Adaptive Threat Detection Framework for IoT-Enabled Healthcare, Financial, and Connected Systems in the United States

Internet of Things (IoT) technologies have significantly transformed healthcare, finance, and smart connected systems by providing real-time communication and automation and intelligent data processing. Although all these developments have occurred, the fact that such a large number of IoT devices are becoming interconnected has created significant cybersecurity concerns and new opportunities for sophisticated cyber threats like Distributed Denial of Service (DDoS), botnet attacks, brute force attacks, spoofing, and malware attacks to target critical infrastructures. In IoT environments, which can be dynamic and heterogeneous, traditional intrusion detection systems are not effective in detecting new and changing attack patterns. This work introduces an Adaptive AI-Driven Threat Detection Framework aimed at safeguarding IoT healthcare, financial, and connected systems, which entails intelligent detection of anomalies and threat analysis in real time. The proposed framework leverages a realistic IOT network traffic dataset called CICIoT2023 that includes a wide variety of attack categories and realistic network traffic for modern cybersecurity research. Technologies such as Advanced machine learning algorithms, deep learning algorithms including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Random Forest are applied to classify malicious traffic and benign traffic efficiently. The framework includes adaptive learning mechanisms that are able to continually analyze the behaviors of the network and increase the accuracy of intrusion detection and decrease false positive rates. To improve the performance of the system and to optimize the computational time, data pre-processing, feature extraction, normalization, and model optimization techniques are used. Experimental results prove that the proposed scheme is accurate, precise, recall and F1-score in the detection of cyber threats in heterogeneous IoT networks. This study plays a key role in advancing the field of scalable and intelligent cybersecurity solutions designed to safeguard sensitive healthcare data, financial transactions, and smart infrastructures that are integrated into the digital landscape. The proposed architecture also offers important lessons in the integration of AI and adaptive security mechanisms into future IoT cybersecurity solutions.