Abstract: Techniques and systems for detecting malicious activity within a network are provided herein. A method for detecting malicious activity within a network may include receiving, by a network-based authentication system, a network transaction. The network-based authentication system may identify a first attribute of the network transaction. The method may also include selecting, by the network-based authentication system, a first learning statistical model and a second learning statistical model from a plurality of models for handling the network transaction. Each of the first learning statistical model and the second learning statistical model may create a likelihood that the network transaction is authentic. The first learning statistical model may calculate a first score and the second learning statistical score may calculate a second score. Based on a comparison of the first score to a first threshold and the second score to a second threshold, the network transaction may be authenticated.

Abstract: Techniques and systems for determining a malicious derivative entity within a network are provided herein. A method for determining a malicious derivative entity may include receiving, by a network-based authentication system, a plurality of network transactions. A first attribute of a network transaction within the plurality of network transactions may be identified. The method may also include identifying a plurality of entities for the first attribute. The network-based authentication system may generate a first visual representation of a relationship between the first attribute and the plurality of derivative entities. Each of the derivative entities and the first attribute may be represented as nodes within the first visual representation. A first score for each of the nodes may be determined based on a degree of centrality of the nodes within the first visual representation. One network transaction may be blocked based on at least one node exceeding a first threshold.

Abstract: Techniques and systems for detecting malicious activity within a network are provided herein. A method for detecting malicious activity within a network may include receiving, by a network-based authentication system, a network transaction. The network-based authentication system may identify a first attribute of the network transaction. The method may also include selecting, by the network-based authentication system, a first learning statistical model and a second learning statistical model from a plurality of models for handling the network transaction. Each of the first learning statistical model and the second learning statistical model may create a likelihood that the network transaction is authentic. The first learning statistical model may calculate a first score and the second learning statistical score may calculate a second score. Based on a comparison of the first score to a first threshold and the second score to a second threshold, the network transaction may be authenticated.

Abstract: Techniques and systems for determining a malicious derivative entity within a network are provided herein. A method for determining a malicious derivative entity may include receiving, by a network-based authentication system, a plurality of network transactions. A first attribute of a network transaction within the plurality of network transactions may be identified. The method may also include identifying a plurality of entities for the first attribute. The network-based authentication system may generate a first visual representation of a relationship between the first attribute and the plurality of derivative entities. Each of the derivative entities and the first attribute may be represented as nodes within the first visual representation. A first score for each of the nodes may be determined based on a degree of centrality of the nodes within the first visual representation. One network transaction may be blocked based on at least one node exceeding a first threshold.

Abstract: Techniques and systems for determining a malicious derivative entity within a network are provided herein. A method for determining a malicious derivative entity may include receiving, by a network-based authentication system, a plurality of network transactions. A first attribute of a network transaction within the plurality of network transactions may be identified. The method may also include identifying a plurality of entities for the first attribute. The network-based authentication system may generate a first visual representation of a relationship between the first attribute and the plurality of derivative entities. Each of the derivative entities and the first attribute may be represented as nodes within the first visual representation. A first score for each of the nodes may be determined based on a degree of centrality of the nodes within the first visual representation. One network transaction may be blocked based on at least one node exceeding a first threshold.

Abstract: Techniques and systems for detecting malicious activity within a network are provided herein. A method for detecting malicious activity within a network may include receiving, by a network-based authentication system, a network transaction. The network-based authentication system may identify a first attribute of the network transaction. The method may also include selecting, by the network-based authentication system, a first learning statistical model and a second learning statistical model from a plurality of models for handling the network transaction. Each of the first learning statistical model and the second learning statistical model may create a likelihood that the network transaction is authentic. The first learning statistical model may calculate a first score and the second learning statistical score may calculate a second score. Based on a comparison of the first score to a first threshold and the second score to a second threshold, the network transaction may be authenticated.