, Systems and Communication (ICCSC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- insider threat detection, XGBoost, BERT, hyperparameter optimization, reptile search algorithm, metaheuristic optimization, firewall evasion, organizational security, data breach prevention
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