Design of advanced intrusion detection system for multimodal fuzzy-based convolutional neural networks (mFCNN)

Despite their advancement, new IoT networks are vulnerable to a wide range of security threats and vulnerabilities due to the inefficient processing power of these devices [4]. Due to technological advancements, IDS are now extremely effective network security tools. There has been a significant increase in IoT device usage in recent years. Devices with IoT capabilities can connect to the internet and transmit data [5], accessible and controllable from any location due to the internet. IoT is a network connecting physical and digital devices, not just a computer network [6]. It has application domains in industry, home automation, healthcare, and other innovative technologies. ML, particularly deep learning (DL), is gaining popularity in various scientific fields, including IoT [7]. DL approaches are automatically used in IoT security for detecting network intrusions and malware. For example, the