Abstract: Embodiments detect risky situations in which a domain name record remains viable after the target it identified is not. Such dangling records create various risks because substitute targets, such as fraudulent websites, may be installed without the knowledge of the original target's owner. By obtaining and correlating data from multiple tenants, a cloud service provider detects dangling structures and any attempts to exploit them. Dangling records may specify a custom domain name, for example, or a static IP address that can be misused. In response, the provider's security infrastructure can alert the original target's owner, block the attempted exploit, or otherwise mitigate the risks. Traffic monitoring, control plane API invocations, and domain name server queries may be employed by the security infrastructure to detect resource deletion, resource creation, and resource access attempts that correspond with vulnerable records or suspect activity involving them.

Abstract: Generally discussed herein are devices, systems, and methods for secure container operation. A behavior profile of normal container operation can be generated, such as by using crowd sourced data. A container monitor can provide container actions of an application in a deployed container. The container action can be compared to a behavior profile that indicates normal behavior of the container. A communication can in response to the container actions being inconsistent with the normal behavior of the behavior profile. The container can be halted to stop the abnormal behavior.

Abstract: Cybersecurity enhancements expose likely cyberattacks and command abuse while reducing false positives. Some embodiments ascertain an operating system mismatch, which occurs when a command tailored for operating system X is asserted in an environment tailored to operating system Y. False positives may be reduced by alerting on such a mismatch only when a command's process belongs to a web server or other targeted process, or uses the same supporting technology (e.g., framework, scripting language, or runtime environment) as the web server or other targeted process. Some embodiments watch for command abuse by spotting assertions of commands that appear frequently in cyberattacks even though those commands also have legitimate uses such as system administration, network administration, or software development.

Abstract: The automated estimation that an interface service has been misconfigured. Sensitive interface services are first identified based on common characteristics, and those characteristics are associated with sensitivity based on behavior across multiple clusters. Thereafter, the threat assessment estimates that a particular interface service is misconfigured if the particular interface service has these same common characteristics, is accessible from outside the cluster, and does not require authentication. Cluster administrators can therefore be more fully and timely advised when a misconfiguration of an interface service subjects their cluster to undue security risks.

Abstract: The automated estimation that an interface service has been misconfigured. Sensitive interface services are first identified based on common characteristics, and those characteristics are associated with sensitivity based on behavior across multiple clusters. Thereafter, the threat assessment estimates that a particular interface service is misconfigured if the particular interface service has these same common characteristics, is accessible from outside the cluster, and does not require authentication. Cluster administrators can therefore be more fully and timely advised when a misconfiguration of an interface service subjects their cluster to undue security risks.

Abstract: A previously-unknown type of attack on a web application can be detected dynamically using server logs. An alert can be raised for an application that returns a valid response to the potential attacker (e.g., when an http (hypertext transfer protocol) status code of 200 is returned to the requestor). Server logs can be analyzed to identify an external computer that uses the same attack methodology on multiple targets. The external computer may attempt to access the same Uniform Resource Identifier (URI) on various web sites. In many cases, the http status code that is returned is an error code. Characteristics such as but not limited to fast crawling and numerous error status codes being returned to a particular requestor can be used by a machine learning (ML) system to identify potentially malicious external computing devices and/or vulnerable URIs.

Abstract: According to examples, an apparatus may include a memory on which is stored machine-readable instructions that may cause a processor to receive a request to upload a file to a directory and determine whether the request is a request to upload a predefined type of file to the directory. In addition, based on a determination that the request is a request to upload the predefined type of file to the directory, the processor may determine, through application of a predictive model, whether the directory is a user content directory and based on a determination that the application of the predictive model indicates that the directory is a user content directory, block the request and/or output a notification regarding the receipt of the request.

Abstract: “Sensitive” URIs for a website can be determined. Access attempts to a sensitive URI can be extracted from server logs. As used herein, sensitive URIs are URIs which if breached are likely to result in harm to the website owner. Access to sensitive URIs can be restricted to trusted accessors. Trusted accessors can be determined by filtering out untrusted accessors using thresholds and/or machine learning techniques. After filtering out untrusted accessors, any remaining accessors can be identified as trusted accessors. Trusted accessors can be added to a whitelist. Access requests to access-restricted URIs by an accessor not in the whitelist can be denied and an alert can be generated. Access requests to access-restricted URIs by an accessor in the whitelist can be granted.

Abstract: Systems, methods, and apparatuses are provided for restricting access to a web resource. Website access information is obtained by monitoring accesses to a plurality of websites for each access, which may include a network identifier of an access requestor, a website identifier, and an access time for each request. Based on at least the website access information, it may be determined that a particular access requestor has accessed a number of different websites in a given time period. As a result, the particular access requestor may be classified as a web robot. A request to permit access to a web resource is received by the particular access requestor. In response to receiving the request to permit access to the web resource, the particular access requestor is prevented from accessing the web resource and/or a notification is generated that the particular access requestor is attempting to access the web resource.

