Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform including a processor and a memory; a network interface to communicatively couple to a network; and a network gateway engine to identify devices on the network, the network gateway engine including instructions encoded within the memory to instruct the processor to provide two-phase identification for a device newly-identified on the network, including: a static identification phase including applying discovery probes to the newly-identified device; and a dynamic identification phase including collecting network telemetry for the newly-identified device over time and analyzing the collected network telemetry to determine if the network telemetry is consistent with expected network usage for the newly-discovered device.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform including a processor and a memory; an operating system including a priority architecture; a multi-process web browser; and a browser optimizer agent including instructions encoded within the memory to instruct the processor to: inspect a process of the web browser; determine from the inspection that resource utilization for the process can be improved, and adjust resource priority via the operating system to improve resource utilization for the process.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform including a processor and a memory; a user application; telemetry probes to collect telemetry about use of the user space application; a detection proxy to collect telemetry data from the telemetry probes and forward the telemetry data to a detection cloud service; and logic to receive from the detection cloud service a detection message that the user application has exhibited behavior consistent with tampering, and to take remedial action responsive to the detection message.

Abstract: Disclosed herein are cloud-based machine learning systems and methods for monitoring networked devices to identify and classify characteristics, to infer typical or atypical behavior and assign reputation profiles across various networked devices, and to make remediation recommendations. In some embodiments, a cloud-based machine learning system may learn the typical operation and interfacing of a plurality of reputable devices that are known to be free from malicious software and other threats. In some embodiments, a cloud-based machine learning system may learn the typical operation and interfacing of a device, and may identify atypical operations or interfaces associated with that device by comparing the operations and interfaces to those of a plurality of networked devices or to those of a defined standard reference device.

Abstract: Example methods, apparatus, systems and articles of manufacture to implement cooperative mitigation of distributed denial of service attacks originating in local networks are disclosed. An example local network router disclosed herein includes a mitigator to mitigate a distributed denial of service attack detected by an Internet service provider, the distributed denial of service attack associated with network traffic originating from a first device connected to a local network. The example local network router also includes a threat signaling server to identify the first device based on first information received from a threat signaling client of the Internet service provider, the first information describing the distributed denial of service attack. The example threat signaling server is also to transmit second information to notify the threat signaling client of the Internet service provider when the network traffic associated with the distributed denial of service attack has been mitigated.

Abstract: Methods apparatus, systems, and articles of manufacture for IoT and PoS anti-malware are disclosed. An example method includes detecting a combination of function calls. Whether the combination of function calls is a forbidden combination of function calls for the device is detected based on a limited intended functionality of the device. The forbidden combination of function calls includes a first function call and a second function call. The first function call is allowed in isolation from the second function call. The second function call is allowed in isolation from the first function call. In response to determining that the combination of function calls is forbidden for the device, a responsive action is performed.

Abstract: Techniques related to flexible record definitions for semi-structured data in a relational database system are disclosed. The techniques include a machine readable medium, on which are stored instructions for accessing flexible record definitions for efficient database storage, comprising instructions that when executed cause a device to: receive a first request to create a first row in a database table, the first request indicating a first set of columns associated with the first row, determine that columns of the first set of columns are included in a second set of columns associated with the database table, select a first variant, from a set of variants, the first variant associated with a third set of columns, the third set of columns including at least the first set of columns, and write the first row to the database table based on the first variant and the third set of columns.

Abstract: Particular embodiments described herein provide for a system that can be configured to receive a notification that a client device is requesting, to modify original data associated with an online application, wherein the original data is stored in encrypted format in a cloud; decrypt the original data using a first client encryption key; store the decrypted data in a location accessible by the online application; enable editing capability of the decrypted data; receive a notification that the client device is finished modifying the data in decrypted format; determine whether the original data in decrypted format was modified; encrypt, based on a determination that the original data was modified, the modified data using a second client encryption key; and upload the modified data in encrypted format to the cloud.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform including a processor and a memory; a system profile store; and a ransomware detection engine including instructions encoded within the memory to instruct the processor to: detect an operation, by a process, that results in an operation on a file, wherein the operation includes newly creating the file including a file type identifier, or where the file is an existing file, changing a file type identifier for the file; querying the system profile store with a combination of the file type identifier and metadata about the file; based at least in part on the querying, determining that the process is a suspected ransomware attack; and taking a remedial action.

Abstract: According to some embodiments, so-called “NS-Tree” indexes may be used to calculate whether all (or a portion) of two independent tables are consistent. The NS-Tree indexes may be comprised of various elements, e.g., a synchronization time, a primary key, a hash of the complete record itself, and an aggregate value associated with each index entry (e.g., an accumulated XOR value). At any point in the index, an entry may possess the accumulated aggregate value of all key entries in the table up to that point. That aggregate value may be used to validate data consistency with another table(s) maintaining the same index. Due to the unique composition of the NS-Tree index, users can also validate two data sets within a ‘sub-range’ of the entire data set. According to other embodiments, NS-Tree indexes may also be applied to two different clusters of nodes by applying the NS-Trees at a ‘federated’ level.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform including a processor and a memory; and instructions encoded within the memory to instruct the processor to: provide a permission list; allocate an executable, the executable to have permissions according to the permission list; designate a child object of the executable; allocate a certificate for the child object; and after a system reboot, grant the child object permissions of the executable after validating the certificate.

