In overview, methods, computer programs products and devices for securing software are provided. In accordance with the disclosure, a method may comprise attaching a debugger process to a software process. During execution of the software process, operations relevant to the functionality of the code process are carried out within the debugger process. As a result, the debugger process cannot be replaced or subverted without impinging on the functionality of the software process. The software process can therefore be protected from inspection by modified or malicious debugging techniques.

In overview, methods, computer programs products and devices for securing software are provided. In accordance with the disclosure, a method may comprise attaching a debugger process to a software process. During execution of the software process, operations relevant to the functionality of the code process are carried out within the debugger process. As a result, the debugger process cannot be replaced or subverted without impinging on the functionality of the software process. The software process can therefore be protected from inspection by modified or malicious debugging techniques.

Methods and systems for assessing internet exposure of a cloud-based workload are disclosed. A method comprises accessing at least one cloud provider API to determine a plurality of entities capable of routing traffic in a virtual cloud environment associated with a target account containing the workload, querying the at least one cloud provider API to determine at least one networking configuration of the entities, building a graph connecting the plurality of entities based on the networking configuration, accessing a data structure identifying services publicly accessible via the Internet and capable of serving as an internet proxy; integrating the identified services into the graph; traversing the graph to identify at least one source originating via the Internet and reaching the workload, and outputting a risk notification associated with the workload. Systems and computer-readable media implementing the above method are also disclosed.

Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.

Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.

Example methods are provided for virtual machine introspection in which a guest monitoring mode (GMM) module monitors the execution of guest calls by an agent that resides in a virtual machine (VM). The GMM module sets a bit in bit mask that corresponds to a guest call that the agent needs to execute, and inserts an invisible breakpoint in the code of the guest call. If the GMM module detects that despite the setting of the bit in the bit mask, the agent does not complete the execution of the code (due to the invisible breakpoint not being triggered), then the GMM module considers this condition as a potential hijack of the VM by malicious code.

Example methods are provided for virtual machine introspection in which a guest monitoring mode (GMM) module monitors the execution of guest calls by an agent that resides in a virtual machine (VM). The GMM module sets a bit in bit mask that corresponds to a guest call that the agent needs to execute, and inserts an invisible breakpoint in the code of the guest call. If the GMM module detects that despite the setting of the bit in the bit mask, the agent does not complete the execution of the code (due to the invisible breakpoint not being triggered), then the GMM module considers this condition as a potential hijack of the VM by malicious code.

Systems and methods are disclosed for event-based application control. A system extension is configured to leverage an endpoint security API for monitoring event activity within operating system kernel processes. The system extension registers with the endpoint security API particular event types for which the system extension would like to receive notifications. In response to receiving notifications regarding detected events corresponding to the registered event types, the system extension determines if the event, and its corresponding process, are safe and allowable to execute. In various embodiments, the system leverages whitelists, blacklists, and rules policies for making a safeness determination regarding the event notification. The system extension transmits this determination to the operating system via the endpoint security API.

Systems and methods are disclosed for event-based application control. A system extension is configured to leverage an endpoint security API for monitoring event activity within operating system kernel processes. The system extension registers with the endpoint security API particular event types for which the system extension would like to receive notifications. In response to receiving notifications regarding detected events corresponding to the registered event types, the system extension determines if the event, and its corresponding process, are safe and allowable to execute. In various embodiments, the system leverages whitelists, blacklists, and rules policies for making a safeness determination regarding the event notification. The system extension transmits this determination to the operating system via the endpoint security API.

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.