Abstract: An application of a hydrogel in the preparation of products for the treatment of bacterial infection is disclosed. Ferrous ion was used in the antibacterial activity test of Staphylococcus aureus and showed a strong bactericidal effect, which the survival rate of Staphylococcus aureus is less than 0.001%, as well as the survival rate of methicillin-resistant Staphylococcus aureus (MRSA) was less than 0.01%. Ferrous ion hydrogel was used for the treatment of keratitis and skin wound infection. Ferrous ion hydrogel for the treatment of keratitis and skin wound infection, which can significantly reduce the risk of pulmonary MRSA infection, prevented the dissimilation of MRSA to the lung, and alleviated systemic inflammation in the body in a timely and effective manner, revealing the therapeutic potential of ferrous compounds for treatment of Staphylococcus aureus infections, including MRSA.

Abstract: A feature selection methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are well defined, a classification of the features is performed. Provided the feature selection analysis determines that features of the components are not well defined, a similarity analysis of the features is performed. Results of the feature selection methodology are generated.

Abstract: A method of generating relevant security rules for a user includes the steps of: building a first tree data structure from paths within a pool of security rules; collecting process paths for the user; and compiling the relevant security rules for the user by traversing the first tree data structure according to the process paths of the user.

Abstract: The disclosure herein describes automatically performing security operations associated with a client system based on aggregated event impact scores of computing events during a rolling time interval. Event data is obtained, wherein the event data is from a plurality of computing devices of the client system associated with computing events occurring during a time interval after an endpoint of the rolling time interval. Event impact scores are calculated for the computing events of the obtained event data over the time interval based at least on cardinality estimation. The calculated event impact scores are merged into the set of aggregated event impact scores associated with the rolling time interval and event impact scores associated with an expired time interval are removed from the set of aggregated event impact scores. Based on the set of aggregated event impact scores, at least one security operation is performed for at least one computing event.

Abstract: A feature selection methodology is disclosed. In a computer-implemented method, the feature selection methodology automatically monitors components of a computing environment. The feature selection methodology then determines the importance of various components of the computing environment. The feature selection methodology further outputs results of the determining of the importance of the components within the computing device.

Abstract: The disclosure provides an approach for detecting and preventing attacks in a network. Embodiments include determining a plurality of network behaviors of a process by monitoring the process. Embodiments include generating a plurality of intended states for the process based on subsets of the plurality of network behaviors. Embodiments include determining a plurality of intended state clusters by applying a clustering technique to the plurality of intended states. Embodiments include determining a state of the process. Embodiments include identifying a given cluster of the plurality of intended state clusters that corresponds to the state of the process. Embodiments include selecting a novelty detection technique based on a size of the given cluster. Embodiments include using the novelty detection technique to determine, based on the given cluster and the state of the process, whether to generate a security alert for the process.

Abstract: The disclosure herein describes automatically performing security operations associated with a client system based on aggregated event impact scores of computing events during a rolling time interval. Event data is obtained, wherein the event data is from a plurality of computing devices of the client system associated with computing events occurring during a time interval after an endpoint of the rolling time interval. Event impact scores are calculated for the computing events of the obtained event data over the time interval based at least on cardinality estimation. The calculated event impact scores are merged into the set of aggregated event impact scores associated with the rolling time interval and event impact scores associated with an expired time interval are removed from the set of aggregated event impact scores. Based on the set of aggregated event impact scores, at least one security operation is performed for at least one computing event.

Abstract: A request to replicate a first account maintained by a data platform is received. Based on the request, account data associated with the account is accessed. The account data comprises security configurations for the first account. In response to the request, the first account is replicated using the account data. A second account results from replicating the first account. The replicating of the first account comprises automatically replicating the security configurations for the first account to the second account. The replicating of the security configurations comprises replicating an identity management configuration of the first account; replicating an authorization configuration of the first account; and replicating an authentication configuration of the first account.

Abstract: Machine learning-based techniques for representing computing processes as vectors are provided. In one set of embodiments, a computer system can receive a name of a computing process and context information pertaining to the computing process. The computer system can further train a neural network based on the name and the context information, where the training results in determination of weight values for one or more hidden layers of the neural network. The computer system can then generate, based on the weight values, a vector representation of the computing process that encodes the context information and can perform one or more analyses using the vector representation.

Abstract: A request to replicate a first account maintained by a data platform is received. Based on the request, account data associated with the account is accessed. The account data comprises security configurations for the first account. In response to the request, the first account is replicated using the account data. A second account results from replicating the first account. The replicating of the first account comprises automatically replicating the security configurations for the first account to the second account. The replicating of the security configurations comprises replicating an identity management configuration of the first account; replicating an authorization configuration of the first account; and replicating an authentication configuration of the first account.

