Abstract: Distinguishing between functional tracking domains and nonfunctional tracking domains on a host web page. In particular, a list of known tracking domains that load content into host web pages may be received. This list of tracking domains may include tracking domains that are functional and tracking domains that are nonfunctional. The tracking domains that are functional may be determined by evaluating various behaviors and characteristics of the tracking domains. Once functional tracking domains have been determined, these functional tracking domains may be allowed, and other tracking domains may be blocked from loading content onto host web pages thereby preserving the functionality of the web pages.

Abstract: Assigning privacy ratings to tracking domains in order to increase user awareness of online privacy violations. A list of known tracking domains and raw data from a set of host websites may be received. A prevalence, frequency, and role for each tracking domain may be determined from the raw data. Each tracking domain may be assigned to a prevalence cluster, a frequency cluster, and a role cluster. A rank may be determined for each prevalence cluster, frequency cluster, and role cluster. A domain privacy rating may be assigned to each tracking domain that is based on the ranks of the clusters to which each tracking domain is assigned. A privacy action may be performed based on the domain privacy rating assigned to each tracking domain.

Abstract: The disclosed computer-implemented method for utilizing user profile data to protect against phishing attacks may include (i) detecting a target user profile associated services accessed by a network-based application, (ii) determining identifiers associated with each of the services, (iii) extracting, for each of the identifiers, feature vectors describing exploitable screen elements in the network-based application associated with phishing attacks, (iv) updating, based on the feature vectors, previously extracted feature vectors in a data repository storing additional profiles for other users associated with the services, (v) predicting, utilizing a machine-learning model, phishing attack threats for target profile user based on a similarity with the additional profiles, and (vi) performing a security action that protects against the phishing attack threats. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems and methods for identifying and removing a tracking capability from an external domain that performs a tracking activity on a host web page. Tracking capabilities of an external domain may be removed by altering web requests and/or responses to API calls. Once these tracking capabilities of the external domain have been removed, the altered web requests and/or altered responses to API calls may be transmitted to a web browser and/or entity making the API call thereby protecting user privacy while allowing the external domain to interact with the host web page.

Abstract: Mitigating website privacy issues by automatically identifying cookie sharing risks in a cookie ecosystem. In some embodiments, a method may include generating, by the computing device, a cookie tree representing cookies created on a web browser while browsing a website. The cookie tree may include nodes representing organizations. The cookie tree may further include hierarchical edges between the nodes representing creation chains of the nodes. The method may also include determining, by the computing device, a creator, a sender, and a receiver of each of the cookies. The method may further include mitigating, by the computing device, website privacy issues based on the creator, sender, and receiver of each of the cookies.

Abstract: Identifying and removing a tracking capability from an external domain that performs a tracking activity on a host web page. Tracking capabilities of an external domain may be removed by altering web requests and/or responses to API calls. Once these tracking capabilities of the external domain have been removed, the altered web requests and/or altered responses to API calls may be transmitted to a web browser and/or entity making the API call thereby protecting user privacy while allowing the external domain to interact with the host web page.

Abstract: Analyzing and mitigating website privacy issues by automatically classifying cookies.

Abstract: Analyzing and mitigating privacy issues on a computing device using cookie generation flows. The method includes initiating a headless web browser, monitoring a request made of a website accessed by the headless web browser, monitoring scripts created on the website, instrumenting a function used to create a cookie on the computing device, tracing an initial generation of a call used to create the cookie on the computing device, obtaining a cookie generation flow related to the creation of the cookie, and initiating a security action based on obtaining the cookie generation flow.

Abstract: Building a ground truth dataset for a machine learning-based security application. In one embodiment, a method may include identifying a set of network devices to add to a ground truth dataset. The method may also include, for each network device in the set of network devices, identifying a potentially malicious application stored on the network device, analyzing behavior of the potentially malicious application to determine whether the potentially malicious application has behaved maliciously, and if so, adding the network device to the ground truth dataset as an infected device or, if not, adding the network device to the ground truth dataset as a clean device. The method may further include training a machine learning classifier of a security application using the ground truth dataset, making a security action decision using the machine learning classifier, and performing a security action on a computer system based on the security action decision.

Abstract: Securing network devices by forecasting future security incidents for a network based on past security incidents. In one embodiment, a method may include constructing past inside-in security features for a network, constructing past outside-in security features for the network, and employing dynamic time warping to generate a similarity score for each security feature pair in the past inside-in security features, in the past outside-in security features, and between the past inside-in security features and the past outside-in security features. The method may further include generating a Coupled Gaussian Latent Variable (CGLV) model based on the similarity scores, forecasting future inside-in security features for the network using the CGLV model, and performing a security action on one or more network devices of the network based on the forecasted future inside-in security features for the network.