Abstract: There is disclosed in one example a computing apparatus, including: a hardware platform including a processor and a memory; a network interface; and a phishing mitigation engine including instructions encoded within the memory to: receive via the network request a validation request from a mobile computing device, the validation request including an e-mail payload; query a cloud phishing reputation service for a reputation, the query including information from the e-mail payload; receive from the cloud phishing reputation service reputation data for the e-mail payload; and provide a push notification to the mobile computing device, the push notification including a reputation notice for the e-mail payload.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to identifying a digital certificate associated with data and assigning a reputation to the digital certificate, where the digital certificate is classified as trusted if the digital certificate is included in an entry in a whitelist and the digital certificate is classified as untrusted if the digital certificate is included in an entry in a blacklist.

Abstract: An attempted transaction is identified involving a customer device and the first customer device is redirected to a security broker. A security report for the first customer device is received from the security broker. The security report is based on security data transmitted from the customer device to the security broker. An action can be performed in association with the attempted transaction based at least in part on the received security report. In some aspects, the security broker receives security data describing security conditions on the customer device in connection with the transaction between the customer device and a transaction partner. A risk tolerance policy is identified that corresponds to the transaction partner, such as an ecommerce provider. A security report is generated based on a comparison of the risk tolerance policy and the security data and the security report.

Abstract: A method in an embodiment includes detecting a change for a virtual machine in a virtual server of a virtual network infrastructure, determining whether a virtual security appliance is configured in the virtual server, and sending a request to create the virtual security appliance in the virtual server. The method further includes allowing the virtual machine to initiate when the virtual security appliance is created in the virtual machine. The virtual security appliance performs security inspections on network packets sent from the virtual machine. In more specific embodiments, the method further includes creating an intercept mechanism in the virtual server to intercept the network packets from the virtual machine. In further embodiments, one or more security policies identify one or more virtual security appliances to process the network packets from the virtual machine.

Abstract: The present disclosure relates to a system and method for providing a service on a wearable device where the wearable device is limited in its functionality in some way when compared with a companion device. In particular, the disclosure describes use cases for configuring the wearable device, and use cases for configuring a wearable device and performing service application functions on the wearable device while leveraging a companion device.

Abstract: Methods, apparatus, systems and articles of manufacture to classify a first file are disclosed herein. Example apparatus include a feature hash generator to generate respective sets of one or more feature hashes for respective features of the first file. The number of the one or more feature hashes to be generated is based on an ability of the feature to distinguish the first file from a second file. The apparatus also includes a bit setter to set respective bits of a first fuzzy hash value based on respective ones of the one or more feature hashes, a classifier to assign the first file to a class associated with a second file based on a similarity between the first fuzzy hash value and a second fuzzy hash value for a second file.

Abstract: In an example, a threat intelligence controller is configured to operate on a data exchange layer (DXL). The threat intelligence controller acts as a DXL consumer of reputation data for a network object, which may be reported in various different types and from various different sources. Of the devices authorized to act as reputation data producers, each may have its own trust level. As the threat intelligence controller aggregates data from various providers, it may weight the reputation reports according to trust level. The threat intelligence engine thus builds a composite reputation for the object. When it receives a DXL message requesting a reputation for the object, it publishes the composite reputation on the DXL bus.

Abstract: There is disclosed in one example a computing apparatus, including: a processor and a memory; a network interface; and instructions encoded within the memory to instruct the processor to: receive a uniform resource locator (URL) for analysis, the URL to access a web page via a remote server; via the network interface, retrieve from the remote server a copy of the web page; render the web page in a headless browser to provide a computer-accessible visual output; perform visual analysis of the visual output via a digital eye; compare the visual analysis to a plurality of known phishing target websites; and if the comparison identifies the web page as visually similar to a known phishing target website, detect the web page as a phishing web page.

Abstract: Methods, systems, and media for detecting anomalous network activity are provided. In some embodiments, a method for detecting anomalous network activity is provided, the method comprising: receiving information indicating network activity, wherein the information includes IP addresses corresponding to devices participating in the network activity; generating a graph representing the network activity, wherein each node of the graph indicates an IP address of a device; generating a representation of the graph, wherein the representation of the graph reduces a dimensionality of information indicated in the graph; identifying a plurality of clusters of network activity based on the representation of the graph; determining that at least one cluster corresponds to anomalous network activity; and in response to determining that the at least one cluster corresponds to anomalous network activity, causing a network connection of at least one device included in the at least one cluster to be blocked.