Abstract: A computer-implemented method for determining whether data is anomalous includes generating a holo-entropy adaptive boosting model using, at least in part, a set of normal data. The holo-entropy adaptive boosting model includes a plurality of holo-entropy models and associated model weights for combining outputs of the plurality of holo-entropy models. The method further includes receiving additional data, and determining at least one of whether the additional data is normal or abnormal relative to the set of normal data or a score indicative of how abnormal the additional data is using, at least in part, the generated holo-entropy adaptive boosting model.

Abstract: A request to replicate a first account maintained by a data platform is received. Based on the request, account data associated with the account is accessed. The account data comprises security configurations for the first account. In response to the request, the first account is replicated using the account data. A second account results from replicating the first account. The replicating of the first account comprises automatically replicating the security configurations for the first account to the second account. The replicating of the security configurations comprises replicating an identity management configuration of the first account; replicating an authorization configuration of the first account; and replicating an authentication configuration of the first account.

Abstract: The disclosure herein describes automatically performing security operations associated with a client system based on aggregated event impact scores of computing events during a rolling time interval. Event data is obtained, wherein the event data is from a plurality of computing devices of the client system associated with computing events occurring during a time interval after an endpoint of the rolling time interval. Event impact scores are calculated for the computing events of the obtained event data over the time interval based at least on cardinality estimation. The calculated event impact scores are merged into the set of aggregated event impact scores associated with the rolling time interval and event impact scores associated with an expired time interval are removed from the set of aggregated event impact scores. Based on the set of aggregated event impact scores, at least one security operation is performed for at least one computing event.

Abstract: A method of generating relevant security rules for a user includes the steps of: building a first tree data structure from paths within a pool of security rules; collecting process paths for the user; and compiling the relevant security rules for the user by traversing the first tree data structure according to the process paths of the user.

Abstract: A request to replicate a first account maintained by a data platform is received. Based on the request, account data associated with the account is accessed. The account data comprises security configurations for the first account. In response to the request, the first account is replicated using the account data. A second account results from replicating the first account. The replicating of the first account comprises automatically replicating the security configurations for the first account to the second account. The replicating of the security configurations comprises replicating an identity management configuration of the first account; replicating an authorization configuration of the first account; and replicating an authentication configuration of the first account.

Abstract: Certain aspects herein provide a system and method for performing behavior analysis for a computing device by a computing system. In certain aspects, a method includes detecting an event occurring at the computing device at a first time, determining, based on the detecting, an event category of the event, and collecting first one or more behaviors associated with the determined event category occurring on the computing device based. The method also includes comparing the first one or more behaviors with a dataset indicating one or more expected behaviors of the computing device associated with the event. Upon determining that at least one of the first one or more behaviors corresponds to an unexpected behavior based on the comparing, the method further includes taking one or more remedial actions.

Abstract: A method for managing alarms in a virtual machine environment includes receiving alarm data related to a process and storing the alarm data in a database, where the alarm data comprises one or more features. The method further includes retrieving intended state information for the process and comparing the one more features of the alarm data to the intended state information to determine whether the alarm is an outlier. The method also includes computing a normal score for the alarm if the alarm is not an outlier, and computing an abnormal score for the alarm if the alarm is an outlier. The method also includes sending a notification for the alarm and the computed score.

Abstract: An example method of representing a selected entity in a plurality of entities in a computing system includes: obtaining a graph representation of the plurality of entities, the graph representation having nodes and edges representing a hierarchy of the plurality of entities; extracting a set of paths from the graph representation, each path in the set of paths including a series of edge-connected nodes in the graph representation; processing the set of paths to generate a vector representation of the selected entity, the vector representation having a plurality of elements representing a context of the selected entity within the graph representation; and providing the vector representation as input to an application executing in the computing system.

Abstract: The disclosure provides an approach for detecting and preventing attacks in a network. Embodiments include determining a plurality of network behaviors of a process by monitoring the process. Embodiments include generating a plurality of intended states for the process based on subsets of the plurality of network behaviors. Embodiments include determining a plurality of intended state clusters by applying a clustering technique to the plurality of intended states. Embodiments include determining a state of the process. Embodiments include identifying a given cluster of the plurality of intended state clusters that corresponds to the state of the process. Embodiments include selecting a novelty detection technique based on a size of the given cluster. Embodiments include using the novelty detection technique to determine, based on the given cluster and the state of the process, whether to generate a security alert for the process.

Abstract: Feature vectors are abstracted from data describing application processes. The feature vectors are grouped to define non-anomalous clusters of feature vectors corresponding to normal application behavior. Subsequent feature vectors are considered anomalous if they do not fall within one of the non-anomalous clusters; alerts are issued for anomalous feature vectors. In addition, the subsequent feature vectors may be used to regroup feature vectors to adapt to changes in what constitutes normal application behavior.