A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods

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. 

Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers

Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate the data. This article focuses around the anomalies under different contexts. This research has proposed methodology to secure Electronic Health Records (EHRs) within a complex environment. We have employed a systematic approach encompassing data preprocessing, labeling, modeling, and evaluation. Anomalies are not labelled thus a mechanism is required that predicts them with greater accuracy and less false positive results. This research utilized unsupervised machine learning algorithms that includes Isolation Forest and Local Outlier Factor clustering algorithms. By calculating anomaly scores and validating clustering through metrics like the Silhouette Score and Dunn Score, we enhanced the capacity to secure sensitive healthcare data evolving digital threats. Three variations of Isolation Forest (IForest)models (SVM, Decision Tree, and Random Forest) and three variations of Local Outlier Factor (LOF) models (SVM, Decision Tree, and Random Forest) are evaluated based on accuracy, sensitivity, specificity, and F1 Score. Isolation Forest SVM achieves the highest accuracy of 99.21%, high sensitivity (99.75%) and specificity (99.32%), and a commendable F1 Score of 98.72%. The Isolation Forest Decision Tree also performs well with an accuracy of 98.92% and an F1 Score of 99.35%. However, the Isolation Forest Random Forest exhibits lower specificity (72.84%) than the other models. The experimental results reveal that Isolation Forest SVM emerges as the top performer showcasing the effectiveness of these models in anomaly detection tasks. The proposed methodology utilizing isolation forest and SVM produced better results by detecting anomalies with less false positives in this specific EHR of a hospital in North England. Furthermore the proposal is also able to identify new contextual anomalies that were not identified in the baseline methodology. 

pdf/anomaly-based-threat-detection-in-smart-health-using-machine-2fetpn7b9lj7.pdf

Anomaly-based threat detection in smart health using machine learning

Insider threat mitigation: Systematic literature review

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. This study proposes a comprehensive taxonomy as well as a state-of-the-art research categorization according to the contribution of insider threat incidents and the defensive mechanism utilized against such insiders. The major objective of a proposed categorization is to provide structural information in the field of insider threat based on past research theories for analyzing literature review. The proposed categorization is classified into four groups: (i) dataset analysis, (ii) incident analysis, (iii) defensive solution, and (iv) encountered challenges. However, the respective taxonomies and annotations are included for complete insight into insiders. i.e., existing studies on systematic taxonomy based on incidents of insider threats are presented. The major contribution of this study in the area of insider threat is to deliver the following knowledge to upcoming domain specific researchers: (i) taxonomy in an innovative systematic approach concerning the categories of incidents and determine the possible defensive mechanism against insiders. (ii) a study on available benchmark datasets used by existing research for evaluating the defensive mechanisms. (iii) a brief description of past solutions and frameworks to model insider behavior with the aim of studying existing defensive mechanisms, and (iv) a short discussion of challenges encountered by defensive solutions based on existing research in the area of insider threat. 

Understanding insiders in cloud adopted organizations: A survey on taxonomies, incident analysis, defensive solutions, challenges

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Malicious insider threat detection using variation of sampling methods for anomaly detection in cloud environment

Understanding insiders in cloud adopted organizations: A survey on taxonomies, incident analysis, defensive solutions, challenges

Introduction to the special section on cyber-physical system for autonomous process control in industry 5.0