Patent Publication Number: US-2022237271-A1

Title: Authentication based on physical interaction and characteristic noise patterns

Description:
TECHNICAL FIELD 
     An embodiment of the present subject matter relates generally to authentication and, more specifically, to authentication based on physical interaction and characteristic noise patterns. 
     BACKGROUND 
     Current technology allows users to perform a wide variety of tasks by providing authentication for security. For example, online services (e.g., banking services, online retailer, etc.) allow users to access their bank accounts, transfer funds, access personal information, purchase items, etc., by simply providing a set of user credentials (e.g., username and password). While these types of online services provide convenience, they also create security concerns. For example, a bad actor with knowledge of the user credentials of another user (e.g., through phishing or social engineering) can access the other user&#39;s user bank account, transfer funds, etc. Accordingly, providing secure authentication is a growing concern. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. Some embodiments are illustrated by way of example, and not limitation, in the figures of the accompanying drawings in which: 
         FIG. 1  is a block diagram of a system for authentication based on physical interaction and characteristic noise patterns, in accordance with some example embodiments. 
         FIG. 2  is a communication diagram showing a service provider computing system operating as an intermediary between a client device and an authentication system  108  to provide authentication based on physical interaction and characteristic noise patterns, according to some example embodiments. 
         FIG. 3  is a communication diagram showing an authentication system  108  communicating directly with a client device to provide authentication based on physical interaction and characteristic noise patterns, according to some example embodiments. 
         FIG. 4  is a block diagram of an authentication system, according to some example embodiments. 
         FIG. 5  is a communication diagram showing an authentication system providing authentication based on physical interaction and characteristic noise patterns, according to some example embodiments. 
         FIG. 6  is a block diagram of an authentication analysis component, according to some example embodiments. 
         FIG. 7  is a flowchart showing a method for authentication based on physical interaction and characteristic noise patterns, according to certain example embodiments. 
         FIG. 8  is a flowchart showing a method for determining an authentication score, according to certain example embodiments. 
         FIGS. 9A-9D  show a user interface for providing an authentication requirement based on a physical interaction, according to some example embodiments 
         FIG. 10  is a block diagram illustrating a representative software architecture, which may be used in conjunction with various hardware architectures herein described. 
         FIG. 11  is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for purposes of explanation, various details are set forth in order to provide a thorough understanding of some example embodiments. It will be apparent, however, to one skilled in the art, that the present subject matter may be practiced without these specific details, or with slight alterations. 
     Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present subject matter. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” appearing in various places throughout the specification are not necessarily all referring to the same embodiment. 
     For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the present subject matter. However, it will be apparent to one of ordinary skill in the art that embodiments of the subject matter described may be practiced without the specific details presented herein, or in various combinations, as described herein. Furthermore, well-known features may be omitted or simplified in order not to obscure the described embodiments. Various examples may be given throughout this description. These are merely descriptions of specific embodiments. The scope or meaning of the claims is not limited to the examples given. 
     Disclosed are systems, methods, and non-transitory computer-readable media for authentication based on physical interaction and characteristic noise patterns. An authentication requirement (e.g., entering a username and password) can be implemented to authenticate a requested transaction. For example, a user requesting to perform a transaction, such as logging into an account, transferring funds, etc., may be prompted to satisfy an authentication requirement, such as entering a username and password, providing a code or personal identification number, answering a secret question, and the like. Approval and performance of the requested transaction may be contingent on the authentication requirement being properly satisfied. Accordingly, the requested transaction may be denied if the authentication requirement is not satisfied. 
     Common examples of an authentication requirement include providing a specified piece of data, such as a username/password, code, personal identification number (PIN), answer, and the like. One issue with these types of authentication requirements is that they can be satisfied by another user that has knowledge of the specified data. For example, a bad actor with knowledge of a username and password may simply use the username and password to perform a transaction, such as accessing email account, transferring funds, and the like. The effectiveness of the authentication requirement is therefore contingent on maintaining the privacy of the specified data. 
     To alleviate this issue, an authentication requirement may be based on a physical interaction. A physical interaction may be any type of physical movement or action, such as performing a swipe, moving a device in a specified motion, tapping on the device, and the like. In contrast to providing a specified piece of data, performance of a physical interaction may be unique to each user. That is, each person may perform a specified physical interaction in a manner that is unique to that person. For example, different people may perform a physical interaction such as swiping a finger across a screen in a unique manner by starting at different positions on the screen, using a different hand (e.g., right hand, left hand) to perform the swipe, following different trajectories, swiping at different speeds, ending at different positions, and the like. Different users may also hold a client device in different orientations when performing the swipe, further adding to the uniqueness of the physical interaction. Due to these unique qualities, a bad actor that is aware of the physical interaction to be performed (e.g., swiping from right to left) may still be unable to replicate the physical interaction sufficiently to satisfy the authentication requirement. 
     To implement an authentication requirement based on a physical interaction, a user is prompted to perform the physical interaction one or more times during an initial registration phase. Sensors are used to capture sensor data describing the physical interactions performed by the user during the initial registration phase. This sensor data can be stored and subsequently used as a reference during authentication requests to determine whether an authentication request has been satisfied. For example, the stored sensor data may be used as a reference of the known performance of the physical interaction by the authorized user. 
     After completion of the initial registration phase, a user requesting to perform a transaction may be prompted to perform the physical interaction as an authentication requirement. Sensors are again used to capture sensor data describing the physical interaction performed by the requesting user, which is then used along with the stored sensor data captured during the registration phase to determine whether the user requesting to perform the transaction is the authenticated user. For example, the sensor data captured during the authentication requirement may be analyzed to determine a set of user characteristics describing the specific manner in which the user performed the physical interaction. For example, the user characteristics may describe the speed, direction, starting/ending point, and other features of the physical interaction. The user characteristics of the physical interaction may then be compared to user characteristics of the known performance of the physical interaction by the authorized to determine whether the physical interaction was performed by the authorized user (e.g., whether the users characteristics match or are sufficiently similar). 
     In addition to the unique user characteristics of the authorized user&#39;s performance of the physical interaction, unique characteristics of the client device may also be used to authenticate a requesting user. For example, each client device may include physical variances and deviations caused during the manufacturing process. These deviations may be the result of varying solder points, minor defects in sensors, and the like. These manufacturing deviations result in a unique characteristic noise pattern being included in the sensor data captured by the client device. 
     During an authentication request, the characteristic noise pattern included in the sensor data can be used to determine whether the client device being used to perform the physical interaction is an expected client device, such as a client device of the authenticated user. For example, a characteristic noise pattern identified from the sensor data captured as part of the authentication requirement can be compared to a characteristic noise pattern identified from sensor data describing the known performance of the physical interaction by the authorized user. A determination that the characteristic noise patterns match or are sufficiently similar indicates that the client device used to perform the physical interaction is the client device of the authorized user. 
     In some embodiments, the user characteristics of a physical interaction and the noise characteristic patterns may be evaluated separately when determining whether an authentication requirement has been satisfied. For example, satisfaction of the authentication requirement may be based on each of the user characteristics and the noise characteristic patterns being separately satisfied. Alternatively, the user characteristics and the noise characteristic patterns may be evaluated in combination. For example, satisfaction of the authentication requirement may be based on a cumulative probability score determined based on the user characteristics and the noise characteristic pattern. 
