2026-05-16T00:57:29.085Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:10146 2024-12-03T20:10:47Z 3

Leveraging Metaheuristic Optimization to Enhance Insider Threat Detection Through Email Content Natural Language Processing, Chapter in LNNS Lecture Notes in Networks and Systems: INFUS 2024: Intelligent and Fuzzy Systems, Springer, volume 1089 2024 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/10146 https://link.springer.com/chapter/10.1007/978-3-031-67195-1_63 N. Bacanin L. Jovanovic S. Janicijevic M. Antonijevic M. Sarac M. Zivkovic Ensuring operational integrity in the ever-evolving internet landscape requires robust cybersecurity measures to combat emerging security compromise vectors. Despite traditional techniques, insider threats pose a significant challenge, exploiting the human element and contributing to substantial cyberattack losses. On-premises attacks, facilitated by physical access, defy conventional detection methods, necessitating alternative approaches. This study explores the integration of natural language processing (NLP) with the AdaBoost classifier for identifying malicious intent in email content, utilizing publicly available datasets to detect insider threats. Optimizing model hyperparameters through various algorithms, including a modified elk herd optimization algorithm, enhances performance. The best-performing model is analyzed in-depth, uncovering key terms crucial for malicious intent detection and their roles in the classification process. bookPart Springer, Cham 569 577 10.1007/978-3-031-67195-1_63 LNNS Lecture Notes in Networks and Systems: INFUS 2024: Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference, volume 1089