Abstract: The disclosed computer-implemented method for mapping services utilized by network domains may include (i) receiving a request to perform a risk assessment on a domain, (ii) querying a database for records associated with the domain, where each record links to a network resource that enables functionality of the domain, (iii) generating a service map that matches each network resource to a corresponding service type and service provider, (v) performing the risk assessment of the domain, and (vi) facilitating a security measure for the domain based on a result of the risk assessment. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for evaluating cybersecurity risk may include (i) identifying telemetry data collected from endpoints of an entity, (ii) calculating a cybersecurity risk score for the entity by searching the telemetry data for information indicative of cybersecurity risk exposure of the entity and performing an actuarial analysis on the information indicative of the cybersecurity risk exposure to quantize a potential consequence of the cybersecurity risk exposure, and (iii) performing, based on the cybersecurity risk score, a security action to protect the entity from the potential consequence of the cybersecurity risk exposure. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for evaluating infection risks based on profiled user behaviors may include (1) collecting user-behavior profiles that may include labeled profiles (e.g., infected profiles and/or clean profiles) and/or unlabeled profiles, (2) training a classification model to distinguish infected profiles from clean profiles using features and labels of the user-behavior profiles, and (3) using the classification model to predict (a) a likelihood that a computing system of a user will become infected based on a profile of user behaviors of the user and/or (b) a likelihood that a user behavior in the user-behavior profiles will result in a computing-system infection. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for identifying non-malicious files on computing devices within organizations may include (1) identifying a file on at least one computing device within multiple computing devices managed by an organization, (2) identifying a source of the file based on examining a relationship between the file and the organization, (3) determining that the source of the file is trusted within the organization, and then (4) concluding, based on the source of the file being trusted within the organization, that the file is not malicious. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for evaluating infection risks based on profiled user behaviors may include (1) collecting user-behavior profiles that may include labeled profiles (e.g., infected profiles and/or clean profiles) and/or unlabeled profiles, (2) training a classification model to distinguish infected profiles from clean profiles using features and labels of the user-behavior profiles, and (3) using the classification model to predict (a) a likelihood that a computing system of a user will become infected based on a profile of user behaviors of the user and/or (b) a likelihood that a user behavior in the user-behavior profiles will result in a computing-system infection. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for preventing targeted malware attacks may include (1) identifying at least one candidate risk factor for targets of previous targeted malware attacks that were directed to the targets based on characteristics of the targets, (2) calculating a degree of association between the candidate risk factor and the previous targeted malware attacks by comparing rates of targeted malware attacks between a group that possesses the risk factor and a group that does not possess the risk factor, (3) identifying a candidate target of a targeted malware attack that possesses the candidate risk factor, and (4) adjusting a security policy assigned to the candidate target of the targeted malware attack based on the calculated degree of association between the candidate risk factor and the previous targeted malware attacks. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for determining the trustworthiness of files within organizations may include (1) identifying a file on a computing device within multiple computing devices managed by an organization, (2) in response to identifying the file, identifying at least one additional computing device within the multiple computing devices that is potentially associated with the file, (3) distributing at least a portion of the file to a user of the additional computing device with a request to receive an indication of the trustworthiness of the file, and then (4) receiving, from the additional computing device, a response that indicates the trustworthiness of the file. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques are disclosed for dynamically managing hardening policies in a client computer (e.g., of an enterprise network). A hardening management application monitors activity on the client computer that is associated with a first hardening policy. The monitored activity is evaluated based on one or more metrics. Upon determining that at least one of the metrics is outside of a tolerance specified in the first hardening policy, the client computer is associated with a second hardening policy. The client computer is reconfigured based on the second hardening policy.

Abstract: The disclosed computer-implemented method for determining malicious-download risk based on user behavior may include (1) identifying a set of users that are at high risk for malicious downloads and a set of users that are at low risk for malicious downloads, (2) determining a high-risk pattern of download behavior that is shared by the set of high-risk users and that is not shared by the set of low-risk users, (3) analyzing download behavior of an uncategorized user over a predefined time period in order to categorize the download behavior as high-risk or low-risk, and (4) categorizing the uncategorized user as a high-risk user in response to determining that the download behavior of the uncategorized user falls within a predefined similarity threshold of the high-risk pattern of download behavior. Various other methods, systems, and computer-readable media are also disclosed. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for dynamic access control over shared resources may include (1) detecting an attempt by a user to access a resource via a computing environment, (2) identifying a risk level of the user attempting to access the resource, (3) identifying a sensitivity level of the resource, (4) identifying a risk level of the computing environment through which the user is attempting to access the resource, (5) determining an overall risk level for the attempt to access the resource based at least in part on (A) the risk level of the user, (B) the sensitivity level of the resource, and (C) the risk level of the computing environment, and then (6) determining, based at least in part on the overall risk level, whether to grant the user access to the resource via the computing environment. Various other methods, systems, and computer-readable media are also disclosed.