       FIG. 1  is a block diagram of a system  100  for authentication based on physical interaction and characteristic noise patterns, in accordance with some example embodiments. As shown, multiple devices (i.e., client device  102 , client device  104 , service provider computing system  106 , and an authentication system  108 ) are connected to a communication network  110  and configured to communicate with each other through use of the communication network  110 . The communication network  110  is any type of network, including a local area network (LAN), such as an intranet, a wide area network (WAN), such as the internet, a telephone and mobile device network, such as cellular network, or any combination thereof. Further, the communication network  110  may be a public network, a private network, or a combination thereof. The communication network  110  is implemented using any number of communication links associated with one or more service providers, including one or more wired communication links, one or more wireless communication links, or any combination thereof. Additionally, the communication network  110  is configured to support the transmission of data formatted using any number of protocols. 
     Multiple computing devices can be connected to the communication network  110 . A computing device is any type of general computing device capable of network communication with other computing devices. For example, a computing device can be a personal computing device such as a desktop or workstation, a business server, or a portable computing device, such as a laptop, smart phone, or a tablet personal computer (PC). A computing device can include some or all of the features, components, and peripherals of the machine  1100  shown in  FIG. 11 . 
     To facilitate communication with other computing devices, a computing device includes a communication interface configured to receive a communication, such as a request, data, and the like, from another computing device in network communication with the computing device and pass the communication along to an appropriate module running on the computing device. The communication interface also sends a communication to another computing device in network communication with the computing device. 
     In the system  100 , users may interact with a service provider computing system  106  to utilize services provided by a service provide. Users communicate with and utilize the functionality of the service provider computing system  106  by using the client devices  102  and  104  that are connected to the communication network  110  by direct and/or indirect communication. A service provider may provide any type of service, whether it be online or offline, and the service provider computing system  106  may facilitate any related service that is provided online, such as a banking service, online retailer, and the like. 
     Although the shown system  100  includes only two client devices  102 ,  104  and one service provider computing system  106 , this is only for ease of explanation and is not meant to be limiting. One skilled in the art would appreciate that the system  100  can include any number of client devices  102 ,  104  and/or service provider computing system  106 . Further, each service provider computing system  106  may concurrently accept communications from and initiate communication messages and/or interact with any number of client devices  102 ,  104 , and support connections from a variety of different types of client devices  102 ,  104 , such as desktop computers; mobile computers; mobile communications devices, e.g., mobile phones, smart phones, tablets; smart televisions; set-top boxes; and/or any other network enabled computing devices. Hence, the client devices  102  and  104  may be of varying type, capabilities, operating systems, and so forth. 
     A user interacts with a service provider computing system  106  via a client-side application installed on the client devices  102  and  104 . In some embodiments, the client-side application includes a component specific to the service provider computing system  106 . For example, the component may be a stand-alone application, one or more application plug-ins, and/or a browser extension. However, the users may also interact with the service provider computing system  106  via a third-party application, such as a web browser or messaging application, that resides on the client devices  102  and  104  and is configured to communicate with the service provider computing system  106 . In either case, the client-side application presents a user interface (UI) for the user to interact with the service provider computing system  106 . For example, the user interacts with the service provider computing system  106  via a client-side application integrated with the file system or via a webpage displayed using a web browser application. 
     A service provider computing system  106  is one or more computing devices associated with a service provider to provide functionality of the service provider. A service provider may be any type of business or entity that provides a service for customers. The service may be any type of online service and/or offline service, such as a banking service, travel service, retail service, and the like. The service provider computing system  106  may facilitate any online portion of the services provided by a service provider but does not have to provide an online service that is accessible to users. For example, the service provider computing system  106  may simply be a computing system used by a service provider to perform any type of functionality. 
     A service provider may enable its users/customers to perform various transactions as part of the services provided by the service provider. A transaction may be any of a variety of types of transaction, such as logging into an account, purchasing items, transferring money, accessing account data, and the like. A service provider may utilize the functionality of the authentication system  108  to authenticate requested transaction. For example, the authentication system  108  may provide authentication based on physical interaction and characteristic noise patterns. Although the authentication system  108  and the service provider computing system  106  are shown as separate entities, this is only one embodiment and is not meant to be limiting. In other embodiments, the functionality of the authentication system  108  may be partially or completely integrated within the service provider computing system  106 . 
     The authentication system  108  communicates with the service provider computing system  106  and/or client device  102 ,  104  to provide for authentication based on physical interaction and characteristic noise patterns. For example, in some embodiments, the service provider computing system  106  operates as an intermediary between the client devices  102 ,  104  and the authentication system  108 . In this type of embodiment, the service provider computing system  106  may receive sensor data from a client device  102 ,  104  describing a physical interaction performed by a user of the client device  102 ,  104 , and pass the sensor data to the authentication system  108 . The authentication system  108  uses the received sensor data to determine whether an authentication requirement has been satisfied and provides a response to the service provider computing system  106 . 
     Alternatively, the authentication system  108  may communicate directly with a client device  102 ,  104  to collect sensor data. For example, the authentication system  108  may then use the received sensor data to determine whether an authentication requirement has been satisfied and provide a response to the service provider computing system  106 . 
       FIG. 2  is a communication diagram showing a service provider computing system  106  operating as an intermediary between a client device  102  and an authentication system  108  to provide authentication based on physical interaction and characteristic noise patterns, according to some example embodiments. As shown, the client device  102  transmits a transaction request  202  to the service provider computing system  106 . The transaction request  202  may be a request to perform any type of transaction, such as logging into an account, transferring funds, and the like. 
     In response to receiving the transaction request  202 , the service provider computing system  106  communicates  204  with the client device  102  to initiate an authentication process. This may include prompting the requesting user to provide specified data, such as a username and password. The authentication process may also include prompting the requesting user to perform a physical interaction, such as performing a swipe across a screen of the client device  102 , shaking the client device  102 , and the like. Sensors included in the client device  102  may gather sensor data describing performance of the physical interaction, which is provided to the service provider computing system  106 . 
     In turn, the service provider computing system  106  transmits an authentication request  206  to the authentication system  108 . The authentication request  206  may include the sensor data received from the client device  102 . The authentication request  206  may also include data identifying the requesting user, such as an account identifier associated with the requesting user and/or data identifying the client device  102 . 
     The authentication system  108  uses the received sensor data to perform an authentication analysis  208  to determine whether the authentication requirement has been satisfied. For example, the authentication system  108  uses the sensor data to determine whether user characteristics describing features of the physical interaction match or are sufficiently similar to user characteristics describing features of the known performance of the physical interaction by an authorized user. The authentication system  108  may also identify a characteristic noise pattern included in the sensor data to determine whether the authentication requirement has been satisfied. For example, the characteristic noise pattern can indicate whether the client device  102  being used to perform the physical interaction is an expected client device  102  such as a client device  102  known to be associated with the authenticated user. 
