Abstract: A method for executing a smart recall of a plurality of manufactured units is provided. The method may include, inter alia, using a database architecture to administer a supply chain for the units, assigning each component of the units a distributed ledger (blockchain) identification (ID), and additional pieces of information such as a serial number of the component, a date of manufacture of the component, a location of manufacture of the component, an ID associated with the source of the component, and/or the ID(s) of one or more subcomponent unit(s) of the component. The assigned information is stored in the database architecture. The method may also include generating a private database supplier key and a manufacturer key. The method further includes: transmitting a notice to downstream manufacturers, transmitting a notice to a regulatory agency to implement safety procedures and/or unit and transmitting a recall notice.

Abstract: A method is provided for preventing an IoT device within a trusted system from being harnessed in a malicious DDOS attack. The method may include bombarding the IoT device. The bombardment may originate from within the system, and may inundate the IoT device with harmless packets in a manner mimicking a traditional DOS attack. The inundating may utilize the resources of the IoT device to respond to the bombardment, and may thereby render the IoT device unavailable for fraudulent uses.

Abstract: A method is provided for preventing an IoT device within a trusted system from being harnessed in a malicious DDOS attack. The method may include bombarding the IoT device. The bombardment may originate from within the system, and may inundate the IoT device with harmless packets in a manner mimicking a traditional DOS attack. The inundating may utilize the resources of the IoT device to respond to the bombardment, and may thereby render the IoT device unavailable for fraudulent uses.

Abstract: A method is provided for preventing an IoT device within a trusted system from being harnessed in a malicious DDOS attack. The method may include bombarding the IoT device. The bombardment may originate from within the system, and may inundate the IoT device with harmless packets in a manner mimicking a traditional DOS attack. The inundating may utilize the resources of the IoT device to respond to the bombardment, and may thereby render the IoT device unavailable for fraudulent uses.

Abstract: Methods, systems, and apparatuses for blockchain-based unexpected data detection are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive a plurality of network function requests corresponding to the decentralized network. The node may analyze the plurality of network function requests to identify whether the requests included unexpected data and/or irregular data, and/or are associated with flagged wallets and/or smart contracts.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods, systems, and apparatuses for blockchain-based unexpected data detection are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive a plurality of network function requests corresponding to the decentralized network. The node may analyze the plurality of network function requests to identify whether the requests included unexpected data and/or irregular data, and/or are associated with flagged wallets and/or smart contracts.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Apparatus and methods for a reverse proxy server are provided. The reverse proxy server may provide compatibility and security for nodes on an IoT network. A reverse proxy server may normalize inter-node communications. A reverse proxy server may identify an IoT node and upon identification of the node, determine native communication protocol(s) specific to the node. The reverse proxy server may encapsulate data transmitted via the native communication protocol. The reverse proxy server may impose a master-slave relationship. For example, the reverse proxy server may be a master of one or more nodes (slaves) on the IoT. The reverse proxy server may enforce security protocols on all slaves. A slave (e.g., node) may be required to route all communications to other IoT devices through the master (e.g., reverse proxy server). An IoT network may include more than one reverse proxy server. Each slave may have one or more masters.

Abstract: A method for executing a smart recall of a plurality of manufactured units is provided. The method may include, inter alia, using a database architecture to administer a supply chain for the units, assigning each component of the units a distributed ledger (blockchain) identification (ID), and additional pieces of information such as a serial number of the component, a date of manufacture of the component, a location of manufacture of the component, an ID associated with the source of the component, and/or the ID(s) of one or more subcomponent unit(s) of the component. The assigned information is stored in the database architecture. The method may also include generating a private database supplier key and a manufacturer key. The method further includes: transmitting a notice to downstream manufacturers, transmitting a notice to a regulatory agency to implement safety procedures and/or unit and transmitting a recall notice.

Abstract: A method is provided for preventing an IoT device within a trusted system from being harnessed in a malicious DDOS attack. The method may include bombarding the IoT device. The bombardment may originate from within the system, and may inundate the IoT device with harmless packets in a manner mimicking a traditional DOS attack. The inundating may utilize the resources of the IoT device to respond to the bombardment, and may thereby render the IoT device unavailable for fraudulent uses.

Abstract: A supply chain for a batch of identical units is provided. The supply chain includes database architecture. The batch of units may include a distributed ledger (blockchain) identification (ID), and at least two additional pieces of information. The information includes a serial number of the batch, a location of manufacture of the batch, an ID associated with the source of the batch, and/or the ID(s) of one or more components of each unit in the batch. The supply chain may include a private database supplier key. The supplier key enables publishing of batch transaction information onto a blockchain. The information may include a transfer of control of the batch from the supplier to a receiver-manufacturer. The supply chain may further include a private database receiver-manufacturer key associated with each batch associated with the receiver-manufacturer. The private database manufacturer key enables the receiver-manufacturer to review transactions associated with each batch.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods, systems, and apparatuses for blockchain-based unexpected data detection are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive a plurality of network function requests corresponding to the decentralized network. The node may analyze the plurality of network function requests to identify whether the requests included unexpected data and/or irregular data, and/or are associated with flagged wallets and/or smart contracts.

Abstract: A method and system looks for patterns in a series of gesture data samples to determine consistency or inconsistency within the data sample. One embodiment includes device authentication using a unique biometric algorithm that provides biometrically enhanced gesture-based authentication using a software only solution. In this embodiment, the system and method provides a mechanism to gather user gesture timing data, and to analyze and abstract the data into a non-repudiated template against which future gesture timings can be verified.

Abstract: A method and system looks for patterns in a series of data samples to determine consistency or inconsistency within the data sample. One embodiment includes computer authentication using a unique biometric algorithm that provides biometrically enhanced computer-based authentication using a software only solution. In this embodiment, the system and method provides a mechanism to gather user keystroke timing data, and to analyze and abstract the data into a non-repudiated template against which future keystroke timings can be verified.

Abstract: A method and system looks for patterns in a series of data samples to determine consistency or inconsistency within the data sample. One embodiment includes computer authentication using a unique biometric algorithm that provides biometrically enhanced computer-based authentication using a software only solution. In this embodiment, the system and method provides a mechanism to gather user keystroke timing data, and to analyze and abstract the data into a non-repudiated template against which future keystroke timings can be verified.