MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641096318 A) filed by Dr. S. Bhuvana; Mr. Parimel Azhagan Kusala Kumaran; Ms. K. Anusuya; Dr. Shobana D; Dr. Praveen Talari; Mr. Nanda Kumar. E; Dr. V. Parimala; and Mr. D. Suresh M. E. , Ph. D on August 10, 2026, for Hybrid Ai And Blockchain Framework For Secure Next-Generation Communication Networks.
Inventors include Dr. S. Bhuvana; Mr. Parimel Azhagan Kusala Kumaran; Ms. K. Anusuya; Dr. Shobana D; Dr. Praveen Talari; Mr. Nanda Kumar. E; Dr. V. Parimala; and Mr. D. Suresh M. E. , Ph. D.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: A hybrid artificial intelligence (AI)/blockchain framework is proposed for enabling secure next-generation communication networks. The framework is designed to utilize the inherent qualities of AI, such as decision-making ability and self-learning capacity, and blockchain, including distributed storage and immutability, to empower networks with the ability to provide ultra-reliable and trustworthy communications. The AI component takes advantage of its self-learning capability to constantly monitor the traffic patterns in the network and predict potential security threats as well as optimize the network performance. The framework also uses the blockchain component to store information about network events of interest, which helps to provide transparency and facilitates the auditing process, thus increasing the security level of the network. The hybrid nature of the framework allows these two components to work together and benefit from each other’s qualities. The two components are intertwined because the decisions made by the AI part of the framework are stored in the blockchain, while the rules defined in smart contracts running on the blockchain protect the AI models and control their learning process. The proposed framework is focused on addressing the most persistent threats to communication networks, including denial-of-service (DoS) and jamming attacks, unauthorized access, and traffic integrity violations. It can be applied to future networks with extremely high data rates, a huge number of devices, and very low latencies, which are also susceptible to other threats, for example, machine learning (ML) data poisoning. First of all, the framework secures such networks by constantly monitoring them for signs of cyberattacks using AI algorithms. The AI component of the framework analyzes network traffic patterns and device behavior to identify suspicious activities or deviations from standard patterns. Since networks with extremely high data rates and a tremendous number of devices require a significant amount of traffic to be processed, such frameworks need optimization. The AI component can help such networks by constantly analyzing network traffic and predicting the most probable threats that such networks will face. Moreover, the ML models running on the AI component can be trained to detect novel and sophisticated attacks that conventional attack detection methods may fail to identify. The blockchain component of the framework can then store the decisions made by the AI component and all network events of interest. The information about network events can be helpful for auditing purposes, as it provides transparency about the decisions made in the network. Additionally, storing this information on a blockchain adds another level of security because it would be almost impossible to alter this information without the agreement of the majority of nodes in the blockchain network. Smart contracts running on the blockchain can add another level of security by, for example, controlling and verifying access to the network resources, such as devices and data. The use of smart contracts, however, might introduce vulnerabilities that malicious actors could exploit. The hybrid nature of the framework makes it advantageous over conventional communication network protection frameworks in several ways. The framework utilizes the self-learning capabilities of the AI component and the high level of security provided by the blockchain component to enhance the security of next-generation networks. The AI component benefits from the high level of security offered by the blockchain component, which protects it from potential threats. Meanwhile, the blockchain component relies on the computational power of the AI component to make decisions that enhance the security of the network. These decisions are then stored on the blockchain component. By combining these two technologies, the framework is able to offer enhanced security while still being able to scale to meet the needs of future networks with extremely high data rates and a tremendous number of devices. In addition, the use of such a framework ensures privacy, as it allows the AI component to process network traffic data without accessing the actual data. Finally, the confidentiality of the data is ensured if such a framework is implemented with appropriate cryptographic tools, while anonymity can also be achieved if required by the particular use case.
Disclaimer: Curated by HT Syndication.