     After performing the authentication analysis  208 , the authentication system  108  provides a response  210  to the service provider computing system  106 . The response  210  indicates the outcome of the authentication analysis  208 , such as indicating whether the authentication requirement has or has not been satisfied. In turn, the service provider computing system  106  may approve or deny the requested transaction based on the response  210 . For example, the service provider computing system  106  may approve the requested transaction if the authorization requirement has been satisfied or deny the requested transaction if the authorization requirement has not been satisfied. In the event that the requested transaction is approved, the service provider computing system  106  may execute  212  the requested transaction. For example, the service provider computing system  106  may execute a login to an account, execute a transfer of funds, and the like. The service provider computing system  106  may communicate  214  with the client device  102  to provide the client device  102  with any subsequent data resulting from execution of the requested transaction. For example, the service provider computing system  106  may provide the client device  102  with a notification that the requested transaction has been completed, any requested data (e.g., financial data), and the like. 
       FIG. 3  is a communication diagram showing an authentication system  108  communicating directly with a client device  102  to provide authentication based on physical interaction and characteristic noise patterns, according to some example embodiments. As shown, the client device  102  transmits a transaction request  302  to the service provider computing system  106 . The transaction request  302  may be a request to perform any type of transaction, such as logging into an account, transferring funds, and the like. 
     In turn, the service provider computing system  106  transmits an authentication request  304  to the authentication system  108 . The authentication request may include data identifying the requesting user, such as an account identifier associated with the requesting user and/or data identifying the client device  102 , such as phone number. 
     The authentication system  108  communicates  306  with the client device  102  to initiate an authentication process. For example, the authentication system  108  may transmit a message (e.g., short message service (SMS) message) to the client device  102  that prompts the requesting user to perform a physical interaction, such as performing a swipe across a screen of the client device  102 , shaking the client device  102 , and the like. Sensors included in the client device  102  may gather sensor data describing performance of the physical interaction, which is then provided to the authentication system  108  as part of the communications  306 . 
     The authentication system  108  uses the received sensor data to perform an authentication analysis  308  to determine whether the authentication requirement has been satisfied. For example, the authentication system  108  uses the sensor data to determine whether user characteristics describing features of the physical interaction match or are sufficiently similar to user characteristics describing features of the known performance of the physical interaction by an authorized user. The authentication system  108  may also identify a characteristic noise pattern included in the sensor data to determine whether the authentication requirement has been satisfied. For example, the characteristic noise pattern can indicate whether the client device  102  being used to perform the physical interaction is an expected client device  102  such as a client device  102  known to be associated with the authenticated user. 
     After performing the authentication analysis  308 , the authentication system  108  provides a response  310  to the service provider computing system  106 . The response  310  indicates the outcome of the authentication analysis  308 , such as indicating whether the authentication requirement has or has not been satisfied. In turn, the service provider computing system  106  may approve or deny the requested transaction based on the response  310 . For example, the service provider computing system  106  may approve the requested transaction if the authorization requirement has been satisfied or deny the requested transaction if the authorization requirement has not been satisfied. In the event that the requested transaction is approved, the service provider computing system  106  may execute  312  the requested transaction. For example, the service provider computing system  106  may execute a login to an account, execute a transfer of funds, and the like. The service provider computing system  106  may communicate  314  with the client device  102  to provide the client device  102  with any subsequent data resulting from execution of the requested transaction. For example, the service provider computing system  106  may provide the client device  102  with a notification that the requested transaction has been completed, any requested data (e.g., financial data), and the like. 
       FIG. 4  is a block diagram of an authentication system  108 , according to some example embodiments. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components (e.g., modules) that are not germane to conveying an understanding of the inventive subject matter have been omitted from  FIG. 4 . However, a skilled artisan will readily recognize that various additional functional components may be supported by the authentication system  108  to facilitate additional functionality that is not specifically described herein. Furthermore, the various functional modules depicted in  FIG. 4  may reside on a single computing device or may be distributed across several computing devices in various arrangements such as those used in cloud-based architectures. 
     As shown, the authentication system  108  includes a registration component  402 , an authentication request management component  404 , an authentication analysis component  406 , an output component  408 , a feedback component  410 , and a data storage  412 . 
     The registration component  402  facilitates an initial registration phase for implementing an authentication requirement based on a physical interaction. As explained previously, user characteristics describing the unique qualities of a user&#39;s performance of a physical interaction as well as a characteristic noise pattern associated with a user&#39;s client device  102  can be used to authenticate that a user requesting to perform a transaction is an authorized user. To implement an authentication requirement based on a physical interaction, the registration component  402  facilitates an initial registration phase during with a user is prompted to perform the physical interaction one or more times. The physical interaction may be any type of physical action or movement that can be captured by sensors. For example, the physical interaction may be performing a movement across a touchscreen of a client device  102 , such as a swipe or other motion, shaking a client device  102 , moving arms in a pattern, and the like. 
     In some embodiments, the physical interaction may be a predetermined physical interaction that the user is prompted to perform, such as performing a specified swipe or motion. Alternatively, in some embodiments, the physical interaction may be selected by the registering user. For example, the user may be prompted to perform a physical interaction of the user&#39;s choice, such as by performing any desired motion across a touch screen of the client device  102 , moving the client device  102  in a specified pattern, entering a personal identification number (PIN), and the like. 
     Sensors included in the client device  102  are used to capture sensor data describing the physical interactions performed by the user during the registration phase. The client device  102  may include any of a variety of types of sensors that may be used to capture sensor data describing the physical interaction. For example, the sensors may include optical sensors (e.g., cameras), audio sensors (e.g., microphones, motion sensors, touch sensors (e.g., touchscreens), accelerometers, and the like. 
     The client device  102  provides the sensor data captured during the registration phase to the authentication system  108 . The registration component  402  stores the sensor data in the data storage  412 , where it may be associated with the registering user and/or client device  102 . For example, the sensor data may be associated with a unique user identifier associated with the user and/or device identifier associated with the client device  102 , such as a phone number. The stored sensor data may be used subsequently during authentication requests to determine whether a user requesting to perform a transaction is the authenticated user (e.g., the user that performed the physical interactions during the registration phase). 
     In some embodiments, the registration component  402  may perform some additional processing of the sensor data. For example, the registration component  402  may generate user characteristics describing the physical interaction. The user characteristics may be a feature vector representing the physical interaction performed by the user based on the sensor data. The feature vector may include individual values determined based on specified features describing the physical interaction. For example, the feature vector may include values indicating a starting and/or ending point of motion, a speed of a motion, a direction of a motion, a trajectory of a motion, and the like. In some embodiments, the registration component  402  may use the user characteristics to generate a machine learning model. For example, the registration component  402  may use the user characteristics generated from the sensor data as training data used to train a machine learning algorithm. The resulting machine learning model may provide an output probability score indicating a likelihood that a physical interaction was performed by the authorized user. For example, sensor data describing the physical interaction and/or user characteristics (e.g., feature vector) generated from the sensor data may be used as input into the trained machine learning model, which in turn provides an output probability score. 
     In some embodiments, the registration component  402  may identify a characteristic noise pattern included in the sensor data captured during the registration phase. Each client device  102  may include physical variances and deviations caused during the manufacturing process. These deviations may be the result of varying solder points, minor defects in sensors, and the like. These manufacturing deviations result in a unique characteristic noise pattern being included in the sensor data captured by the client device  102 . The registration component  402  may analyze the sensor data to identify the characteristic noise pattern associated with the client device  102  used during the registration phase, which may be used subsequently during authentication requests to determine whether a user requesting to perform a transaction is the authenticated user (e.g., the user that performed the physical interactions during the registration phase). 
