Malicious Insider Threat Detection Using Sentiment Analysis of Social Media Topics

Kenny, Matt; Pitropakis, Nikolaos; Sayeed, Sarwar; Chrysoulas, Christos; Mylonas, Alexios

doi:10.1007/978-3-031-65175-5_19

IFIP Advances in Information and Communication Technology

2024

DOI: 10.1007/978-3-031-65175-5_19

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Malicious Insider Threat Detection Using Sentiment Analysis of Social Media Topics

Matt Kenny,

Nikolaos Pitropakis,

Sarwar Sayeed

et al.

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2024

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Cited by 1 publication

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

Mladenovic,

Antonijevic,

Jovanovic

et al. 2024

Sci Rep

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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.

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

Mladenovic,

Antonijevic,

Jovanovic

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Malicious Insider Threat Detection Using Sentiment Analysis of Social Media Topics

Cited by 1 publication

References 18 publications

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

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

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