{'fraction_non_alphanumeric': 0.01611721611721612, 'fraction_numerical': 0.0007326007326007326, 'mean_word_length': 4.763713080168777, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
IoT Group Key Management using Incremental Gaussian Mixture Model

Sensors and the internet are used in the Internet of Things (IoT) to link physical and computer networks. Future IoT development will be heavily influenced by machine learning and cloud computing. Wi-Fi and ethernet are the most common methods of connecting IoT devices to the internet today. Because of the great range and low latency of the 5G cellular network, most gadgets will choose it in the future. This study discusses how to overcome the limitations of early key management systems and provides a key mechanism that is more effective in the Internet of Things context. Prior to this, the network had been deployed and was being operated by interactions using a mathematical structure known as the blom’s key pre- distribution approach. In the event that a new user needs to join the network, the system generates a random group and distributes the secret key. Extra time is required for both processing and communication, as is evident from the outset. To reduce the time it takes to maintain the key distribution within a group or network, this article outlines the use of machine learning principles. Subgroups based on features will likewise be created by this method In addition, the nodes are grouped into viewpoint subgroups, and the secret key is successfully sent among the nodes.