     Similar to the sensor data, the registration component  402  may generate feature vectors and/or train a machine learning model based on the characteristic noise pattern. For example, the feature vector may include individual values determined based on specified features describing the characteristic noise pattern. Further, the registration component  402  may use the characteristic noise pattern and/or feature vectors generated from the characteristic noise pattern as training data used to train a machine learning algorithm. The resulting machine learning model may provide an output probability score indicating a likelihood that a physical interaction was performed by the client device  102  used during the registration phase. For example, sensor data describing a characteristic noise pattern identified from sensor data captured as part of an authentication request and/or a feature vector generated from the characteristic noise pattern may be used as input into the trained machine learning model, which in turn provides an output probability score. 
     The authentication request management component  404  receives and processes authentication requests. An authentication request is a request to authenticate whether a user requesting to perform a transaction is authorized to perform the transaction. For example, the requesting user may be authenticated based on whether an authentication requirement including a physical interaction has been satisfied. 
     The authentication request management component  404  may receive an authentication request from the service provider computing system  106 . The authentication request may include data identifying an account, authorized user and or client device  102 . For example, the authentication request may include a unique identifier associated with a user and/or account. 
     In some embodiments, the authentication request may include sensor data describing a physical interaction performed by a requesting user to satisfy an authentication requirement. For example, the service provider computing system  106  may facilitate communications with a client device  102  of the requesting user to prompt the user to perform the physical interaction and receive sensor data describing the physical interaction. In this type of embodiments, the authentication request management component  404  may provide the sensor data and other data included in the authentication request to the authentication analysis component  406 . 
     Alternatively, in some embodiments, the authentication request management component  404  may facilitate performance of the physical interaction by the requesting user and collection of the sensor data. For example, the authentication request management component  404  may transmit a message (e.g. SMS message) to the client device  102  of the requesting user that prompts the requesting user to perform the physical interaction. The message may include a user interface element, such as a button, that when actuated causes the client device  102  to enable the user to perform the physical interaction and/or collect sensor data to capture the physical interaction. The client device  102  returns the sensor data to the authentication system  108 , which is received by the authentication request management component  404 . In turn, the authentication request management component  404  may provide the sensor data and other data included in the authentication request to authentication analysis component  406 . 
     The authentication analysis component  406  uses the received sensor data to determine whether the requesting user is the authorized user. For example, the authentication analysis component  406  uses the sensor data describing the physical interaction performed by the requesting user along with the stored sensor data captured during the registration phase to determine whether the user requesting to perform the transaction is the authenticated user. This may be accomplished in several ways, such as by generating user characteristics (e.g., feature vector) describing the physical interaction from the sensor data received from the client device  102  and comparing the user characteristics to user characteristics generated from the sensor data stored in the data storage  412 . As another example, the sensor data received from the client device  102  may be used as input into a machine learning model trained based on the sensor data captured during the registration phase, resulting in a probability score indicating the likelihood that the physical interaction was performed by the authorized user. The authentication analysis component  406  may then determine whether the user requesting to perform the transaction is the authenticated user based on the probability score. 
     The authentication analysis component  406  may similarly use a characteristic noise pattern included in the sensor data to determine whether the user requesting to perform the transaction is the authenticated user. For example, the authentication analysis component  406  may generate a feature vector from the characteristic noise pattern, which is analyzed in relation to a feature vector generated from a characteristic noise pattern included in the sensor data captured during the registration phase. As another example, authentication analysis component  406  may be use the characteristic noise pattern as input into a machine learning model trained based on the sensor data captured during the registration phase. The authentication analysis component  406  may use the resulting probability score to determine whether the user requesting to perform the transaction is the authenticated user based on the probability score. 
     In some embodiments, the authentication analysis component  406  may evaluate the user characteristics describing the physical interaction and the noise characteristic patterns separately when determining whether an authentication requirement has been satisfied. For example, satisfaction of the authentication requirement may be based on each of the user characteristics describing the physical interaction and the noise characteristic patterns being separately satisfied. Alternatively, the user characteristics describing the physical interaction and the noise characteristic patterns may be evaluated in combination. For example, satisfaction of the authentication requirement may be based on a cumulative probability score determined based on the user characteristics describing the physical interaction and the noise characteristic pattern. 
     The functionality of the authentication analysis component  406  is discussed in greater detail below in relation to  FIG. 6 . The authentication analysis component  406  notifies the output component  408  indicating whether the physical interaction was determined to have been performed by the authorized user. In turn, the output component  408  transmits a response message to the service provider computing system  106  indicating whether the authorization requirement was satisfied. For example, if the authentication analysis component  406  determines that the physical interaction was performed by the authorized user, the output component  408  transmits a response message to the service provider computing system  106  indicating that the authorization requirement is satisfied. Alternatively, if the authentication analysis component  406  determines that the physical interaction was not performed by the authorized user, the output component  408  transmits a response message to the service provider computing system  106  indicating that the authorization requirement is not satisfied. 
     The feedback component  410  received feedback data describing performance of the authentication system  108 . For example, the feedback data may indicate whether the authentication system  108  correctly identified whether a physical interaction was performed by an authorized user. The feedback component  410  may use the received feedback data to further refine performance of the authentication system  108 . For example, the feedback data may be used generate additional training data to refine machine learning models used by the authentication system  108 . 
       FIG. 5  is a communication diagram showing an authentication system  108  providing authentication based on physical interaction and characteristic noise patterns, according to some example embodiments. As shown, a client device  102  may communicate with a service provider computing system  106 . For example, the client device  102  may communicate with the service provider computing system  106  to utilize an online service provided by the service provider computing system  106 . This may include requesting to perform transactions, such as logging into an account, transferring funds, and the like. 
     The service provider computing system  106  communicates with the authentication system  108  to authenticate requested transactions. For example, performance of a requested transaction may be conditioned upon satisfaction of an authentication requirement by a requesting user, such as the user performing a physical interaction. The authentication system  108  determines whether an authentication requirement based on a physical interaction has been satisfied. For example, the authentication system  108  uses sensor data captured by the client device  102  that describes the physical interaction as well as stored sensor data describing the physical interaction performed by an authorized user to determine whether the user requesting to perform the transaction is the authorized user. 
     The service provider computing system  106  transmits an authentication request to the authentication system  108  to initiate the authentication process. The authentication request is received by the authentication request management component  404  of the authentication system  108 . The authentication request may include data describing the requesting user, such as unique user identifier, device identifier, and the like. The authentication request may also include sensor data describing performance of the physical interaction by the requesting user. Alternatively, the authentication request management component  404  may communicate with the client device  102  to prompt the requesting user to perform the physical interaction. The client device  102  captures sensor data describing the physical interaction, which is returned to the authentication system  108  and received by the authentication request management component  404 . 
     The authentication request management component  404  provides the sensor data and other data received in the authentication request to the authentication analysis component  406 . The authentication analysis component  406  uses the sensor data to determine whether the physical interaction was performed by the authorized user. The determination may be based on user characteristics describing the physical interaction as well as a characteristic noise pattern identified from the sensor data. The authentication analysis component  406  provides a notification to the output component  408  indicating whether the physical interaction was determined to have been performed by the authorized user. 
