A review of recent advances, challenges, and opportunities in malicious insider threat detection using machine learning methods
FR Alzaabi, A Mehmood - IEEE Access, 2024 - ieeexplore.ieee.org
Insider threat detection has become a paramount concern in modern times where
organizations strive to safeguard their sensitive information and critical assets from …
organizations strive to safeguard their sensitive information and critical assets from …
Machine learning approaches to detect, prevent and mitigate malicious insider threats: State-of-the-art review
Insider threats are profoundly damaging and pose serious security challenges. These
threats, perpetrated by insiders, may arise from delinquency, retaliation, or motives such as …
threats, perpetrated by insiders, may arise from delinquency, retaliation, or motives such as …
Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers
This study examines the formidable and complex challenge of insider threats to
organizational security, addressing risks such as ransomware incidents, data breaches, and …
organizational security, addressing risks such as ransomware incidents, data breaches, and …
Anomaly-based threat detection in smart health using machine learning
Background Anomaly detection is crucial in healthcare data due to challenges associated
with the integration of smart technologies and healthcare. Anomaly in electronic health …
with the integration of smart technologies and healthcare. Anomaly in electronic health …
Handling imbalance dataset issue in insider threat detection using machine learning methods
Insider threats, characterized by their baleful impact and substantial costs, arise from internal
factors within organizations. These threats are rare and usually unnoticed, as the malicious …
factors within organizations. These threats are rare and usually unnoticed, as the malicious …
Unsupervised novelty detection for time series using a deep learning approach
Abstract In the Smart Homes and IoT devices era, abundant available data offers immense
potential for enhancing system intelligence. However, the need for effective anomaly …
potential for enhancing system intelligence. However, the need for effective anomaly …
Understanding insiders in cloud adopted organizations: A survey on taxonomies, incident analysis, defensive solutions, challenges
S Asha, D Shanmugapriya - Future Generation Computer Systems, 2024 - Elsevier
In cybersecurity, one of the most significant challenges is an insider threat, in which existing
researchers must provide an extensive solution aiming at an enhanced security network …
researchers must provide an extensive solution aiming at an enhanced security network …
AUTH: An Adversarial Autoencoder Based Unsupervised Insider Threat Detection Scheme for Multisource Logs
X Zhu, J Dong, J Qi, Z Zhou, Z Dong… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Deep learning has shown broad research prospects in addressing insider threats, a serious
problem currently facing industrial information systems. Although deep learning is able to …
problem currently facing industrial information systems. Although deep learning is able to …
Deep Learning for Anomaly Detection in Time-Series Data: An Analysis of Techniques, Review of Applications, and Guidelines for Future Research
Industries are generating massive amounts of data due to increased automation and
interconnectedness. As data from various sources becomes more available, the extraction of …
interconnectedness. As data from various sources becomes more available, the extraction of …
Zero-SAD: Zero-Shot Learning Using Synthetic Abnormal Data for Abnormal Behavior Detection on Private Cloud
While many studies have been conducted to detect abnormal behavior in cloud
environments by analyzing system call sequences, these studies often cannot be applied to …
environments by analyzing system call sequences, these studies often cannot be applied to …