SYSTEM AND METHOD FOR DETECTING BOTS BASED ON ITERATIVE CLUSTERING AND FEEDBACK-DRIVEN ADAPTIVE LEARNING TECHNIQUES

A system and method for detecting and blocking bots are presented. The method includes receiving unlabeled data regarding a visitor of a web source, grouping the received unlabeled data with similar characteristics into a group of data, detecting, based on the group of data, at least one anomaly, and determining, based on the at least one detected anomaly, several visitors to be blacklisted. 1. A method for detecting and blocking bots , comprising:receiving unlabeled data regarding a visitor of a web source;grouping the received unlabeled data with similar characteristics into a group of data;detecting, based on the group of data, at least one anomaly; anddetermining, based on the at least one detected anomaly, a number of visitors to be blacklisted.2. The method of claim 1 , wherein saving the detected at least one anomaly further comprises:any one of a conceptual drift, or a clustering of the group of data in a database, andwherein the concept drift is a change in a relationship between input and output over a predetermined period of time.3. The method of claim 1 , further comprising:providing feedback to an adaptive learning component to train a bot detection component.4. The method of claim 1 , further comprising:blocking each visitor added to the blacklist.5. The method of claim 1 , wherein determining the number of visitors to be blacklisted further comprises:identifying a cluster group and determining at least one tuning factor;determining a probability of blacklisted visitor to arrive again;determining a probability of the visitor to solve CAPTCHA;determining probability of the visitor solving captcha when blacklisted;determining a total number of false positive; anddetermining blacklist size of this size.6. The method of claim 5 , wherein the tuning factor is any one of:business limit per day (BLpD);number of iteration to occur per day (NpD);minimum blacklisting bound; ormaximum blacklisting bound.7. The method of claim 5 , wherein determining of the ...

System and method for detecting bots using semi-supervised deep learning techniques

A system of method of detecting bots are presented. The method includes receiving access patterns of a visitor accessing a protected web property, encoding each of the access patterns into a fixed length feature vector, determining an offline-trained model based on past data, generating an anomaly score based on the fixed length feature vector and an offline-trained model, and determining the visitor to be a bot, when the generated anomaly score associated with the visitor reaches a predetermined threshold.

BLOCKCHAIN SELECTIVE WORLD STATE DATABASE

A processor may receive a smart contract. The processor may generate an entity policy. The entity policy may provide one or more world-state rules associated with a particular entity. The processor may update, responsive to receiving the smart contract, a world-state database of the particular entity. The one or more world-state rules may affect the world-state database of the particular entity differently than the world-state database of a second entity. 1. A method for selectively updating one or more world-state databases in a blockchain network , comprising:receiving, by a processor, a request for a smart contract;identifying an entity policy, wherein the entity policy provides one or more world-state rules associated with a particular entity; andupdating, responsive to receiving the smart contract, a world-state database of the particular entity, wherein the one or more world-state rules affect the world-state database of the particular entity differently than the world-state database of a second entity.2. The method of claim 1 , wherein updating claim 1 , responsive to receiving the smart contract claim 1 , the world-state database of the particular entity further includes:determining, using the entity policy, if the smart contract is associated with the particular entity;identifying the one or more world-state rules associated with the particular entity; andapplying the one or more world-state rules and the smart contract to the world-state database of the particular entity.3. The method of claim 1 , wherein updating claim 1 , responsive to receiving the smart contract claim 1 , the world-state database of the particular entity includes:determining a relevant information from the smart contract based on the one or more world-state rules; andupdating the world-state database of the particular entity to include the relevant information.4. The method of claim 3 , further comprising:selecting the relevant information, wherein the relevant information is dictated ...

SYSTEM AND METHOD FOR DETECTING BOTS BASED ON ANOMALY DETECTION OF JAVASCRIPT OR MOBILE APP PROFILE INFORMATION

A system and method for detecting bots. The method includes receiving a request to access a server, the request is being received from a client device, and responsive to the request, causing the client device to download a script code file to the client device. The script code file, when executed, collects a profile, and the profile includes a plurality of parameters. The method also includes receiving the created profile, generating a score based on the plurality of parameters to identify a bot, and initiating a mitigation action based on the identified bot. 1. A method for detecting bots , comprising:receiving a request to access a server, the request being received from a client device;responsive to the request, causing the client device to download a script code file to the client device, whereinthe script code file, when executed, collects a profile, receiving the created profile;', 'generating a score based on the plurality of parameters to identify a bot; and', 'initiating a mitigation action based on the identified bot., 'the profile including a plurality of parameters'}2. The method of claim 1 , wherein generating a score based on the plurality of parameters to identify a bot further comprises: detecting an anomaly within the plurality of parameters.3. The method of claim 2 , further comprising:developing a first trained model by collecting past data on the client device;detecting anomaly based on the collected past data; anddeveloping the first trained model based on the detected anomaly.4. The method of claim 3 , wherein generating the score further comprises:collecting the profile parameters of a user;extracting a plurality of features from the profile parameters;analyzing the extracted plurality of features in a second trained model; andgenerating an anomaly score based on the analysis of the extracted plurality of features, wherein the anomaly score is the generated score.5. The method of claim 1 , wherein the plurality of parameters describes one of: ...

DATA ARCHIVE

An example operation includes one or more of identifying, by an archiving server node, a unique archival policy for each of a plurality of blockchain nodes, executing, by the archiving server node, a consensus mechanism to determine at least one block from a plurality of blocks of the plurality of the blockchain nodes to be archived, and running the unique archival policy to archive the at least one block from the plurality of the blocks.

System and method for detecting bots based on iterative clustering and feedback-driven adaptive learning techniques

A system and method for detecting and blocking bots are presented. The method includes receiving unlabeled data regarding a visitor of a web source, grouping the received unlabeled data with similar characteristics into a group of data, detecting, based on the group of data, at least one anomaly, and determining, based on the at least one detected anomaly, several visitors to be blacklisted.

Data archive

An example operation includes one or more of identifying, by an archiving server node, a unique archival policy for each of a plurality of blockchain nodes, executing, by the archiving server node, a consensus mechanism to determine at least one block from a plurality of blocks of the plurality of the blockchain nodes to be archived, and running the unique archival policy to archive the at least one block from the plurality of the blocks.

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Blockchain selective world state database

A processor may receive a smart contract. The processor may generate an entity policy. The entity policy may provide one or more world-state rules associated with a particular entity. The processor may update, responsive to receiving the smart contract, a world-state database of the particular entity. The one or more world-state rules may affect the world-state database of the particular entity differently than the world-state database of a second entity.

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