     In turn, the output component  408  provides a response message to the service provider computing system  106  indicating whether the authentication requirement has been satisfied. For example, the output component  408  transmits a response message indicating that the authentication requirement has been satisfied if the physical interaction was determined to have been performed by the authorized user. Alternatively, the output component  408  transmits a response message indicating that the authentication requirement has not been satisfied if the physical interaction was determined to have not been performed by the authorized user. 
     The service provider computing system  106  may process the requested transaction based on the response message received from the output component  408 . For example, the service provider computing system  106  may approve (e.g., execute) the requested transaction if the response message indicates that the authentication request has been satisfied. Alternatively, the service provider computing system  106  may deny the requested transaction if the response message indicates that the authentication request has not been satisfied. 
     The service provider computing system  106  may provide the authentication system  108  with feedback data that is received by the feedback component  410 . The feedback data may indicate whether the determination provided by authentication system  108  was correct or incorrect. For example, the feedback data may indicate that the authentication system  108  incorrectly determined that the authentication requirement had been satisfied or had not been satisfied. Alternatively, the feedback data may indicate that the authentication system  108  correctly determined that the authentication requirement had been satisfied or had not been satisfied. The feedback data may include data identifying the requested transaction to which it pertains. 
     The received feedback data may be used to further refine performance of the authentication system  108 . For example, the feedback component  410  may use the feedback data and the corresponding sensor data to further train machine learning models used by the authentication analysis component  406 . As another example, the feedback component  410  may use the feedback data and the corresponding sensor data to identify additional features describing the physical interaction and/or the characteristic noise pattern for identifying whether a physical interaction was performed by the authorized user. In some embodiments, the feedback component  410  may avoid the use of outlier data points when training the machine learning models, which may reduce the rate of false acceptances and rejections. 
       FIG. 6  is a block diagram of an authentication analysis component  406 , according to some example embodiments. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components (e.g., modules) that are not germane to conveying an understanding of the inventive subject matter have been omitted from  FIG. 6 . However, a skilled artisan will readily recognize that various additional functional components/devices may be supported by the authentication analysis component  406  to facilitate additional functionality that is not specifically described herein. Furthermore, the various functional components/devices depicted in  FIG. 6  may reside on a single computing device or may be distributed across several computing devices in various arrangements such as those used in cloud-based architectures 
     As shown, the authentication analysis component  406  includes a sensor data receiving component  602 , a user characteristics generator  604 , a noise characteristic pattern generator  608 , a user characteristics model  608 , a noise characteristic pattern model  610 , a secondary machine learning model  612 , and a threshold comparison component  614 . 
     The sensor data receiving component  602  receives sensor data describing a physical interaction performed by a user requesting to perform an interaction. For example, the sensor data receiving component  602  receives the sensor data from the authentication request management component  404 . The sensor data may have been collected by sensors of a client device  102  used to request a transaction. For example, the user of the client device  102  may have been prompted to perform the physical interaction as an authentication requirement to execute the requested transaction. The sensor data receiving component  602  provides the received sensor data to the user characteristic generator  604  and the noise characteristic pattern generator  606 . 
     The user characteristic generator  604  uses the sensor data received from the sensor data receiving component  602  to generate user characteristics describing the physical interaction performed as part of the authentication requirement. The user characteristics may be a feature vector that represents the physical interaction performed by the user requesting to perform the transaction. 
     The noise characteristic pattern generator  606  generates a noise characteristic pattern from the sensor data. Each client device  102  may include physical variances and deviations caused during the manufacturing process. These deviations may be the result of varying solder points, minor defects in sensors, electrical components, and the like. These manufacturing deviations result in a unique noise characteristic pattern being included in the sensor data captured by the client device  102 . For example, the unique characteristic noise pattern may be represented in binary value deviation in digital or as voltage deviation in analogue chipsets. 
     The user characteristics generated by the user characteristics generator  604  and the noise characteristic pattern generated by the noise characteristic pattern generator  606  are both used to determine whether a physical interaction was performed by an authorized user. For example, the user characteristics generator  604  provides an input to the user characteristics model  608  based on the user characteristics generated from the sensor data and the noise characteristic pattern generator  606  provides an input to the noise characteristic pattern model  610  based on the noise characteristic pattern generated from the sensor data. 
     Both the user characteristics model  608  and the noise characteristic pattern model  610  are machine learning models generated based on sensor describing known physical interactions performed by the authorized user. For example, the sensor data may have been captured from the authorized user during a registration phase. The user characteristics model  608  and the noise characteristic pattern model  610  may also be subsequently retrained or refined based on sensor data subsequently captured during authentication request that were confirmed to have been or not have been performed by the authorized user. For example, the service provider computing system  106  may provide the authentication system  108  with feedback data indicating whether a physical interaction was confirmed to have or have not been performed by the authorized user. The associated sensor data may then be used as training data to further refine the user characteristics model  608  and the noise characteristic pattern model  610 . 
     The user characteristics model  608  is trained based on user characterizes describing a physical interaction. Accordingly, the trained user characteristics model  608  receives an input of a user characteristics representing a physical interaction and outputs a physical interaction score indicating the likelihood that the physical interaction was performed by the authorized user. 
     The noise characteristic pattern model  610  is trained based on noise characteristic patterns identified from the sensor data describing the physical interaction. Accordingly, the noise characteristic pattern model  610 , once trained, receives an input representing a noise characteristic pattern and outputs a sensor score indicating the likelihood that the physical interaction was performed by the client device  102  associated with the authorized user. 
     The authentication system  108  determines whether a physical interaction was performed by the authorized user based on a combination of the physical interaction score and the sensor score. For example, an input generated based on the physical interaction score and the sensor score are provided as input into a secondary machine learning model  612 . The secondary machine learning model  612  is a machine learning model that is trained based on the output of the user characteristics model  608  and the noise characteristic pattern model  610 . Accordingly, the secondary machine learning model  612  receives an input based on a combination of the physical interaction score and the sensor score and outputs an authentication score indicating a likelihood that the physical interaction was performed by the authorized user. 
     The resulting authentication score is provided to the threshold comparison component  614 . The threshold comparison component  614  compares the authentication score to a threshold authentication score to determine whether the physical interaction was performed by the authorized user. For example, the threshold comparison component  614  determines that the physical interaction was performed by the authorized user when the authorization score meets or exceeds the threshold authentication score. Alternatively, the threshold comparison component  614  determines that the physical interaction was not performed by the authorized user when the authorization score is below the threshold authentication score. The threshold comparison component  614  may provide it output to other component of the authentication system  108 , such as the output component  408 . 
       FIG. 7  is a flowchart showing a method  700  for authentication based on physical interaction and characteristic noise patterns, according to certain example embodiments. The method  700  may be embodied in computer readable instructions for execution by one or more processors such that the operations of the method  700  may be performed in part or in whole by the authentication analysis component  406 ; accordingly, the method  700  is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method  700  may be deployed on various other hardware configurations and the method  700  is not intended to be limited to the authentication analysis component  406 . 
