Abstract: This disclosure describes a bot detection system that leverages deep learning to facilitate bot detection and mitigation, and that works even when an attacker changes an attack script. The approach herein provides for a system that rapidly and automatically (without human intervention) retrains on new, updated or modified attack vectors.

Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.

Abstract: This disclosure describes a bot detection system that leverages deep learning to facilitate bot detection and mitigation, and that works even when an attacker changes an attack script. The approach herein provides for a system that rapidly and automatically (without human intervention) retrains on new, updated or modified attack vectors.

Abstract: A method of detecting bots, preferably in an operating environment supported by a content delivery network (CDN) that comprises a shared infrastructure of distributed edge servers from which CDN customer content is delivered to requesting end users (clients). The method begins as clients interact with the edge servers. As such interactions occur, transaction data is collected. The transaction data is mined against a set of “primitive” or “compound” features sets to generate a database of information. In particular, preferably the database comprises one or more data structures, wherein a given data structure associates a feature value with its relative percentage occurrence across the collected transaction data. Thereafter, and upon receipt of a new transaction request, primitive or compound feature set data derived from the new transaction request are compared against the database. Based on the comparison, an end user client associated with the new transaction request is then characterized, e.g.

Abstract: This disclosure describes a technique to determine whether a client computing device accessing an API is masquerading its device type (i.e., pretending to be a device that it is not). To this end, and according to this disclosure, the client performs certain processing requested by the server to reveal its actual processing capabilities and thereby its true device type, whereupon—once the server learns the true nature of the client device—it can take appropriate actions to mitigate or prevent further damage. To this end, during the API transaction the server returns information to the client device that causes the client device to perform certain computations or actions. The resulting activity is captured on the client computing and then transmitted back to the server, which then analyzes the data to inform its decision about the true client device type.

Abstract: This disclosure describes a technique to determine whether a client computing device accessing an API is masquerading its device type (i.e., pretending to be a device that it is not). To this end, and according to this disclosure, the client performs certain processing requested by the server to reveal its actual processing capabilities and thereby its true device type, whereupon—once the server learns the true nature of the client device—it can take appropriate actions to mitigate or prevent further damage. To this end, during the API transaction the server returns information to the client device that causes the client device to perform certain computations or actions. The resulting activity is captured on the client computing and then transmitted back to the server, which then analyzes the data to inform its decision about the true client device type.

Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.

Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.

Abstract: A method of detecting bots, preferably in an operating environment supported by a content delivery network (CDN) that comprises a shared infrastructure of distributed edge servers from which CDN customer content is delivered to requesting end users (clients). The method begins as clients interact with the edge servers. As such interactions occur, transaction data is collected. The transaction data is mined against a set of “primitive” or “compound” features sets to generate a database of information. In particular, preferably the database comprises one or more data structures, wherein a given data structure associates a feature value with its relative percentage occurrence across the collected transaction data. Thereafter, and upon receipt of a new transaction request, primitive or compound feature set data derived from the new transaction request are compared against the database. Based on the comparison, an end user client associated with the new transaction request is then characterized, e.g.

Abstract: A method of detecting bots, preferably in an operating environment supported by a content delivery network (CDN) that comprises a shared infrastructure of distributed edge servers from which CDN customer content is delivered to requesting end users (clients). The method begins as clients interact with the edge servers. As such interactions occur, transaction data is collected. The transaction data is mined against a set of “primitive” or “compound” features sets to generate a database of information. In particular, preferably the database comprises one or more data structures, wherein a given data structure associates a feature value with its relative percentage occurrence across the collected transaction data. Thereafter, and upon receipt of a new transaction request, primitive or compound feature set data derived from the new transaction request are compared against the database. Based on the comparison, an end user client associated with the new transaction request is then characterized, e.g.

Abstract: This disclosure describes a bot detection system that leverages deep learning to facilitate bot detection and mitigation, and that works even when an attacker changes an attack script. The approach herein provides for a system that rapidly and automatically (without human intervention) retrains on new, updated or modified attack vectors.

