Enhanced Intrusion Detection System in IOT-MQTT Using Machine Learning Classifiers

Enhanced Intrusion Detection System in IOT-MQTT Using Machine Learning Classifiers

Ms.R.Leelavathi, M.E.1,Dr PMVijayan,M.Tech2,(ph.D) V.SaiMounika3, S.GoldenReddy4, B.Suvarna5, B.Vikas6, N.Tejeshwar7

1Associate Professor of ECE dept, Siddartha Institute of Science and Technology JNTUA University, INDIA 2Associate Professor of ECE dept, Siddartha Institute of Science and Technology JNTUA University, INDIA 3,4,5,6,7Students of ECE dept, Siddartha Institute of Science and Technology JNTUA University, INDIA

E-mail:saimounikaveluru@gmail.com

Abstract:

This study significantly enhances IoT security by employing state-of-the-art techniques to detect a range of attacks, including as DoS, brute force, and malformed packets. The hybrid classifier that combines a 1D CNN and fuzzy logic is optimized via the Improved Vulture Starvation-based African Vultures Optimisation Algorithm (IVS-AVOA). The system further enhances attack detection by employing an ensemble classifier that uses models such as Random Forest, XGBoost, and LightGBM, augmented by TSO. The hybrid solution has a 98.5% detection accuracy and a 1.2% false-positive rate, which is better than standalone rule-based orML-driven IDS. The solution offers a robust and adaptable defence mechanism, improving security in vast IoT contexts by utilizing a publically available MQTT dataset and scalable, efficient, and dependable methodologies.

Keywords: IoT Security, MQTT protocol, IDS, Machine learning classifiers (Fuzzy Logic, 1DCNN)