Abstract. The rapid development of the Internet of Things (IoT) is accompanied by a growing number of connected devices and an increasing volume of cyberattacks, which necessitates the development of effective and scalable protection mechanisms. This paper proposes IoTector, a platform for network traffic monitoring and cyberattack detection in IoT networks. IoTector is designed as an intelligent gateway deployed between IoT devices and the network infrastructure, providing real-time detection of attacks and anomalies. To analyze network traffic, the system employs deep learning models, including DNN, CNN, and CNN–BiLSTM, enabling effective detection of various attack types. A prototype of the platform was implemented on Raspberry Pi 5 and supports device connectivity through wireless technologies such as Wi-Fi and Bluetooth. In addition, a software interface was developed to provide network status monitoring, threat visualization, device management, and support for model training. Unlike traditional intrusion detection systems mainly focused on centralized traffic analysis, the proposed approach integrates intelligent filtering, monitoring, and management within a unified platform. The obtained results confirm the high effectiveness of IoTector in detecting attacks and anomalies, as well as its practical applicability in real-world IoT environments.

Keywords: Internet of Things, IoT security, intrusion detection system, IoTector, federated learning, deep learning, network traffic monitoring, distributed systems.