     At operation  702 , the sensor data receiving component  602  receives sensor data describing a physical interaction. For example, the sensor data receiving component  602  receives the sensor data from the authentication request management component  404 . The sensor data may have been collected by sensors of a client device  102  used to request a transaction. For example, the user of the client device  102  may have been prompted to perform the physical interaction as an authentication requirement to execute the requested transaction. The sensor data receiving component  602  provides the received sensor data to the user characteristic generator  604  and the noise characteristic pattern generator  606 . 
     At operation  704 , the noise characteristic pattern generator  606  identifies (e.g., generates) a characteristic noise pattern from the sensor data. Each client device  102  may include physical variances and deviations caused during the manufacturing process. These deviations may be the result of varying solder points, minor defects in sensors, electrical components, and the like. These manufacturing deviations result in a unique characteristic noise pattern being included in the sensor data captured by the client device  102 . The noise characteristic pattern generator  606  identifies a noise characteristic pattern from the sensor data, which can be used along with the sensor data describing the physical interaction to determine whether the physical interaction was performed by an authorized user. 
     At operation  706 , the authentication analysis component  406  determines an authentication score based on the sensor data and the characteristic noise pattern. For example, the authentication analysis component  406  may determine the authentication score using the method  800  described below in relation to  FIG. 8 . 
     At operation  708 , the threshold comparison component  614  compares the authentication score to the threshold authentication score. The threshold comparison component  614  compares the authentication score to a threshold authentication score to determine whether the physical interaction was performed by the authorized user. 
     At operation  710 , the threshold comparison component  614  determines whether the physical interaction was performed by an authorized user based on the comparison. For example, the threshold comparison component  614  determines that the physical interaction was performed by the authorized user when the authorization score meets or exceeds the threshold authentication score. Alternatively, the threshold comparison component  614  determines that the physical interaction was not performed by the authorized user when the authorization score is below the threshold authentication score. The threshold comparison component  614  may provide it output to other component of the authentication system  108 , such as the output component  408 . 
       FIG. 8  is a flowchart showing a method  800  for determining an authentication score, according to certain example embodiments. The method  800  may be embodied in computer readable instructions for execution by one or more processors such that the operations of the method  800  may be performed in part or in whole by the authentication analysis component  406 ; accordingly, the method  800  is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method  800  may be deployed on various other hardware configurations and the method  800  is not intended to be limited to the authentication analysis component  406 . 
     At operation  802 , the user characteristics generator  604  generates user characteristic representing a physical interaction. For example, user characteristic generator  604  uses sensor data received from the sensor data receiving component  602  to generate user characteristics describing the physical interaction performed as part of the authentication requirement. The user characteristics may be a feature vector that represents the physical interaction performed by the user requesting to perform the transaction. 
     At operation  804 , the noise characteristic pattern generator  606  generates a characteristic noise pattern. Each client device  102  may include physical variances and deviations caused during the manufacturing process. These deviations may be the result of varying solder points, minor defects in sensors, electrical components, and the like. These manufacturing deviations result in a unique noise characteristic pattern being included in the sensor data captured by the client device  102 . For example, the unique characteristic noise pattern may be represented in binary value deviation in digital or as voltage deviation in analogue chipsets. 
     At operation  806 , user characteristics model  608  determines a physical interaction score based on the user characteristics, and at operation  808 , the noise characteristic pattern model  610  determines a sensor score based on the characteristic noise pattern. Both the user characteristics model  608  and the noise characteristic pattern model  610  are machine learning models generated based on sensor describing known physical interactions performed by the authorized user. The user characteristics model  608  receives an input of the user characteristics representing the physical interaction and outputs the physical interaction score indicating the likelihood that the physical interaction was performed by the authorized user. Similarly, the noise characteristic pattern model  610  receives an input representing a noise characteristic pattern and outputs a sensor score indicating the likelihood that the physical interaction was performed by the client device  102  associated with the authorized user. 
     At operation  810 , the secondary machine learning model  612  determines an authentication score based on the physical interaction score and the sensor score. The secondary machine learning model  612  is a machine learning model that is trained based on the output of the user characteristics model  608  and the noise characteristic pattern model  610  during a training phase. The secondary machine learning model  612  receives an input based on a combination of the physical interaction score and the sensor score and outputs the authentication score indicating a likelihood that the physical interaction was performed by the authorized user. 
       FIGS. 9A-9D  show a user interface for providing an authentication requirement based on a physical interaction, according to some example embodiments. As shown in  FIG. 9A , the user interface  900  prompts the user to perform a physical interaction by swiping a path connecting some of the presented dots. The path may be selected by the user or assigned to the user during an initiation phase. To perform the physical interaction, the user may use an input device, such as a touchscreen or mouse, to perform the path across the shown dots. While the user interface shows the specified path to be followed, this is only for illustrative purposes. In practice, the path may be shown or concealed when presented to the user during an authentication request. 
     As shown in  FIG. 9B , the user interface  900  prompts the user to perform a physical interaction by spinning a circle presented on the screen. The pattern in which the user is to spin the circle may be selected by the user or assigned to the user during an initiation phase. To perform the physical interaction, the user may use an input device, such as a touchscreen or mouse, to rotate (e.g., spin) the circle in the specified pattern. 
     As shown in  FIG. 9C , the user interface  900  prompts the user to perform a physical interaction by entering a pattern of multiple points over a presented image. The pattern and/or specified points may be selected by the user or assigned to the user during an initiation phase. To perform the physical interaction, the user may use an input device, such as a touchscreen or mouse, to provide an input at specified point and in a desired order. While the user interface shows the specified points and order to be entered, this is only for illustrative purposes. In practice, the points and order may be shown or concealed when presented to the user during an authentication request. 
     As shown in  FIG. 9D , the user interface  900  prompts the user to perform a physical interaction by entering a specified PIN. The PIN the user is to enter may be selected by the user or assigned to the user during an initiation phase. To perform the physical interaction, the user may use an input device, such as a touchscreen or mouse, to select from the presented numbers to form the PIN. 
     Software Architecture 
       FIG. 10  is a block diagram illustrating an example software architecture  1006 , which may be used in conjunction with various hardware architectures herein described.  FIG. 10  is a non-limiting example of a software architecture  1006  and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture  1006  may execute on hardware such as machine  1100  of  FIG. 11  that includes, among other things, processors  1104 , memory  1114 , and (input/output) I/O components  1118 . A representative hardware layer  1052  is illustrated and can represent, for example, the machine  1100  of  FIG. 11 . The representative hardware layer  1052  includes a processing unit  1054  having associated executable instructions  1004 . Executable instructions  1004  represent the executable instructions of the software architecture  1006 , including implementation of the methods, components, and so forth described herein. The hardware layer  1052  also includes memory and/or storage modules  1056 , which also have executable instructions  1004 . The hardware layer  1052  may also comprise other hardware  1058 . 
     In the example architecture of  FIG. 10 , the software architecture  1006  may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture  1006  may include layers such as an operating system  1002 , libraries  1020 , frameworks/middleware  1018 , applications  1016 , and a presentation layer  1014 . Operationally, the applications  1016  and/or other components within the layers may invoke application programming interface (API) calls  1008  through the software stack and receive a response such as messages  1012  in response to the API calls  1008 . The layers illustrated are representative in nature and not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware  1018 , while others may provide such a layer. Other software architectures may include additional or different layers. 
