Abstract: There is disclosed in one example a computing apparatus, including: a processor and a memory; a data store having stored thereon trained models MGLOBAL and MENT, wherein model MGLOBAL includes a clustering model of proximity and prevalence of a first body of computing objects, and MENT includes a clustering model of proximity and prevalence of a second body of computing object; and instructions encoded within the memory to instruct the processor to: receive an object under analysis; apply a machine learning model to compute a global variance score between the object under analysis and MGLOBAL; apply the machine learning model to compute an enterprise variance score between the object under analysis and MENT; compute from the global variance score and the enterprise variance score a cross-sectional variance score; and assign the object under analysis an analysis priority according to the cross-sectional variance score.

Abstract: There is disclosed in one example a computer-implemented method of detecting a statistically-significant security event and automating a response thereto, including: querying, or causing to be queried, a security intelligence database for sector-wise historical norms for an indicator of compromise (IoC); obtaining sector-wise expected prevalence data for the IoC; receiving observed sector-wise prevalence data for the IoC; computing a first test statistic from a goodness-of-fit test between the observed and expected prevalences; from the observed sector-wise prevalence data, computing a second test statistic from a difference between a highest prevalence and a next-highest prevalence; computing a third test statistic from a difference between the observed prevalence of a highest prevalence sector and the expected prevalence for the highest prevalence sector; selecting a least significant statistic from among the first, second, and third test statistics; and determining from the least significant statistic whet

Abstract: A method including receiving a feature vector of an unknown sample, computing a MinHash of the unknown sample based on Jaccard-compatible features, querying a Locality Sensitive Hashing forest of known samples with the MinHash of the unknown sample to identify a first subset of known samples that are similar to the unknown sample, receiving for each individual known sample in the first subset, a feature vector including non-Jaccard distance-compatible features, computing a first sub-distance and a second sub-distance between the unknown sample and the known samples in the first subset, calculating a total distance for each known sample in the first subset by combining the first and the second sub-distances, identifying, based on the calculated total distances, a second subset of known samples that are most similar to the unknown sample, and classifying the unknown sample based on the second subset.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform, including a processor, a memory, and a network interface; a bucketized reputation modifier table; and instructions encoded within the memory to instruct the processor to: perform a feature-based malware analysis of an object; assign the object a malware reputation according to the feature-based malware analysis; query and receive via the network interface a complementary score for a complementary property of the object; query the bucketized reputation modifier table according to the complementary score to receive a reputation modifier for the object; adjust the object's reputation according to the reputation modifier; and take a security action according to the adjusted reputation.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform, including a processor, a memory, and a network interface; a bucketized reputation modifier table; and instructions encoded within the memory to instruct the processor to: perform a feature-based malware analysis of an object; assign the object a malware reputation according to the feature-based malware analysis; query and receive via the network interface a complementary score for a complementary property of the object; query the bucketized reputation modifier table according to the complementary score to receive a reputation modifier for the object; adjust the object's reputation according to the reputation modifier; and take a security action according to the adjusted reputation.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes at least one memory, instructions; and processor circuitry to execute the instructions to train a neural network with a plurality of raw byte data samples, perform feature extraction on ones of the plurality of raw byte data samples, determine whether ones of the plurality of raw byte data samples are clean or malicious using the extracted features, and determine a family of malware to which an identified malicious sample belongs.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes at least one memory, instructions, and processor circuitry to execute the instructions. The processor circuitry executes the instructions to identify a test data distribution, generate a first visualization of the identified test data distribution, select a visualization type for a machine learning model, generate a second visualization including an indication of features extracted from the test data by the machine learning model, and generate a third visualization of results of inference performed by the machine learning model, the inference performed on the test data.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform including a processor circuit and a memory circuit; first means for accessing a machine learning engine; second means for accessing a user interface; and instructions encoded within the memory to instruct the processor to: load into the machine learning engine via the first means an object prevalence model, including an enterprise-specific prevalence model; provide to the machine learning engine an object set from the enterprise; identify an enterprise-novel object from the object set; solicit and receive via the second means user-sourced feedback for the enterprise-novel object; and act according to the user-sourced feedback.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform, including a processor, a memory, and a network interface; a bucketized reputation modifier table; and instructions encoded within the memory to instruct the processor to: perform a feature-based malware analysis of an object; assign the object a malware reputation according to the feature-based malware analysis; query and receive via the network interface a complementary score for a complementary property of the object; query the bucketized reputation modifier table according to the complementary score to receive a reputation modifier for the object; adjust the object's reputation according to the reputation modifier; and take a security action according to the adjusted reputation.