Abstract: A non-transitory computer readable storage medium including instructions that, when executed by a computing system, cause the computing system to perform operations. The operations include collecting, by a processing device, raw data regarding a user action. The operations also include converting, by the processing device, the raw data to characteristic test data (CTD), wherein the CTD represents behavior characteristics of a current user. The operations also include identifying, by the processing device, a characteristic model corresponding to the behavior characteristics represented by the CTD. The operations also include generating, by the processing device, a predictor from a comparison of the CTD against the corresponding characteristic model, wherein the predictor comprises a score indicating a probability that the user action came from an authenticated user.

Abstract: This disclosure describes a technique to determine whether a client computing device accessing an API is masquerading its device type (i.e., pretending to be a device that it is not). To this end, and according to this disclosure, the client performs certain processing requested by the server to reveal its actual processing capabilities and thereby its true device type, whereupon—once the server learns the true nature of the client device—it can take appropriate actions to mitigate or prevent further damage. To this end, during the API transaction the server returns information to the client device that causes the client device to perform certain computations or actions. The resulting activity is captured on the client computing and then transmitted back to the server, which then analyzes the data to inform its decision about the true client device type.

Abstract: A method of detecting bots, preferably in an operating environment supported by a content delivery network (CDN) that comprises a shared infrastructure of distributed edge servers from which CDN customer content is delivered to requesting end users (clients). The method begins as clients interact with the edge servers. As such interactions occur, transaction data is collected. The transaction data is mined against a set of “primitive” or “compound” features sets to generate a database of information. In particular, preferably the database comprises one or more data structures, wherein a given data structure associates a feature value with its relative percentage occurrence across the collected transaction data. Thereafter, and upon receipt of a new transaction request, primitive or compound feature set data derived from the new transaction request are compared against the database. Based on the comparison, an end user client associated with the new transaction request is then characterized, e.g.

Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.

Abstract: A non-transitory computer readable storage medium including instructions that, when executed by a computing system, cause the computing system to perform operations. The operations include collecting, by a processing device, raw data regarding a user action. The operations also include converting, by the processing device, the raw data to characteristic test data (CTD), wherein the CTD represents behavior characteristics of a current user. The operations also include identifying, by the processing device, a characteristic model corresponding to the behavior characteristics represented by the CTD. The operations also include generating, by the processing device, a predictor from a comparison of the CTD against the corresponding characteristic model, wherein the predictor comprises a score indicating a probability that the user action came from an authenticated user.

Abstract: A technique to slow down or block creation of automated attack scripts uses a detector configured to discriminate whether particular attack-like activity is a true attack, or simply a hacker “testing” an automated attack script, and then permitting any such test script to continue working (attacking) the site, albeit on a limited basis. In this manner, the hacker receives an indication that his or her automated attack script is already working. Thereafter, when the detector later detects a launch of an actual attack based on or otherwise associated with the automated attack script (previously under test), the attack fails either because the script was not a working script in the first instance, or because information learned about the script is used to adjust the site as necessary to then prepare adequately for a true attack.

Abstract: This disclosure describes a technique to determine whether a client computing device accessing an API is masquerading its device type (i.e., pretending to be a device that it is not). To this end, and according to this disclosure, the client performs certain processing requested by the server to reveal its actual processing capabilities and thereby its true device type, whereupon—once the server learns the true nature of the client device—it can take appropriate actions to mitigate or prevent further damage. To this end, during the API transaction the server returns information to the client device that causes the client device to perform certain computations or actions. The resulting activity is captured on the client computing and then transmitted back to the server, which then analyzes the data to inform its decision about the true client device type.

Abstract: A method of detecting bots, preferably in an operating environment supported by a content delivery network (CDN) that comprises a shared infrastructure of distributed edge servers from which CDN customer content is delivered to requesting end users (clients). The method begins as clients interact with the edge servers. As such interactions occur, transaction data is collected. The transaction data is mined against a set of “primitive” or “compound” features sets to generate a database of information. In particular, preferably the database comprises one or more data structures, wherein a given data structure associates a feature value with its relative percentage occurrence across the collected transaction data. Thereafter, and upon receipt of a new transaction request, primitive or compound feature set data derived from the new transaction request are compared against the database. Based on the comparison, an end user client associated with the new transaction request is then characterized, e.g.

Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.