A non-transitory storage medium having stored thereon logic wherein the logic is executable by one or more processors to perform operations is disclosed. The operations may include parsing an object, detecting one or more features of a predefined feature set, evaluating each feature-condition pairing of a virtual feature using the one or more values observed of each of the one or more detected features, determining whether results of the evaluation of one or more feature-condition pairings satisfies terms of the virtual feature, and responsive to determining the results of the evaluation satisfy the virtual feature, performing one or more of a static analysis to determine whether the object is associated with anomalous characteristics or a dynamic analysis on the object to determine whether the object is associated with anomalous behaviors.

Machine learning adversarial campaign mitigation on a computing device. The method may include deploying an original machine learning model in a model environment associated with a client device; deploying a classification monitor in the model environment to monitor classification decision outputs in the machine learning model; detecting, by the classification monitor, a campaign of adversarial classification decision outputs in the machine learning model; applying a transformation function to the machine learning model in the model environment to transform the adversarial classification decision outputs to thwart the campaign of adversarial classification decision outputs; determining a malicious attack on the client device based in part on detecting the campaign of adversarial classification decision outputs; and implementing a security action to protect the computing device against the malicious attack.

Machine learning adversarial campaign mitigation on a computing device. The method may include deploying an original machine learning model in a model environment associated with a client device; deploying a classification monitor in the model environment to monitor classification decision outputs in the machine learning model; detecting, by the classification monitor, a campaign of adversarial classification decision outputs in the machine learning model; applying a transformation function to the machine learning model in the model environment to transform the adversarial classification decision outputs to thwart the campaign of adversarial classification decision outputs; determining a malicious attack on the client device based in part on detecting the campaign of adversarial classification decision outputs; and implementing a security action to protect the computing device against the malicious attack.

Graph computing over micro and macro views includes expanding, with a processor at run-time, a set of nodes to include a node generated in response to received data corresponding to an event query. A first inference of an inference ensemble is determined by traversing a base graph whose nodes are associated with a discriminant power that exceeds a predetermined entity threshold. A second inference of the inference ensemble is determined by traversing a micro-view graph whose nodes are selected based on a number of references that exceeds a predetermined reference threshold. A third inference of the inference ensemble is determined by traversing a macro-view graph having one or more committee nodes and computing for each committee node a macro-node vote and generating a response to the event query based on the inference ensemble.

A multi-endpoint event graph is used to detect malware based on malicious software moving through a network.

A multi-endpoint event graph is used to detect malware based on malicious software moving through a network.

A compute instance is instrumented to detect certain kernel memory allocation functions, in particular functions that allocate heap memory and/or make allocated memory executable. Dynamic shell code exploits can then be detected when code executing from heap memory allocates additional heap memory and makes that additional heap memory executable.

A compute instance is instrumented to detect certain kernel memory allocation functions, in particular functions that allocate heap memory and/or make allocated memory executable. Dynamic shell code exploits can then be detected when code executing from heap memory allocates additional heap memory and makes that additional heap memory executable.

In one embodiment, a system for managing communication connections in a virtualization environment includes a plurality of host machines implementing a virtualization environment, wherein each of the host machines includes a hypervisor, at least one user virtual machine (user VM), and a distributed file server that includes file server virtual machines (FSVMs) and associated local storage devices. Each FSVM and associated local storage device are local to a corresponding one of the host machines, and the FSVMs conduct I/O transactions with their associated local storage devices based on I/O requests received from the user VMs. Each of the user VMs on each host machine sends each of its respective I/O requests to an FSVM that is selected by one or more of the FSVMs for each I/O request based on a lookup table that maps a storage item referenced by the I/O request to the selected one of the FSVMs.

In one embodiment, a system for managing communication connections in a virtualization environment includes a plurality of host machines implementing a virtualization environment, wherein each of the host machines includes a hypervisor, at least one user virtual machine (user VM), and a distributed file server that includes file server virtual machines (FSVMs) and associated local storage devices. Each FSVM and associated local storage device are local to a corresponding one of the host machines, and the FSVMs conduct I/O transactions with their associated local storage devices based on I/O requests received from the user VMs. Each of the user VMs on each host machine sends each of its respective I/O requests to an FSVM that is selected by one or more of the FSVMs for each I/O request based on a lookup table that maps a storage item referenced by the I/O request to the selected one of the FSVMs.