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Research Paper | Computer Science | Zimbabwe | Volume 12 Issue 6, June 2023 | Rating: 5.2 / 10
The Development of an AI-Based Network Security Algorithm for an IoT Healthcare Platform
Keith Lungile Ncube | Mainford Mutandavari
Abstract: The Internet of things is made up of all IPv6-capable hardware that is linked to and communicates with one another via the Internet. Our civilization uses this common phenomenon on a daily basis. Two of the main obstacles in large-scale IoT installations are data privacy and security. This is especially true for important applications like Industry 4.0 and e-healthcare. Securing the IoT-cloud ecosystem for healthcare data is one of the hardest and tough issues of today. The IoT Cloud infrastructure is particularly susceptible to flaws and attacks because of the numerous sensors utilized to produce enormous amounts of data. This can make the network less secure. The finest technology for healthcare applications is artificial intelligence (AI), as it provides the best method for enhancing data security and reliability. The IoT cloud framework already uses a number of AI-based security mechanisms. Significant flaws in existing algorithms include complicated algorithm design and ineffective data processing. Additionally, they are unsuitable for analyzing unstructured data, which raises the price of IoT sensors. In order to improve the security and privacy of healthcare data stored in IoT clouds, this study introduces Probabilistic Super Learning (PSL) and Random Hashing (RH), two AI-based intelligence feature learning mechanisms. This research also employs the suggested learning approach to reduce the price of IoT sensors. The initial assault is discovered using this training model. The attack's properties are then changed in order to learn how attacks operate. Additionally, the data matrix's hash values are used to generate the random key. Elliptic Curve Cryptography is linked with this method for data security. The upgraded ECC-RH technique uses randomly generated hash keys to encrypt and decode data. Performance evaluation compares and validates the outcomes of various methodologies. A secure network layer is provided for IoT apps connected across 5G networks and beyond in the context of the final analysis of bio-inspired algorithms.
Keywords: Cloud Computing, Healthcare System, 5th Generation Network; Artificial Intelligence; Biological; Internet of Things (IoT); Network layer; Security; Wireless Sensor Networks
Edition: Volume 12 Issue 6, June 2023,
Pages: 297 - 306