TY - GEN
T1 - Swarm Optimization for Enhanced Random Forest-Based IoT Security
AU - Siłka, Jakub
AU - Wieczorek, Michał
AU - Wiltos, Katarzyna
AU - Woźniak, Marcin
PY - 2025
Y1 - 2025
N2 - . This paper presents an interesting parameter selection method for random forest algorithms, using a proprietary algorithm based on the concept of swarm optimization, called the Orange Cat Algorithm. The research was conducted on a smart home intrusion detection dataset, known as the Smart Home Intrusion Detection Dataset. In today’s world of increasing complexity of Internet of Things (IoT) systems, especially in the area of smart home security, there is a need for effective intrusion detection mechanisms. The Orange Cat algorithm is specifically designed to find the extremes of a function in a small number of iterations which is particularly useful for finding parameters for random forests, which contributes to improving the accuracy of intruder detection. Experiments showed that the proposed approach achieved an impressive accuracy of 99.40%. These results confirm the effectiveness of the Orange Cat Optimization algorithm in the parameter selection process, contributing to improved reliability and robustness of detection systems and thus preventing intrusions in IoT-based smart homes.
AB - . This paper presents an interesting parameter selection method for random forest algorithms, using a proprietary algorithm based on the concept of swarm optimization, called the Orange Cat Algorithm. The research was conducted on a smart home intrusion detection dataset, known as the Smart Home Intrusion Detection Dataset. In today’s world of increasing complexity of Internet of Things (IoT) systems, especially in the area of smart home security, there is a need for effective intrusion detection mechanisms. The Orange Cat algorithm is specifically designed to find the extremes of a function in a small number of iterations which is particularly useful for finding parameters for random forests, which contributes to improving the accuracy of intruder detection. Experiments showed that the proposed approach achieved an impressive accuracy of 99.40%. These results confirm the effectiveness of the Orange Cat Optimization algorithm in the parameter selection process, contributing to improved reliability and robustness of detection systems and thus preventing intrusions in IoT-based smart homes.
KW - Cybersecurity
KW - IoT
KW - Orange Cat Optimization
KW - Random Forest Algorithm
UR - https://www.scopus.com/pages/publications/105010210420
U2 - 10.1007/978-3-031-81596-6_16
DO - 10.1007/978-3-031-81596-6_16
M3 - Conference contribution
AN - SCOPUS:105010210420
SN - 9783031815959
T3 - Lecture Notes in Computer Science
SP - 176
EP - 185
BT - Artificial Intelligence and Soft Computing - 23rd International Conference, ICAISC 2024, Proceedings
A2 - Rutkowski, Leszek
A2 - Korytkowski, Marcin
A2 - Scherer, Rafal
A2 - Tadeusiewicz, Ryszard
A2 - Pedrycz, Witold
A2 - Zurada, Jacek M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2024
Y2 - 16 June 2024 through 20 June 2024
ER -