2026-06-22T17:53:11.437Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11905 2026-04-26T22:36:12Z 1

Applied Metaheuristic Hyperparameter Tuning: Machine Learning in Internet of Things Cybersecurity 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11905 https://ieeexplore.ieee.org/abstract/document/11479528 N. Bacanin L. Jovanovic M. Zivkovic C. Stoean T. Zivkovic M. Antonijevic The Internet of Things (IoT) plays an ever increasingly important role in everyday life. Devices with constant internet connectivity are now common in homes, industrial and other professional environments, as well as part of smart energy grids and management systems. However, given the often limited computational resources of these devices, security is often given lower priority. As IoT devices can access the Internet, they can be used as parts of botnets and used to execute various cyberattacks on the network. Such vulnerabilities are especially crucial when they concern critical infrastructure and data. This study evaluates the effectiveness of a machine learning (ML) based solution for IoT threat detection. Given the dynamic nature of IoT networks, and the continuous development of new vectors of attack, an adaptive approach needs to be taken to maintain security. A modified metaheuristic optimizer is presented and used to tune classifier hyperparameters to ensure favorable performance. The solution being introduced has been applied to authentic data and the simulations suggest promising outcomes. The best profiteering models reach an accuracy rating of 0.99361. conferenceObject 449 455 10.1109/SYNASC69064.2025.00066 2025 27th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), IEEE, Timisoara, Romania