Patent classifications
G06F21/56
Automated Code Lockdown To Reduce Attack Surface For Software
In an example embodiment, a system determines a set of instructions from the available instructions for a computer application. The determined set of instructions provides specific functionality of the computer application. The system may determine the set of instructions by performing functional testing and negative testing on the specific functionality. The system may reorganize and randomize the set of instructions in memory and write the reorganized set of instructions to a smaller memory space. For each available instruction not in the set of instructions, the system changes the respective instruction to inoperative to prevent execution of the respective instruction. The system may change the respective instruction to inoperative by overwriting the instruction with a NOP instruction. The system then captures a memory address of the computer application being accessed at runtime. The system may declare a security attack if the captured memory address matches a memory address for an inoperative instruction.
SYSTEM AND METHOD TO MITIGATE MALICIOUS CALLS
Systems and methods are provided in example embodiments for mitigating malicious calls. The system can be configured to receive a function call, determine the location of a memory page that initiated the function call, determine if the memory page is associated with a trusted module, and block the function call if the memory page is not associated with the trusted module. In addition, the system can determine the return address for the function call and block the function call if the return address does not belong to the trusted module. Further, the system can determine a parameter for the function call, determine if the parameter is a known parameter used by the process that called the function, and block the function call if the parameter is not the known parameter used by the process that called the function.
ANOMALY AND MALWARE DETECTION USING SIDE CHANNEL ANALYSIS
The present disclosure describes systems and methods for detecting malware. More particularly, the system includes a monitoring device that monitors side-channel activity of a target device. The monitoring device that can work in conjunction with (or independently of) a cloud-based security analytics engine to perform anomaly detection and classification on the side-channel activity. For example, the monitoring device can calculate a first set of features that are then transmitted to the security analytics engine for anomaly detection and classification.
Method For Updating Process Objects In An Engineering System
A method for updating process objects of an automation project stored in an engineering system, wherein an automation device is designed and/or configured via the engineering system to control a technical process and wherein, furthermore, the technical process to be controlled can be operated and monitored via an operator system in which changes to process objects made during the run-time are not lost but secured and are automatically “updated” or “traced” in the engineering system.
ANTI-MALWARE DEVICE, ANTI-MALWARE SYSTEM, ANTI-MALWARE METHOD, AND RECORDING MEDIUM IN WHICH ANTI-MALWARE PROGRAM IS STORED
An anti-malware device 50 includes: a risk information storage unit 51 in which risk information 510 is stored, in which there are associated a value indicating an attribution of an information processing device 60 for executing software 600, a value indicating an attribution of the software 600, and a value that indicates the degree of risk when the software 600 is executed; a subject attribution collection unit 53 for collecting the value indicating the attribution of the information processing device 60; an object attribution collection unit 54 for collecting the value indicating the attribution of the software 600; and a determination unit 55 for determining that the software 600 is malware when the value indicating the degree of risk obtained by comparing the risk information 510 and the values collected by the subject attribution collection unit 53 and object attribution collection unit 54 satisfies a criterion.
Extracting Malicious Instructions on a Virtual Machine in a Network Environment
A system including a guest virtual machine with one or more virtual machine measurement points configured to collect virtual machine operating characteristics metadata and a hypervisor control point configured to receive virtual machine operating characteristics metadata from the virtual machine measurement points. The hypervisor control point is further configured to send the virtual machine operating characteristics metadata to a hypervisor associated with the guest virtual machine. The system further includes the hypervisor configured to receive the virtual machine operating characteristics metadata and to forward the virtual machine operating characteristics metadata to a hypervisor device driver in a virtual vault machine. The system further includes the virtual vault machine configured to determine a classification for the guest virtual machine based on the virtual machine operating characteristics metadata and to send the determined classification to a vault management console.
Extracting Malicious Instructions on a Virtual Machine in a Network Environment
A system including a guest virtual machine with one or more virtual machine measurement points configured to collect virtual machine operating characteristics metadata and a hypervisor control point configured to receive virtual machine operating characteristics metadata from the virtual machine measurement points. The hypervisor control point is further configured to send the virtual machine operating characteristics metadata to a hypervisor associated with the guest virtual machine. The system further includes the hypervisor configured to receive the virtual machine operating characteristics metadata and to forward the virtual machine operating characteristics metadata to a hypervisor device driver in a virtual vault machine. The system further includes the virtual vault machine configured to determine a classification for the guest virtual machine based on the virtual machine operating characteristics metadata and to send the determined classification to a vault management console.
MODEL-BASED COMPUTER ATTACK ANALYTICS ORCHESTRATION
Examples relate to model-based computer attack analytics orchestration. In one example, a computing device may: generate, using an attack model that specifies behavior of a particular attack on a computing system, a hypothesis for the particular attack, the hypothesis specifying, for a particular state of the particular attack, at least one attack action; identify, using the hypothesis, at least one analytics function for determining whether the at least one attack action specified by the hypothesis occurred on the computing system; provide an analytics device with instructions to execute the at least one analytics function on the computing system; receive analytics results from the analytics device; and update a state of the attack model based on the analytics results.
SCALABLE COMPUTER VULNERABILITY TESTING
Vulnerability testing tasks can be received and distributed, via a work scheduler, to computer test environments. Each of the test environments can have a detector computing component running in the environment. Each detector component can respond to receiving one of the tasks from the work scheduler by conducting a vulnerability test on an endpoint of a target, detecting results of the vulnerability test, generating output indicating the results of the vulnerability test, and sending the output to an output processor. The work scheduler can initiate dynamic scaling of the test environments by activating and deactivating test environments in response to determining that the test environments are overloaded or underloaded, respectively. Also an overall time-based limit on testing for a target can be enforced via the work scheduler.
ANTI-PHISHING PROTECTION
Anti-Phishing protection assists in protecting against phishing attacks. Any links that are contained within a message that has been identified as a phishing message are disabled. A warning message is shown when the phishing message is accessed. The first time a disabled link within the phishing message is selected a dismissible dialog box is displayed containing information about how to enable links in the message. After the user dismisses the dialog, clicking on a disabled link causes the warning message to flash drawing the user's attention to the potential severity of the problem. The links may be enabled by the user by selecting the warning message and choosing the appropriate option. Once the user enables the links, future displays of the message show the links as enabled.