     The operating system  1002  may manage hardware resources and provide common services. The operating system  1002  may include, for example, a kernel  1022 , services  1024 , and drivers  1026 . The kernel  1022  may act as an abstraction layer between the hardware and the other software layers. For example, the kernel  1022  may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services  1024  may provide other common services for the other software layers. The drivers  1026  are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  1026  include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth, depending on the hardware configuration. 
     The libraries  1020  provide a common infrastructure that is used by the applications  1016  and/or other components and/or layers. The libraries  1020  provide functionality that allows other software components to perform tasks in an easier fashion than to interface directly with the underlying operating system  1002  functionality (e.g., kernel  1022 , services  1024 , and/or drivers  1026 ). The libraries  1020  may include system libraries  1044  (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, and the like. In addition, the libraries  1020  may include API libraries  1046  such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries  1020  may also include a wide variety of other libraries  1048  to provide many other APIs to the applications  1016  and other software components/modules. 
     The frameworks/middleware  1018  (also sometimes referred to as middleware) provide a higher-level common infrastructure that may be used by the applications  1016  and/or other software components/modules. For example, the frameworks/middleware  1018  may provide various graphical user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware  1018  may provide a broad spectrum of other APIs that may be used by the applications  1016  and/or other software components/modules, some of which may be specific to a particular operating system  1002  or platform. 
     The applications  1016  include built-in applications  1038  and/or third-party applications  1040 . Examples of representative built-in applications  1038  may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. Third-party applications  1040  may include an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform, and may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. The third-party applications  1040  may invoke the API calls  1008  provided by the mobile operating system (such as operating system  1002 ) to facilitate functionality described herein. 
     The applications  1016  may use built in operating system functions (e.g., kernel  1022 , services  1024 , and/or drivers  1026 ), libraries  1020 , and frameworks/middleware  1018  to create UIs to interact with users of the system. Alternatively, or additionally, in some systems, interactions with a user may occur through a presentation layer, such as presentation layer  1014 . In these systems, the application/component “logic” can be separated from the aspects of the application/component that interact with a user. 
       FIG. 11  is a block diagram illustrating components of a machine  1100 , according to some example embodiments, able to read instructions  1004  from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG. 11  shows a diagrammatic representation of the machine  1100  in the example form of a computer system, within which instructions  1110  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1100  to perform any one or more of the methodologies discussed herein may be executed. As such, the instructions  1110  may be used to implement modules or components described herein. The instructions  1110  transform the general, non-programmed machine  1100  into a particular machine  1100  programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  1100  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1100  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1100  may comprise, but not be limited to, a server computer, a client computer, a PC, a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine  1100  capable of executing the instructions  1110 , sequentially or otherwise, that specify actions to be taken by machine  1100 . Further, while only a single machine  1100  is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions  1110  to perform any one or more of the methodologies discussed herein. 
     The machine  1100  may include processors  1104 , memory/storage  1106 , and I/O components  1118 , which may be configured to communicate with each other such as via a bus  1102 . The memory/storage  1106  may include a memory  1114 , such as a main memory, or other memory storage, and a storage unit  1116 , both accessible to the processors  1104  such as via the bus  1102 . The storage unit  1116  and memory  1114  store the instructions  1110  embodying any one or more of the methodologies or functions described herein. The instructions  1110  may also reside, completely or partially, within the memory  1114 , within the storage unit  1116 , within at least one of the processors  1104  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1100 . Accordingly, the memory  1114 , the storage unit  1116 , and the memory of processors  1104  are examples of machine-readable media. 
     The I/O components  1118  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1118  that are included in a particular machine  1100  will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1118  may include many other components that are not shown in  FIG. 11 . The I/O components  1118  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components  1118  may include output components  1126  and input components  1128 . The output components  1126  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1128  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1118  may include biometric components  1130 , motion components  1134 , environmental components  1136 , or position components  1138  among a wide array of other components. For example, the biometric components  1130  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components  1134  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1136  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometer that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1138  may include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1118  may include communication components  1140  operable to couple the machine  1100  to a network  1132  or devices  1120  via coupling  1124  and coupling  1122 , respectively. For example, the communication components  1140  may include a network interface component or other suitable device to interface with the network  1132 . In further examples, communication components  1140  may include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  1120  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  1140  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1140  may include radio frequency identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1140  such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting a NFC beacon signal that may indicate a particular location, and so forth. 
     “CARRIER SIGNAL” in this context refers to any intangible medium that is capable of storing, encoding, or carrying instructions  1110  for execution by the machine  1100 , and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions  1110 . Instructions  1110  may be transmitted or received over the network  1132  using a transmission medium via a network interface device and using any one of a number of well-known transfer protocols. 
     “CLIENT DEVICE” in this context refers to any machine  1100  that interfaces to a communications network  1132  to obtain resources from one or more server systems or other client devices  102 ,  104 . A client device  102 ,  104  may be, but is not limited to, mobile phones, desktop computers, laptops, PDAs, smart phones, tablets, ultra books, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, STBs, or any other communication device that a user may use to access a network  1132 . 
     “COMMUNICATIONS NETWORK” in this context refers to one or more portions of a network  1132  that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a LAN, a wireless LAN (WLAN), a WAN, a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network  1132  or a portion of a network  1132  may include a wireless or cellular network and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology. 
     “MACHINE-READABLE MEDIUM” in this context refers to a component, device or other tangible media able to store instructions  1110  and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., erasable programmable read-only memory (EEPROM)), and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions  1110 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions  1110  (e.g., code) for execution by a machine  1100 , such that the instructions  1110 , when executed by one or more processors  1104  of the machine  1100 , cause the machine  1100  to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” refers to “non-transitory” machine-readable mediums and excludes signals or other “transitory” computer readable mediums. A “non-transitory” machine-readable medium is a physical device that can store data for a period of time during which the stored data may be transferrable or reproducible. Examples of a non-transitory machine-readable medium are a physical memory device, Random Access Memory (RAM), etc. In contrast, transitory machine-readable mediums are not physical and store data only momentarily, such as a signal. 
     “COMPONENT” in this context refers to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors  1104 ) may be configured by software (e.g., an application  1016  or application portion) as a hardware component that operates to perform certain operations as described herein. A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor  1104  or other programmable processor  1104 . Once configured by such software, hardware components become specific machines  1100  (or specific components of a machine  1100 ) uniquely tailored to perform the configured functions and are no longer general-purpose processors  1104 . It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component” (or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor  1104  configured by software to become a special-purpose processor, the general-purpose processor  1104  may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors  1104 , for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time. Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses  1102 ) between or among two or more of the hardware components. In embodiments in which multiple hardware components are configured or instantiated at different times, communications between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). The various operations of example methods described herein may be performed, at least partially, by one or more processors  1104  that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors  1104  may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented component” refers to a hardware component implemented using one or more processors  1104 . Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors  1104  being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors  1104  or processor-implemented components. Moreover, the one or more processors  1104  may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines  1100  including processors  1104 ), with these operations being accessible via a network  1132  (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors  1104 , not only residing within a single machine  1100 , but deployed across a number of machines  1100 . In some example embodiments, the processors  1104  or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors  1104  or processor-implemented components may be distributed across a number of geographic locations. 
