The necessity to ensure that Internet of Things (IoT) networks are secure is one of the biggest issues that has arisen as a result of the growing demand for technology that uses the IoT. Considering how many gadgets are linked to the internet, safeguarding their networks is a growing worry. Due to the IoT's network's complexity and resource constraints, traditional intrusion detection systems encounter a number of problems. The main objectives of this project are to design, develop, and evaluate a hybrid level placement method for an IDS based on multi- agent systems, BC technology (Block-Chain), and DL algorithms (Deep Learning). The breakdown of data administration, data collection, analysis, and reaction into its component parts reveals the overall system design. The National Security Laboratory's knowledge discovery and data mining dataset is used to test the system as part of the validation procedure. These results demonstrate how deep learning algorithms are effective at identifying risks at the network and transport levels. The experiment shows that deep learning techniques function well when used to find intrusions in a network environment for the Internet of Things.

Objective: This study aims to address the growing network security risks posed by the increasing integration of IoMT infrastructure into medical facilities. The main goal is to develop and propose the Duo-Secure IoMT architecture that effectively counters complex cybersecurity threats such as botnet-driven distributed denial of service (DDoS) attacks and zero-day attacks while maintaining the integrity of clinical data. Methods: The Duo-Secure IoMT framework uses multimodal sensory cues to differentiate between typical IoMT data behaviour and patterns of malicious attacks. To analyze sensory data gathered by IoMT devices, it combines a stacked Bidirectional-LSTM model with a dynamic fuzzy clustering algorithm. By constantly monitoring and analyzing IoMT traffic, this two-stage methodology is intended to identify complex attack patterns within the network. The framework's capacity to anticipate cardiac abnormalities and identify network malware in a distributed IoMT network environment is illustrated using a case study of heart disease that includes 18,940 data points and 36 factors. Findings: The Duo-Secure IoMT framework demonstrates an impressive 92% accuracy rate in predicting cardiac anomalies, highlighting its effectiveness in medical diagnosis in the IoMT context. Additionally, it achieves 98.50% accuracy in detecting network malware, making it a reliable tool for identifying potential security threats in IoMT infrastructure. Novelty: This study makes a novel contribution by combining multimodal sensory data with sophisticated machine learning methods, specifically by combining stacked Bidirectional-LSTM models with dynamic fuzzy clustering. Traditional security techniques might overlook intricate attack patterns, but this method enables the Duo-Secure IoMT framework to recognize them. Additionally, by attaining high accuracy in malware detection and medical anomaly prediction, the framework offers a revolutionary way to increase cybersecurity resilience in IoMT-based healthcare facilities. Keywords: IoMT Security, Multi-modal Anomaly Detection, Bi-LSTM Architecture, Network Flow Analysis, Data Balancing Techniques, Feature Selection Methods