2026-05-04T18:32:29.245Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:11785 2026-01-06T18:30:58Z 3

Applied Modified Metaheuristic: Optimization for Anomaly Detection in Cloud-System Operational Logs, Chapter in LNNS Lecture Notes in Networks and Systems: WorldS4 2025: Intelligent Sustainable Systems, Springer, volume 1731 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/11785 https://link.springer.com/chapter/10.1007/978-3-032-11518-8_19 V. Kostic L. Jovanovic B. Radomirovic V. Zeljkovic E. Tuba M. Tuba M. Zivkovic N. Bacanin Efficient log monitoring is essential for ensuring the performance and reliability of scalable cloud computing systems. As these systems grow in complexity and scale, traditional monitoring approaches become less effective and more resource-intensive. This work explores the integration of natural language processing (NLP) techniques with advanced classification algorithms to identify anomalies in cloud-based log data. Given that classifier accuracy heavily relies on optimal hyperparameter configurations, a metaheuristic approach is employed to automate and refine the tuning process. A novel variant of the artificial bee colony (ABC) algorithm, named self-adaptive ABC (SAABC) is proposed and benchmarked using publicly available real-world datasets. Experimental results indicate strong performance, with the SAABC achieving an accuracy of .985275, demonstrating its potential for intelligent and scalable log anomaly detection. bookPart Springer, Cham 245 256 10.1007/978-3-032-11518-8_19 LNNS Lecture Notes in Networks and Systems: WorldS4 2025: Intelligent Sustainable Systems, volume 1731