     “PROCESSOR” in this context refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor  1104 ) that manipulates data values according to control signals (e.g., “commands,” “op codes,” “machine code,” etc.) and which produces corresponding output signals that are applied to operate a machine  1100 . A processor  1104  may be, for example, a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, a radio-frequency integrated circuit (RFIC) or any combination thereof. A processor  1104  may further be a multi-core processor having two or more independent processors  1104  (sometimes referred to as “cores”) that may execute instructions  1110  contemporaneously. 
     Non-Limiting Examples 
     Example 1 is a method comprising: receiving sensor data captured by a set of sensors of a client device, the sensor data describing a physical interaction with the client device that was performed as part of an authentication request; identifying a characteristic noise pattern from the sensor data, the characteristic noise pattern caused by manufacturing deviations of the set of sensors that captured the sensor data; determining an authentication score based on the sensor data describing the physical interaction with the client device and the characteristic noise pattern, the authentication score indicating a likelihood that the physical interaction was performed by an authenticated user; and determining whether to approve an authentication request based on a comparison of the authentication score to a threshold authentication score. 
     In Example 2, the subject matter of Example 1 includes, wherein determining the authentication score comprises: determining a physical interaction score based on the sensor data describing the physical interaction with the client device and historical sensor data describing physical interactions performed by the authenticated user; determining a sensor score based on the characteristic noise pattern identified from the sensor data and historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user; and determining the authentication score based on the physical interaction score and the sensor score. 
     In Example 3, the subject matter of Examples 1-2 includes, wherein determining the physical interaction score comprises: generating a first input based on the sensor data describing the physical interaction with the client device; and providing the first input into a first machine learning model, yielding the physical interaction score, the first machine learning model having been trained based on the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 4, the subject matter of Examples 1-3 includes, wherein determining the sensor score comprises: generating a second input based on the characteristic noise pattern identified from the sensor data; and providing the second input into a second machine learning model, yielding the sensor score, the second machine learning model having been trained based on the historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 5, the subject matter of Examples 1-4 includes, wherein determining the authentication score based on the physical interaction score and the sensor score comprises: generating a third input based on the physical interaction score and the sensor score; and providing the third input into a third machine learning model, yielding the authentication score, the third machine learning model having been trained based on the historical sensor data describing physical interactions performed by the authenticated user and the historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 6, the subject matter of Examples 1-6 includes, wherein determining whether to approve the authentication request based on the comparison of the authentication score to the threshold authentication score comprises: in response to determining that the authentication score exceeds the threshold authentication score, approving the authentication request. 
     In Example 7, the subject matter of Examples 1-6 includes, wherein determining whether to approve the authentication request based on the comparison of the authentication score to the threshold authentication score comprises: in response to determining that the authentication score is less than the threshold authentication score, denying the authentication request. 
     Example 8 is a system comprising: one or more computer processors; and one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to perform operations comprising: receiving sensor data captured by a set of sensors of a client device, the sensor data describing a physical interaction with the client device that was performed as part of an authentication request; identifying a characteristic noise pattern from the sensor data, the characteristic noise pattern caused by manufacturing deviations of the set of sensors that captured the sensor data; determining an authentication score based on the sensor data describing the physical interaction with the client device and the characteristic noise pattern, the authentication score indicating a likelihood that the physical interaction was performed by an authenticated user; and determining whether to approve an authentication request based on a comparison of the authentication score to a threshold authentication score. 
     In Example 9, the subject matter of Example 8 includes, wherein determining the authentication score comprises: determining a physical interaction score based on the sensor data describing the physical interaction with the client device and historical sensor data describing physical interactions performed by the authenticated user; determining a sensor score based on the characteristic noise pattern identified from the sensor data and historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user; and determining the authentication score based on the physical interaction score and the sensor score. 
     In Example 10, the subject matter of Examples 8-9 includes, wherein determining the physical interaction score comprises: generating a first input based on the sensor data describing the physical interaction with the client device; and providing the first input into a first machine learning model, yielding the physical interaction score, the first machine learning model having been trained based on the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 11, the subject matter of Example 8-10 includes, wherein determining the sensor score comprises: generating a second input based on the characteristic noise pattern identified from the sensor data; and providing the second input into a second machine learning model, yielding the sensor score, the second machine learning model having been trained based on the historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 12, the subject matter of Examples 8-11 includes, wherein determining the authentication score based on the physical interaction score and the sensor score comprises: generating a third input based on the physical interaction score and the sensor score; and providing the third input into a third machine learning model, yielding the authentication score, the third machine learning model having been trained based on the historical sensor data describing physical interactions performed by the authenticated user and the historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 13, the subject matter of Examples 8-12 includes, wherein determining whether to approve the authentication request based on the comparison of the authentication score to the threshold authentication score comprises: in response to determining that the authentication score exceeds the threshold authentication score, approving the authentication request. 
     In Example 14, the subject matter of Examples 8-13 includes, wherein determining whether to approve the authentication request based on the comparison of the authentication score to the threshold authentication score comprises: in response to determining that the authentication score is less than the threshold authentication score, denying the authentication request. 
     Example 15 is a non-transitory computer-readable medium storing instructions that, when executed by one or more computer processors of one or more computing devices, cause the one or more computing devices to perform operations comprising: receiving sensor data captured by a set of sensors of a client device, the sensor data describing a physical interaction with the client device that was performed as part of an authentication request; identifying a characteristic noise pattern from the sensor data, the characteristic noise pattern caused by manufacturing deviations of the set of sensors that captured the sensor data; determining an authentication score based on the sensor data describing the physical interaction with the client device and the characteristic noise pattern, the authentication score indicating a likelihood that the physical interaction was performed by an authenticated user; and determining whether to approve an authentication request based on a comparison of the authentication score to a threshold authentication score. 
     In Example 16, the subject matter of Example 15 includes, wherein determining the authentication score comprises: determining a physical interaction score based on the sensor data describing the physical interaction with the client device and historical sensor data describing physical interactions performed by the authenticated user; determining a sensor score based on the characteristic noise pattern identified from the sensor data and historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user; and determining the authentication score based on the physical interaction score and the sensor score. 
     In Example 17, the subject matter of Examples 15-16 includes, wherein determining the physical interaction score comprises: generating a first input based on the sensor data describing the physical interaction with the client device; and providing the first input into a first machine learning model, yielding the physical interaction score, the first machine learning model having been trained based on the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 18, the subject matter of Examples 15-17 includes, wherein determining the sensor score comprises: generating a second input based on the characteristic noise pattern identified from the sensor data; and providing the second input into a second machine learning model, yielding the sensor score, the second machine learning model having been trained based on the historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 19, the subject matter of Examples 115-18 includes, wherein determining the authentication score based on the physical interaction score and the sensor score comprises: generating a third input based on the physical interaction score and the sensor score; and providing the third input into a third machine learning model, yielding the authentication score, the third machine learning model having been trained based on the historical sensor data describing physical interactions performed by the authenticated user and the historical characteristic noise patterns identified from the historical sensor data describing physical interactions performed by the authenticated user. 
     In Example 20, the subject matter of Examples 15-19 includes, wherein determining whether to approve the authentication request based on the comparison of the authentication score to the threshold authentication score comprises: in response to determining that the authentication score exceeds the threshold authentication score, approving the authentication request.