ArticleScientific reports2024
Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Comparative evaluation of natural language processing approaches with particle swarm optimized LightGBM for anomaly detection in cloud system logs.Scientific reports · 2026Article
- An enhanced connected banking system optimizer with multiple strategies for numerical optimization problems.Scientific reports · 2026Article
- Keyphrase extraction by the use of glove and ResNeXt optimized by enhanced human evolutionary optimization (EHEO) algorithm.Scientific reports · 2025Article
- Research topic detection in scientific articles using a hybrid BERT integrated telescopic vector tree model with emperor penguin enhanced NSGA II optimization.Scientific reports · 2025Article
- An optimized bidirectional recurrent neural network for kidney stone detection based on developed bald eagle search method in CT scan images.Scientific reports · 2025Article
- Unmasking insider threats using a robust hybrid optimized generative pretrained neural network approach.Scientific reports · 2025Article
- Towards precision agriculture: metaheuristic model compression for enhanced pest recognition.Scientific reports · 2025Article
- Emotion-Aware RoBERTa enhanced with emotion-specific attention and TF-IDF gating for fine-grained emotion recognition.Scientific reports · 2025Article
- A dynamic multitask evolutionary algorithm for high-dimensional feature selection based on multi-indicator task construction and elite competition learning.Frontiers in artificial intelligence · 2025Article
- ULBERT: a domain-adapted BERT model for bilingual information retrieval from Pakistan's constitution.Frontiers in big data · 2025Article
- Determining the meter of classical Arabic poetry using deep learning: a performance analysis.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.