Abstract: There is disclosed in one example a computer-implemented method of detecting a statistically-significant security event and automating a response thereto, including: querying, or causing to be queried, a security intelligence database for sector-wise historical norms for an indicator of compromise (IoC); obtaining sector-wise expected prevalence data for the IoC; receiving observed sector-wise prevalence data for the IoC; computing a first test statistic from a goodness-of-fit test between the observed and expected prevalences; from the observed sector-wise prevalence data, computing a second test statistic from a difference between a highest prevalence and a next-highest prevalence; computing a third test statistic from a difference between the observed prevalence of a highest prevalence sector and the expected prevalence for the highest prevalence sector; selecting a least significant statistic from among the first, second, and third test statistics; and determining from the least significant statistic whet

Abstract: A method including receiving a feature vector of an unknown sample, computing a MinHash of the unknown sample based on Jaccard-compatible features, querying a Locality Sensitive Hashing forest of known samples with the MinHash of the unknown sample to identify a first subset of known samples that are similar to the unknown sample, receiving for each individual known sample in the first subset, a feature vector including non-Jaccard distance-compatible features, computing a first sub-distance and a second sub-distance between the unknown sample and the known samples in the first subset, calculating a total distance for each known sample in the first subset by combining the first and the second sub-distances, identifying, based on the calculated total distances, a second subset of known samples that are most similar to the unknown sample, and classifying the unknown sample based on the second subset.

Abstract: There is disclosed in one example a computing apparatus, including: a processor and a memory; a data store having stored thereon trained models MGLOBAL and MENT, wherein model MGLOBAL includes a clustering model of proximity and prevalence of a first body of computing objects, and MENT includes a clustering model of proximity and prevalence of a second body of computing object; and instructions encoded within the memory to instruct the processor to: receive an object under analysis; apply a machine learning model to compute a global variance score between the object under analysis and MGLOBAL; apply the machine learning model to compute an enterprise variance score between the object under analysis and MENT; compute from the global variance score and the enterprise variance score a cross-sectional variance score; and assign the object under analysis an analysis priority according to the cross-sectional variance score.

Abstract: In an example, there is a disclosed a computing apparatus, including: a psychological state data interface to receive psychological state data; one or more logic elements, including at least one hardware element, including a verification engine to: receive a requested user action; receive a psychological state input via the psychological state data interface; analyze the psychological state input; and bar the requested user action at least partly responsive to the analyzing.

Abstract: There is disclosed in one example a social media server, including: a processor; a trusted input/output (IO) interface to communicatively couple to a consumer device; a network interface to communicatively couple to an enterprise; and a memory having stored thereon executable instructions to instruct the processor to provide a data loss prevention (DLP) engine to: receive via the trusted IO interface a signed and encrypted user posting for the social media service, the user posting including a signed user state report verifying that the user has passed a biometric screening; transmit content of the user posting to the enterprise via the network interface for DLP analysis; receive from the enterprise a notification that the user posting has passed DLP analysis; and accept the user posting.

Abstract: There is disclosed in one example a data loss prevention (DLP) server, including: a processor; a trusted input/output (IO) interface to communicatively couple to a user device; a social media interface to communicatively couple to a social media service; a trusted execution environment (TEE); and a memory having stored thereon executable instructions to instruct the processor to provide a DLP engine to: receive via the trusted IO interface a signed and encrypted user posting for the social media service, the user posting including a signed user state report verifying that the user has passed a biometric screening; and submit the user posting on behalf of the user to the social media service via the social media interface.

Abstract: There is disclosed in one example a social media server, including: a processor; a trusted input/output (IO) interface to communicatively couple to a consumer device; a network interface to communicatively couple to an enterprise; and a memory having stored thereon executable instructions to instruct the processor to provide a data loss prevention (DLP) engine to: receive via the trusted IO interface a signed and encrypted user posting for the social media service, the user posting including a signed user state report verifying that the user has passed a biometric screening; transmit content of the user posting to the enterprise via the network interface for DLP analysis; receive from the enterprise a notification that the user posting has passed DLP analysis; and accept the user posting.

Abstract: In an example, there is disclosed a computing apparatus, comprising: a psychological state data interface to receive psychological state data; one or more logic elements, including at least one hardware element, comprising a verification engine to: receive a requested user action; receive a psychological state input via the psychological state data interface; analyze the psychological state input; and bar the requested user action at least partly responsive to the analyzing.

Abstract: Technologies are provided in embodiments for receiving policy information associated with at least one security exception, the security exception relating to execution of at least one program, determining an operation associated with the security exception based, at least in part, on the policy information, and causing the operation to be performed, based at least in part, on a determination that the at least one security exception occurred.

Abstract: In an example, there is disclosed a computing apparatus, comprising: a psychological state data interface to receive psychological state data; one or more logic elements, including at least one hardware element, comprising a verification engine to: receive a requested user action; receive a psychological state input via the psychological state data interface; analyze the psychological state input; and bar the requested user action at least partly responsive to the analyzing.

Abstract: Technologies are provided in embodiments for receiving policy information associated with at least one security exception, the security exception relating to execution of at least one program, determining an operation associated with the security exception based, at least in part, on the policy information, and causing the operation to be performed, based at least in part, on a determination that the at least one security exception occurred.