The University of Akron

Jin Kocsis

Yifu Wu

Gihan Janith Mendis Imbulgoda Liyangahawatte

Mututhanthrige Pravenn Sameera Fernando

Mututhanthrige Praveen Sameera Fernando

In various embodiments, the present invention relates to a blockchain-powered SDN-enabled networking infrastructure in which the integration between blockchain-based security and autonomy management layer and multi-controller SDN networking layer is defined to enhance the integrity of the control and management messages. In one or more embodiments, this networking infrastructure is utilized achieve three main functionalities: (1) integrity verification of control and management commands for cloud platforms, (2) identification of the malicious hosts abusing the cloud platform, and (3) enhancing the availability of the cloud platform via autonomous bandwidth provisioning.

Blockchain-powered cloud management system

In one or more embodiments, the present invention is directed to a blockchain secured, software-defined network and monitoring system comprising: a multi-controller software-defined network (SDN) network layer; a blockchain based security and autonomy layer; a deep learning-driven decision making layer comprising the one or more computational centers and a horizontal data plane layer. In some embodiments, the present invention is directed to methods for ensuring the integrity of a control commands and optimizing performance and security using the blockchain secured, software-defined network and monitoring system. In various embodiments, the present invention relates to methods for extracting useful features from said labelled and non-labelled data contained in the horizontal data plane layer in the blockchain secured, software-defined network and monitoring system using a knowledge domain-enabled hybrid semi-supervision learning method.

3S-chain: smart, secure, and software-defined networking (SDN)-powered blockchain-powered networking and monitoring system

In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.

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Aurora, OH, United States of America

Jin Kocsis