Abstract: Embodiments of systems and methods for DNS leak prevention and protection are disclosed herein. In particular, certain embodiments include a local DNS protection agent installed on a system and an associated trusted external DNS protection server. The DNS protection agent prevents DNS leaks from applications on the system such that all DNS requests from the system are confined to requests from the DNS protection agent to the associated DNS protection server. As the DNS leak prevention provided by the DNS protection agent stops applications on the system from circumventing the DNS protection server, all DNS requests originating from the system remain under the control of the DNS protection server and thus desired DNS protection (e.g., as implemented on the DNS protection server) may be maintained. Certain embodiments prevent applications from using certain DNS security protocols, such as DoH and DoT, without going through the DNS protection agent.

Abstract: Examples of the present disclosure describe systems and methods for behavioral threat detection definition. In an example, a behavior rule comprising a set of rule instructions is used to define one or more events indicative of a behavior. For example, a set of events from which one event must be matched may be defined or a set of events from which all events must be matched may be defined. In some examples, events are matched based on an event name or type, or may be matched based on one or more parameters. Exact and/or inexact matching may be used. The set of rule instructions ultimately specifies one or more halt instructions, thereby indicating that a determination as to the presence of the behavior has been made. Example determinations include, but are not limited to, a match determination, a non-match determination, or an indication that additional monitoring should be performed.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection virtual machine. In examples, the virtual machine executes a rule comprising rule instructions. A rule may comprise one or more wait rule instructions that causes the virtual machine to pause execution. As events are added to an event queue for the rule virtual machine, the behavioral threat detection virtual machine evaluates such events in order to identify a positive or, in some instances, a negative match. When a matching event is identified, rule execution resumes. Eventually, a determination is made as a result of processing events and wait packets, thereby indicating the presence or absence of a malicious or potentially malicious behavior, among other examples. Thus, among other things, the behavioral threat detection virtual machine maintains a state associated with rule execution and processes events to identify behaviors accordingly.

Abstract: Examples of the present disclosure describe systems and methods for selective export address table filtering. In aspects, when an executable program is loaded in an operating system's execution environment, an address table filtering (ATF) module is loaded into the address space of a target process associated with the executable program. The ATF module may iterate a list of system library files to identify exported function names. The relative virtual address (RVA) of the exported function names may be modified to point to a protected memory location. An exception handler may be registered to process exceptions relating to access violations of the protected memory location. If the exception handler determines that an access violation caused the detected exception, the instruction pointer of the exception may be compared to the expected system library addresses boundaries. If the instruction pointer address is outside the boundaries, remedial action may occur.

Abstract: Aspects of the present disclosure relate to systems and methods for partitioning an OS or hypervisor utilized on a computing device from the process of proxy control. For example, a proxy may be installed on a separation kernel or firmware on a computing device that routes all data traffic received via a network connection to a cloud which performs various services such as IP reputation management, URL reputation detection and validation, malicious file filtering through potential malware detection.

Abstract: Examples of the present disclosure describe systems and methods for providing advanced file modification heuristics. In aspects, software content is selected for monitoring. The monitoring comprises determining when the software content performs file accesses that are followed by read and/or write operations. The read/write operations are analyzed in real-time to determine whether the software content is modifying file content. If the monitoring indicates the software content is modifying accessed files, mathematical calculations are applied to the read-write operations to determine the nature of the modifications. Based on the determined nature of the file modifications, the actions of the software content may be categorized and halted prior to completion; thereby, mitigating malicious cyberattacks and/or unauthorized accesses.

Abstract: Examples of the present disclosure describe systems and methods for detecting and mitigating stack pivoting exploits. In aspects, various “checkpoints” may be identified in software code. At each checkpoint, the current stack pointer, stack base, and stack limit for each mode of execution may be obtained. The current stack pointer for each mode of execution may be evaluated to determine whether the stack pointer falls within a stack range between the stack base and the stack limit of the respective mode of execution. When the stack pointer is determined to be outside of the expected stack range, a stack pivot exploit is detected and one or more remedial actions may be automatically performed.

Abstract: A protection module operates to analyze threats, at the protocol level (e.g., at the HTML level), by intercepting all requests that a browser engine resident in a computing device sends and receives, and the protection agent completes the requests without the help of the browser engine. And then the protection module analyzes and/or modifies the completed data before the browser engine has access to it, to, for example, display it. After performing all of its processing, removing, and/or adding any code as needed, the protection module provides the HTML content to the browser engine, and the browser engine receives responses from the protection agent as if it was speaking to an actual web server, when in fact, browser engine is speaking to an analysis engine of the protection module.

Abstract: Examples of the present disclosure describe systems and methods for sharing memory using a multi-ring shared, traversable and dynamic database. In aspects, the database may be synchronized and shared between multiple processes and/or operation mode protection rings of a system. The database may also be persisted to enable the management of information between hardware reboots and application sessions. The information stored in the database may be view independent, traversable, and resizable from various component views of the database. In some aspects, an event processor is additionally described. The event processor may use the database to allocate memory chunks of a shared heap to components/processes in one or more protection modes of the operating system.

