Optimising Insider Threat Prediction: Exploring BiLSTM Networks and Sequential Features DOI Creative Commons

Phavithra Manoharan,

Wei Hong, Jiao Yin

et al.

Data Science and Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

Abstract Insider threats pose a critical risk to organisations, impacting their data, processes, resources, and overall security. Such significant risks arise from individuals with authorised access familiarity internal systems, emphasising the potential for insider compromise integrity of organisations. Previous research has addressed challenge by pinpointing malicious actions that have already occurred but provided limited assistance in preventing those risks. In this research, we introduce novel approach based on bidirectional long short-term memory (BiLSTM) networks effectively captures analyses patterns individual sequential dependencies. The focus is predicting whether an would be future day daily behavioural records over previous several days. We analyse performance four supervised learning algorithms manual features, ground truth different combinations. addition, investigate RNN models, such as RNN, LSTM, BiLSTM, incorporating these features. Moreover, explore predictive lengths embedded All experiments are conducted CERT r4.2 dataset. Experiment results show BiLSTM highest combining

Language: Английский

STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network DOI
Pramod Kachare, Sandeep B. Sangle, Digambar Puri

et al.

Cognitive Neurodynamics, Journal Year: 2024, Volume and Issue: 18(5), P. 3195 - 3208

Published: July 19, 2024

Language: Английский

Citations

13
LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection DOI
Pramod Kachare, Digambar Puri, Sandeep B. Sangle

et al.

Physical and Engineering Sciences in Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: June 11, 2024

Language: Английский

Citations

11
Unlocking the Potential of EEG in Alzheimer's Disease Research: Current Status and Pathways to Precision Detection DOI Creative Commons

Faisal Akbar,

Imran Taj,

Syed Muhammad Usman

et al.

Brain Research Bulletin, Journal Year: 2025, Volume and Issue: unknown, P. 111281 - 111281

Published: March 1, 2025

Alzheimer's disease (AD) affects millions of individuals worldwide and is considered a serious global health issue due to its gradual neuro-degenerative effects on cognitive abilities such as memory, thinking, behavior. There no cure for this but early detection along with supportive care plan may aid in improving the quality life patients. Automated AD challenging because symptoms vary patients genetic, environmental, or other co-existing conditions. In recent years, multiple researchers have proposed automated methods using MRI fMRI. These approaches are expensive, poor temporal resolution, do not offer real-time insights, proven be very accurate. contrast, only limited number studies explored potential Electroencephalogram (EEG) signals detection. present cost-effective, non-invasive, high-temporal-resolution alternative Despite their potential, application EEG research remains under-explored. This study reviews publicly available datasets, variety machine learning models developed detection, performance metrics achieved by these methods. It provides critical analysis existing approaches, highlights challenges, identifies key areas requiring further investigation. Key findings include detailed evaluation current methodologies, prevailing trends, gaps field. What sets work apart in-depth Disease providing stronger more reliable foundation understanding role area.

Language: Английский

Citations

1
A heterogeneous graph-based semi-supervised learning framework for access control decision-making DOI Creative Commons
Jiao Yin, Guihong Chen, Wei Hong

et al.

World Wide Web, Journal Year: 2024, Volume and Issue: 27(4)

Published: May 24, 2024

Abstract For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security. This paper proposes a novel semi-supervised learning framework that leverages heterogeneous graph neural network-based embedding to encapsulate both intricate relationships within organizational structure interactions between users resources. Unlike existing methods focusing solely on individual user resource attributes, our approach embeds operational interrelationships into hidden layer node embeddings. These embeddings learned from self-supervised link prediction task based constructed via network. Subsequently, embeddings, along with original features, serve as inputs for supervised decision-making task, facilitating construction of machine-learning model. Experimental results open-sourced Amazon dataset demonstrate proposed outperforms models using or manually extracted graph-based features previous works. The prepossessed codes available GitHub,facilitating reproducibility further research endeavors.

Language: Английский

Citations

4
Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data DOI Creative Commons
Siuly Siuly, Md. Nurul Ahad Tawhid, Yan Li

et al.

Cognitive Computation, Journal Year: 2025, Volume and Issue: 17(2)

Published: April 1, 2025

Language: Английский

Citations

0
Dual-Transformer Cross-Attention Framework for Alzheimer's Disease Detection Via Dpte-Guided Eeg Channel Selection and Multi-Modal Integration DOI

Shyamal Y. Dharia,

Qian Liu, Stephen M. Smith

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection DOI Creative Commons

Ilknur Sercek,

Niranjana Sampathila, İrem Taşçı

et al.

Cognitive Neurodynamics, Journal Year: 2025, Volume and Issue: 19(1)

Published: May 10, 2025

Abstract Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: retrospectively analyzed the EEG records 134 and 113 non-AD patients. To generate multilevel features, discrete wavelet transform was used decompose input EEG-signals. devised novel quantum-inspired EEG-signal extraction function 7-distinct different subgraphs Goldner-Harary pattern (GHPat), selectively assigned specific subgraph, using forward-forward distance-based fitness function, each block textural extraction. extracted statistical features standard moments, which we then merged with features. Other components were iterative neighborhood component analysis selection, shallow k-nearest neighbors, as well majority voting greedy algorithm additional voted prediction vectors select best overall results. With leave-one-subject-out cross-validation (LOSO CV), our attained 88.17% accuracy. Accuracy results stratified by channel lead placement brain regions suggested P4 parietal region be most impactful. Comparison existing methods: The proposed outperforms methods achieving higher accuracy approach, ensuring robustness generalizability. Cortex maps generated that allowed visual correlation channel-wise various regions, enhancing explainability.

Language: Английский

Citations

0
Initial-LLM: A Large Language Model-Guided Metaheuristic Framework for Enhanced Feature Selection in Clinical Decision Support Systems DOI
Zihang Wang, Ye Liang, Wenwei Sun

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 27 - 35

Published: Jan. 1, 2025

Language: Английский

Citations

0
Comparative Study of Machine Learning Algorithms for IoT Cyber Threat Detection in Healthcare Information Systems DOI
Tien Ngo, Jiao Yin, Yong-Feng Ge

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 68 - 77

Published: Jan. 1, 2025

Language: Английский

Citations

0
Certain Studies on Alzheimer's disease: A Comprehensive Review DOI Creative Commons
Viswanathan Arunachalam, Chitra Sarkar,

K. S. Lakshmi

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(06), P. 1537 - 1550

Published: June 5, 2024

The brain serves as the central control centre for our body, and time progresses, an increasing number of new diseases are being identified. A disease is any medical problem or disorder that interferes with brain's normal functioning. This review briefs about various types deep learning models neurological disorders, in addition to neurodegenerative conditions like Parkinson's Alzheimer's. In dataset identifiers commonly used primary source data reviewed studies, forty other methodologies examined. AUC, sensitivity, specificity, accuracy, performance evaluation parameters have also been addressed recorded. key findings from articles briefly summarized, several major issues regarding machine learning-based diagnostic approaches discussed.

Language: Английский

Citations

0