Systems and methods for location-based automated authentication are disclosed. A system comprises a mobile device, a sensor and a backend platform. The sensor and the backend platform are in network communication. The mobile device is operable to continuously transmit Bluetooth Low Energy (BLE) signals comprising encrypted transitory identifiers. The sensor is operable to receive a BLE signal from the mobile device when the mobile device is within a predetermined range, and communicate over a network connection the encrypted transitory identifier comprised in the BLE signal to the backend platform. The backend platform is operable to extract a unique identifier and a changing encrypted identifier from the received encrypted transitory identifier, generate a changing encrypted identifier, and validate a user identification by comparing the generated changing encrypted identifier and the extracted changing encrypted identifier.

An example operation may include one or more of connecting, by a scheduler node, to a blockchain network comprised of member nodes, receiving, by the scheduler node, a plurality of transactions that include deadlines from the member nodes, comparing, by the scheduler node, the deadlines of the plurality of the transactions against an average time to append to a ledger (ATAL) pre-calculated for the scheduler node, dropping, by the scheduler node, the transactions of the plurality of the transactions, if a sum of the ATAL and a current time is larger than the deadlines of the plurality of the transactions, calculating, by the scheduler node, a priority usage balance (PUB) for the member nodes based on the transactions of the plurality of transactions remaining after the transactions of the plurality of the transactions have been dropped, scheduling, by the scheduler node, a transaction with an earliest deadline from the plurality of the remaining transactions to be validated first for an execution, and arranging, by the scheduler node, a validation order for the plurality of the remaining transactions based on the PUBs.

A system for distributed key storage, comprising a requesting device communicatively connected to a plurality of distributed storage nodes, the requesting device designed and configured to receive at least a confidential datum, select at least a distributed storage node of a plurality of distributed storage nodes, whereby selecting further comprises receiving a storage node authorization token from the at least a distributed storage node, querying an instance of a distributed authentication listing containing authentication information using at least a datum of the storage node authorization token, retrieving an authentication determination from the instance of the authentication listing, and selecting the at least a distributed storage node as a function of the authentication determination, generate at least a retrieval authentication datum, and transmit the at least a confidential datum and the at least a retrieval verification datum to the at least a distributed storage node.

Session-specific information stored to a cookie or other secure token can be selected and/or caused to vary over time, such that older copies will become less useful over time. Such an approach reduces the ability of entities obtaining a copy of the cookie from performing unauthorized tasks on a session. A cookie received with a request can contain a timestamp and an operation count for a session that may need to fall within an acceptable range of the current values in order for the request to be processed. A cookie returned with a response can be set to the correct value or incremented from the previous value based on various factors. The allowable bands can decrease with age of the session, and various parameter values such as a badness factor for a session can be updated continually based on the events for the session.

A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

An authorizing party determines an authorization record set that needs to be revoked, where an authorization record included in the authorization record set corresponds to a token that is issued to an authorized party after the authorizing party grants access to the authorized party, and where each authorization record includes an authorization validation moment for a corresponding token. A time validity attribute of the authorization record set is configured. For a specific point-in-time, a value associated with the time validity attribute is set. A determination is performed as to whether the authorization record is revoked based on the authorization validation moment and the value associated with the time validity attribute.

A node system implements a method for node relay communication. A description of a flow entry including an address in a flow and a private key is received. The flow entry and the private key are stored in a database indexed to a flow ID. A packet comprising an authentication code and packet data including packet sequence information and a Flow ID is received. A look up in the database of a flow entry corresponding to the Flow ID of the packet is performed. The packet is either ignored or forwarded to the address in the flow, depending on the result of the look-up.

For a plurality of hosts, observe first time-varying characteristics including network throughput, central processing unit (CPU) usage, and/or memory usage; second time-varying characteristics including software configuration; and time-invariant characteristics including hardware configuration, at a plurality of timestamps. Construct a restricted HMM configured to predict actual host states, wherein the first time-varying characteristics include observed variables. The current observed variables depend on current values of the hidden variables and prior timestamp distribution of the observed variables. The former in turn depend on prior timestamp values of the hidden variables, the time-invariant characteristics of the hosts. and current timestamp values of the second time-varying characteristics. Estimate parameters of the restricted HMM; run the restricted HMM with the estimated parameters for each of the hosts; analyze the results to identify at least one of the hosts which has a potential cybersecurity issue; and take at least one remedial action.

A system and method for creating and providing crime intelligence based on crowdsourced information stored on a blockchain, where the crowdsourced information is analyzed and evaluated preferably according to an artificial intelligence (AI) model and users are rewarded for providing timely, valuable, and accurate crime tips. The crowdsourced information may be obtained in any suitable manner, including but not limited to written text, such as a document, or audio information.

Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.