Systems and methods for blockchain security data intelligence

Abstract

Embodiments are directed to systems and methods configured to detect malware vulnerabilities in computational environments. The method includes receiving input data distributed among a plurality of nodes within a particular network, receiving the input data into a curator engine to convert the at least one input data into a payload having at least one message header, receiving the payload having the at least one message header into a transaction engine, and storing the payload having the at least one message header into a blockchain database. The method continues by processing the payload having the at least one message header to make a decision to transform or analyze the transaction based on a predetermined set of rules, converting the payload into a data file, evaluating the data file, aggregating the data file for a plurality of behavior models, and converting the data file into a valid message payload with a header.

What is claimed is: 
     
         1 . A method of detecting malware vulnerabilities in a computational environment, the method comprising the steps of:
 receiving, from a client environment, at least one input data distributed among a plurality of nodes within a particular network;   receiving, from at least one of the plurality of nodes, the at least one input data into a curator engine to convert the at least one input data into a payload having at least one message header;   receiving, from the curator engine, the payload having the at least one message header into a transaction engine;   storing, from the transaction engine, the payload having the at least one message header into a blockchain database;   receiving and processing, by a rule engine, the payload having the at least one message header to make a decision to transform or analyze the transaction based on a predetermined set of rules;   converting, by the curator engine, the payload having the at least one message header into a data file,   evaluating, by an external malware analysis engine, the data file;   aggregating, by a data aggregator, the data file for a plurality of behavior models;   converting, by the curator engine, the data file into a valid message payload having a header;   wherein the header of the valid message payload is determined whether to be malware, by the rule engine, assigning a second transaction identification and storing the determination prior to the rule engine rejecting the header of the valid message payload or permitting the header of the valid message payload as a validated message payload into the client environment.   
     
     
         2 . The method of  claim 1 , wherein the step of evaluating by the external malware analysis engine includes analyzing the data file for a behavior, mapping the behavior, and outputting a signal of the external malware analysis engine indicating whether the data file is malicious. 
     
     
         3 . The method of  claim 2 , further comprising the steps of:
 validating, by the rule engine, the mapped behavior against a plurality of past behavior models and a knowledge database; and   determining, by the rule engine, an existence of malware, wherein if the existence of malware is present, the data file is rejected and a client user interface is notified via an action engine,   wherein if the existence of malware is not present, the data file is recorded in the blockchain database and transferred to a swarm application program interface.   
     
     
         4 . The method of  claim 3 , wherein the rule engine input is a swarm intelligence having a plurality of input data. 
     
     
         5 . The method of  claim 4 , wherein the swarm intelligence is configured to utilize particle swarm optimization techniques. 
     
     
         6 . The method of  claim 4 , wherein the swarm intelligence comprises a plurality of nodes, each node having at least one blockchain database and at least one smart contract. 
     
     
         7 . The method of  claim 6 , wherein the plurality of nodes are in communication with each other so to securely share information. 
     
     
         8 . The method of  claim 3 , wherein the plurality of past behavior models and the knowledge database include patterns in an operating system such as an audit log, an event tracing, or a kernel driver that are not directly related to a malware sample or the plurality of past behavior models and the knowledge database include a plurality of anomalies in a human user behavior data not related to devices that are inferred using a statistical analysis leading to determining a potential insider threat and a theft of internal data. 
     
     
         9 . A method of detecting vulnerabilities and anomalies in a computational environment, the method comprising the steps of:
 registering, by a unique identifier, an internet of things device to a transaction engine such that a unique signature is provided;   determining, by the transaction engine, a plurality of data to be collected from the internet of things device;   recording, by a blockchain database, the unique signature;   validating, by a rule engine, the unique signature so to make a decision regarding a plurality of firmware and a plurality of machine code associated with the internet of things device based on a predetermined set of rules;   processing, by an action engine, the transaction from the rule engine; and   alerting, by the action engine, feedback to a client environment,   wherein the plurality of firmware and machine code in the internet of things device is analyzed so to maintain enabled capabilities, shielding, and enforcing continuous working order.   
     
     
         10 . The method of  claim 9 , wherein a rule engine input is a plurality of swarm intelligence data such that the plurality of swarm intelligence data comprises a plurality of nodes, each node having at least one blockchain database and at least one smart contract, the plurality of nodes are in communication with each other so to securely share information, the plurality of swarm intelligence data is configured to utilize particle swarm optimization techniques. 
     
     
         11 . A system for enforcing privacy policy regulations in a computational environment, the system comprising:
 a client user interface;   a client environment having a plurality of databases, a plurality of client applications, a curator engine, and an action engine, the plurality of client applications comprising a new user register and a new user event, the plurality of client applications is communicatively coupled to the plurality of databases, the curator engine is communicatively coupled to both the client environment and the plurality of databases;   a swarm intelligence having a transaction engine, a blockchain database, and a rule engine, the swarm intelligence is communicatively coupled to the client environment, the transaction engine is communicatively coupled to the curator engine and to the blockchain database, the blockchain database is communicatively coupled to the rule engine, the rule engine is communicatively coupled to the action engine, the action engine is communicatively coupled to the client user interface,   wherein the system is configured to execute and enforce a plurality of data traceability from an integrated system across a browser, a session, a webserver, or the plurality of databases configured with a personal identifiable information by indexing the personal identifiable information from a genesis block such that a user is permitted to log, delete or encrypt the personal identifiable information according to policy laws.