Abstract: The present disclosure relates to systems and methods for identifying highly sensitive modules and taking a remediation or preventative action if such modules are accessed by malicious software. For example, the likelihood that a module is used for an exploit, and is thus sensitive, is categorized as high, medium, or low. The likelihood that a module can be used for an exploit can dictate whether, and to what degree, an application accessing the module is “suspicious.” However, in some instances, a sensitive module may have legitimate reasons to load when used in certain non-malicious ways. The system may also consider a trust level when determining what actions to take, such that an application and/or user having a higher trust level may be less suspicious when accessing a sensitive module as compared to an application or user having a lower trust level.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection engine. In examples, the behavioral threat detection engine manages execution of one or more virtual machines, wherein each virtual machine processes a rule in relation to a context. The behavioral threat detection engine uses any of a variety of techniques to identify when events occur. Accordingly, the behavioral threat detection engine provides event indications, in the form of event packets, to one or more virtual machines, such that corresponding rules are able to process the events accordingly. Eventually, a rule may make a determination as to the presence or absence of a behavior. As a result, execution of the associated virtual machine may be halted, thereby indicating to the behavioral threat detection engine that a determination has been made. Thus a behavioral threat detection engine employs a behavior-based approach to detecting malicious or potentially malicious behaviors.

Abstract: Examples of the present disclosure describe systems and methods for malicious software detection based on API trust. In an example, a set of software instructions executed by a computing device may call an API. A hook may be generated on the API, such that a threat processor may receive an indication when the API is called. Accordingly, the threat processor may generate a trust metric based on the execution of the set of software instructions, which may be used to determine whether the set of software instructions poses a potential threat. For example, one or more call stack frames may be evaluated to determine whether a return address is preceded by a call instruction, whether the return address is associated with a set of software instructions or memory associated with a set of software instructions, and/or whether the set of software instructions satisfies a variety of security criteria.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: A method and system for controlling access to an Internet resource is disclosed herein. When a request for an Internet resource, such as a Web site, is transmitted by an end-user of a LAN, a security appliance for the LAN analyzes a reputation index for the Internet resource before transmitting the request over the Internet. The reputation index is based on a reputation vector which includes a plurality of factors for the Internet resource such as country of domain registration, country of service hosting, country of an internet protocol address block, age of a domain registration, popularity rank, internet protocol address, number of hosts, to-level domain, a plurality of run-time behaviors, JavaScript block count, picture count, immediate redirect and response latency. If the reputation index for the Internet resource is at or above a threshold value established for the LAN, then access to the Internet resource is permitted.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection engine. In examples, the behavioral threat detection engine manages execution of one or more virtual machines, wherein each virtual machine processes a rule in relation to a context. The behavioral threat detection engine uses any of a variety of techniques to identify when events occur. Accordingly, the behavioral threat detection engine provides event indications, in the form of event packets, to one or more virtual machines, such that corresponding rules are able to process the events accordingly. Eventually, a rule may make a determination as to the presence or absence of a behavior. As a result, execution of the associated virtual machine may be halted, thereby indicating to the behavioral threat detection engine that a determination has been made. Thus a behavioral threat detection engine employs a behavior-based approach to detecting malicious or potentially malicious behaviors.

Abstract: Examples of the present disclosure describe systems and methods for detecting and mitigating stack pivoting exploits. In aspects, various “checkpoints” may be identified in software code. At each checkpoint, the current stack pointer, stack base, and stack limit for each mode of execution may be obtained. The current stack pointer for each mode of execution may be evaluated to determine whether the stack pointer falls within a stack range between the stack base and the stack limit of the respective mode of execution. When the stack pointer is determined to be outside of the expected stack range, a stack pivot exploit is detected and one or more remedial actions may be automatically performed.

Abstract: Examples of the present disclosure describe systems and methods for monitoring the security privileges of a process. In aspects, when a process is created, the corresponding process security token and privilege information is detected and recorded. At subsequent “checkpoints,” the security token is evaluated to determine whether the security token has been replaced, or whether new or unexpected privileges have been granted to the created process. When a modification to the security token is determined, a warning or indication of the modification is generated and the process may be terminated to prevent the use of the modified security token.

Abstract: Aspects of the present disclosure relate to threat detection of executable files. A plurality of static data points may be extracted from an executable file without decrypting or unpacking the executable file. The executable file may then be analyzed without decrypting or unpacking the executable file. Analysis of the executable file may comprise applying a classifier to the plurality of extracted static data points. The classifier may be trained from data comprising known malicious executable files, known benign executable files and known unwanted executable files. Based upon analysis of the executable file, a determination can be made as to whether the executable file is harmful.

Abstract: Methods and systems are provided for detecting a change in web content of a web page. In particular, executable instructions may be inserted into a web page such that a first fingerprint of the web page is created when viewed on a client device. The first fingerprint may then be compared to a previously created fingerprint to determine if the web page has been modified. The fingerprints may be based on one or more elements of the web page.

Abstract: In one aspect, a method of classifying a computer object as malware includes receiving at a base computer data about a computer object from each of plural remote computers on which the object or similar objects are stored. The data about the computer object received from the plural computers is compared in the base computer. The computer object is classified as malware on the basis of said comparison. In one embodiment, the data about the computer object includes one or more of: executable instructions contained within or constituted by the object; the size of the object; the name of the object; the logical storage location or path of the object on the respective remote computers; the vendor of the object; the software product and version associated with the object; and, events initiated by or involving the object when the object is created, configured or runs on the respective remote computers.