A system for detecting security threats in a computing device receives a first set of signals from components of the computing device. The first set of signals includes intercommunication electrical signals between the components of the computing device and electromagnetic radiation signals propagated from the components of the computing device. The system extracts baseline features from the first set of signals. The baseline features represent a unique electrical signature of the computing device. The system extracts test features from a second set of signals received from the component of the system. The system determines whether there is a deviation between the test features and baseline features. If the system detects the deviation, the system determines that the computing device is associated with a particular anomaly that makes the computing device vulnerable to unauthorized access.

A method for user-defined validation of content stored in a storage system, the method may include receiving a request to execute a user-defined validation process (UDVP) on the content that is stored in the storage system; wherein the request is associated with means for executing the UDVP, and a content identifier; scheduling, by the storage system, at least one execution of the UDVP; executing the UDVP according to the scheduling to provide one or more validation results; and finding that the one or more validation results are indicative of potential security issues and performing one or more validation-triggered security measures.

A method for user-defined validation of content stored in a storage system, the method may include receiving a request to execute a user-defined validation process (UDVP) on the content that is stored in the storage system; wherein the request is associated with means for executing the UDVP, and a content identifier; scheduling, by the storage system, at least one execution of the UDVP; executing the UDVP according to the scheduling to provide one or more validation results; and finding that the one or more validation results are indicative of potential security issues and performing one or more validation-triggered security measures.

An Information Handling System (IHS) includes at least one hardware device in communication with a Baseboard Management Controller (BMC). The hardware device includes executable instructions for establishing a secure communication channel with the BMC, and subsequently receiving a list of allowed commands from the BMC. When a command is received by the hardware device, it determines whether the command is included in the list such that when the command is in the list and the command is received within the secure communication channel, the hardware device performs the command. However, when the command is in the list and the command is received outside of the secure communication channel, the hardware device ignores the command.

An Information Handling System (IHS) includes at least one hardware device in communication with a Baseboard Management Controller (BMC). The hardware device includes executable instructions for establishing a secure communication channel with the BMC, and subsequently receiving a list of allowed commands from the BMC. When a command is received by the hardware device, it determines whether the command is included in the list such that when the command is in the list and the command is received within the secure communication channel, the hardware device performs the command. However, when the command is in the list and the command is received outside of the secure communication channel, the hardware device ignores the command.

Techniques for performing machine learning-based program analysis using synthetically generated labeled data are described. A method of performing machine learning-based program analysis using synthetically generated labeled data may include receiving a request to perform program analysis on code, determining a first portion of the code associated with a first error type, sending the first portion of the code to an endpoint of a machine learning service associated with an error detection model to detect the first error type, the error detection model trained using synthetically generated labeled data, and receiving inference results from the error detection model identifying one or more errors of the first error type in the first portion of the code.

Some embodiments provide a method for evaluating authorization policies that restrict access to API (Application Programming Interfaces) calls to an application executing on a host system. At the application, the method receives an API call to execute. The method directs a process virtual machine (VM) executing inside the application to make an authorization decision for the API call. The method executes the API call after receiving an authorization decision to allow the API call from the process VM executing inside the application.

Systems and methods to detect malicious software include an application software repository including a stored header file associated with a driver, an executable, or both, and are operable to (i) receive a memory dump file upon an operating system crash including a driver copy, an executable copy, or both, (ii) verify the memory dump file is new for analysis, (iii) compress the verified memory dump file to generate a memory snapshot of the verified memory dump file, (iv) scan the memory snapshot for a memory dump header file associated with the driver copy, the executable copy, or both, and (v) identify and extract malicious software when the memory dump header file from the memory snapshot fails to match at least one stored header file in the application software repository.

Systems and methods to detect malicious software include an application software repository including a stored header file associated with a driver, an executable, or both, and are operable to (i) receive a memory dump file upon an operating system crash including a driver copy, an executable copy, or both, (ii) verify the memory dump file is new for analysis, (iii) compress the verified memory dump file to generate a memory snapshot of the verified memory dump file, (iv) scan the memory snapshot for a memory dump header file associated with the driver copy, the executable copy, or both, and (v) identify and extract malicious software when the memory dump header file from the memory snapshot fails to match at least one stored header file in the application software repository.

Disclosed herein are a dynamic security module server device for transmitting a dynamic security module to a user terminal and receiving a security management event from the user terminal, and a method of operating the dynamic security module server device. The dynamic security module server device includes a communication unit configured to transmit and receive a security management event over a network, and a processor configured to control the communication unit. The processor is configured to create a security session with the security client of a user terminal, and to transmit a dynamic security module to the security client of the user terminal so that part or all of code performing security management in the security client of the user terminal in which the security session has been created has a predetermined valid period.