Abstract: Cybersecurity enhancements expose likely cyberattacks and command abuse while reducing false positives. Some embodiments ascertain an operating system mismatch, which occurs when a command tailored for operating system X is asserted in an environment tailored to operating system Y. False positives may be reduced by alerting on such a mismatch only when a command's process belongs to a web server or other targeted process, or uses the same supporting technology (e.g., framework, scripting language, or runtime environment) as the web server or other targeted process. Some embodiments watch for command abuse by spotting assertions of commands that appear frequently in cyberattacks even though those commands also have legitimate uses such as system administration, network administration, or software development.

Abstract: A method includes obtaining a dictionary, data for a set of web requests, and definitions of a first set of clusters associated with vulnerability scanners. The method includes identifying a set of clients that transmitted the second set of web requests. The method includes generating a second set of feature vectors, which each corresponds to one of the clients. Each element in each feature vector corresponds respectively to an entry in the dictionary. The method includes clustering the second set of feature vectors into a second set of clusters. The method includes, in response to a first distance between a selected cluster of the second set of clusters and one of the first set of clusters being less than a first predetermined distance, (i) identifying one of the set of web services that received web requests corresponding to feature vectors in the selected cluster and (ii) generating a scanning alert.

Abstract: Generally discussed herein are devices, systems, and methods for secure container operation. A behavior profile of normal container operation can be generated, such as by using crowd sourced data. A container monitor can provide container actions of an application in a deployed container. The container action can be compared to a behavior profile that indicates normal behavior of the container. A communication can in response to the container actions being inconsistent with the normal behavior of the behavior profile. The container can be halted to stop the abnormal behavior.

Abstract: “Sensitive” URIs for a website can be determined. Access attempts to a sensitive URI can be extracted from server logs. As used herein, sensitive URIs are URIs which if breached are likely to result in harm to the website owner. Access to sensitive URIs can be restricted to trusted accessors. Trusted accessors can be determined by filtering out untrusted accessors using thresholds and/or machine learning techniques. After filtering out untrusted accessors, any remaining accessors can be identified as trusted accessors. Trusted accessors can be added to a whitelist. Access requests to access-restricted URIs by an accessor not in the whitelist can be denied and an alert can be generated. Access requests to access-restricted URIs by an accessor in the whitelist can be granted.

Abstract: A method includes obtaining a dictionary, data for a set of web requests, and definitions of a first set of clusters associated with vulnerability scanners. The method includes identifying a set of clients that transmitted the second set of web requests. The method includes generating a second set of feature vectors, which each corresponds to one of the clients. Each element in each feature vector corresponds respectively to an entry in the dictionary. The method includes clustering the second set of feature vectors into a second set of clusters. The method includes, in response to a first distance between a selected cluster of the second set of clusters and one of the first set of clusters being less than a first predetermined distance, (i) identifying one of the set of web services that received web requests corresponding to feature vectors in the selected cluster and (ii) generating a scanning alert.

Abstract: Systems, methods, and apparatuses are provided for restricting access to a web resource. Website access information is obtained by monitoring accesses to a plurality of websites for each access, which may include a network identifier of an access requestor, a website identifier, and an access time for each request. Based on at least the website access information, it may be determined that a particular access requestor has accessed a number of different websites in a given time period. As a result, the particular access requestor may be classified as a web robot. A request to permit access to a web resource is received by the particular access requestor. In response to receiving the request to permit access to the web resource, the particular access requestor is prevented from accessing the web resource and/or a notification is generated that the particular access requestor is attempting to access the web resource.

Abstract: A previously-unknown type of attack on a web application can be detected dynamically using server logs. An alert can be raised for an application that returns a valid response to the potential attacker (e.g., when an http (hypertext transfer protocol) status code of 200 is returned to the requestor). Server logs can be analyzed to identify an external computer that uses the same attack methodology on multiple targets. The external computer may attempt to access the same Uniform Resource Identifier (URI) on various web sites. In many cases, the http status code that is returned is an error code. Characteristics such as but not limited to fast crawling and numerous error status codes being returned to a particular requestor can be used by a machine learning (ML) system to identify potentially malicious external computing devices and/or vulnerable URIs.

Abstract: A system is provided for detecting creation of malicious user accounts. The system includes a processor, a memory, and an application including instructions configured to: collect data corresponding to creation of new user accounts, where the new user accounts are associated with at least two distinct organizations, at least two distinct subscriptions, or at least two distinct customers, and where each of the new user accounts has a user name; determine properties based on the data and for a group of similar ones of the user names; evaluate the properties of the new user accounts corresponding to the group of similar ones of the user names and determine whether a probability for the new user accounts to be created having the group of similar ones of the user names is less than a predetermined threshold, and generate an alert based on a result of the evaluation of the properties.

Abstract: A system is provided for detecting creation of malicious user accounts. The system includes a processor, a memory, and an application including instructions configured to: collect data corresponding to creation of new user accounts, where the new user accounts are associated with at least two distinct organizations, at least two distinct subscriptions, or at least two distinct customers, and where each of the new user accounts has a user name; determine properties based on the data and for a group of similar ones of the user names; evaluate the properties of the new user accounts corresponding to the group of similar ones of the user names and determine whether a probability for the new user accounts to be created having the group of similar ones of the user names is less than a predetermined threshold, and generate an alert based on a result of the evaluation of the properties.