A Simulation-Based Approach to Development of a New Insider Threat Detection Technique: Active Indicators

Yerdon, Valarie; Wohleber, Ryan W.; Matthews, Gerald; Reinerman-Jones, Lauren

doi:10.1007/978-3-319-94782-2_1

Cited by 2 publications

(2 citation statements)

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“…Heuristic-based insider threat detection approaches in [82], [83] present proactive ways to identifying potential malicious activities by establishing a set of rules, guidelines, or heuristics that define normal behavior within an organization's network or information systems. Unlike signature-based detection, which relies on predefined patterns, heuristic-based methods focus on recognizing deviations from established norms.…”

Section: ) Heuristic-based Detection Methodsmentioning

confidence: 99%

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

Alzaabi,

Mehmood

2024

IEEE Access

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Insider threat detection has become a paramount concern in modern times where organizations strive to safeguard their sensitive information and critical assets from malicious actions by individuals with privileged access. This survey paper provides a comprehensive overview of insider threat detection, highlighting its significance in the current landscape of cybersecurity. The review encompasses a broad spectrum of methodologies and techniques, with a particular focus on classical machine-learning approaches and their limitations in effectively addressing the intricacies of insider threats. Furthermore, the survey explores the utilization of modern deep learning and natural language processing (NLP) based methods as promising alternatives, shedding light on their advantages over traditional methods. This analysis underscores the need for sophisticated solutions that can adapt to evolving threat landscapes and accommodate the intricacies of human behavior. In the conclusion section, the paper offers valuable insights into the future directions of insider threat detection. It advocates for the integration of more sophisticated time-series-based techniques, recognizing the importance of temporal patterns in insider threat behaviors. Additionally, the survey underscores the potential of NLP and large language model-based approaches, which can enhance threat detection by deciphering textual and contextual information. These recommendations reflect the evolving nature of insider threats and emphasize the need for proactive, datadriven strategies to safeguard organizations against internal security breaches. In conclusion, this survey not only underscores the urgency of addressing insider threats but also provides a roadmap for the adoption of advanced methodologies to enhance detection and mitigation capabilities in contemporary cybersecurity paradigms.

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Section: ) Heuristic-based Detection Methodsmentioning

confidence: 99%

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

Alzaabi,

Mehmood

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For example, the perfection of neuroergonomic techniques might materialize thought into action, all in a moment. Here, a stray idea or passing fancy is then enacted, even if the individual regrets such thoughts almost he moment they have them (Yerdon et al, 2018; Matthews et al, 2019). Connections here to advanced weaponry will render the outcome of some such actions as irreversible.…”

Section: The Dangers Of Turning the Mind Inside-outmentioning

confidence: 99%

Neuroergonomics: Where the Cortex Hits the Concrete

Hancock

2019

Front. Hum. Neurosci.

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A Simulation-Based Approach to Development of a New Insider Threat Detection Technique: Active Indicators

Cited by 2 publications

References 15 publications

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

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

Neuroergonomics: Where the Cortex Hits the Concrete

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