Abstract: A network security computing system includes a steganographic communications analysis engine monitoring incoming and outgoing messages on a secure computing network. The steganographic communications analysis engine identifies a pattern of file transfers between a first computing device on the secure computing network and an internal or external message recipient. When a pattern is identified, the steganographic communications analysis engine quarantines an associated computing device from the secure network. The steganographic communications analysis engine analyzes files transferred between the computing device and the recipient for indications of steganographic information and causes display, based on an identified indication of steganography, an indication that the computing device had been compromised by command and control malware.

Abstract: A network traffic correlation engine monitors inbound and/or outbound connection information received from on each host computer system on a network. Each host device on the network store data logs corresponding to information corresponding to communications sent by the device and received by the device. The network traffic correlation engine correlates connections between different hosts throughout the network. If the network traffic correlation engine identified unmatched outbound and inbound connections, the network traffic correlation engine generates an alert to initiate further investigation and may also provide a mapping of the communications showing a possible start device for the connection and/or a type of access that the connections may now be providing.

Abstract: A network environment scanning engine may monitor electronic communications received via an external computing network and by an enterprise computing system. The network environment canning engine after receipt of an electronic message, analyze the electronic message to identify, by a network environment scanning engine using a machine learning algorithm, executable code for execution by a processor of computing device addressed as a recipient of the message. The network environment scanning engine further analyzes, using a machine-learning based algorithm in a virtual security environment, the executable code to identify whether the executable code comprises one or more environment variables.

Abstract: Software code optimization for specific computerized tasks is achieved by utilizing task-specific ML models that are trained to determine optimal sets of software code by intelligently assessing available hardware resources (i.e., specifically, processing devices, such as CPUs and GPUs) and, in some instances, SDKs for a specified task and, as a result, determining an optimal set of software codes for the specified task that is tied to one or more of the available hardware resources.

Abstract: Aspects of the disclosure relate to email verification. A computing platform may receive an electronic message and identify one or more portions of content in the message. Then, the computing platform may generate and embed one or more message-specific identifiers into the electronic message and store electronic message information associating the one or more portions of content with the one or more embedded message-specific identifiers. Thereafter, the computing platform may receive an electronic message verification request to verify authenticity of an identified electronic message received by a computing device. The computing platform may prompt a user of the computing device to provide authentication information associated with one or more portions of content of the identified electronic message.

Abstract: A network environment scanning engine may monitor electronic communications received via an external computing network and by an enterprise computing system. The network environment scanning engine after receipt of an electronic message, analyze the electronic message to identify, by a network environment scanning engine using a machine learning algorithm, executable code for execution by a processor of computing device addressed as a recipient of the message. The network environment scanning engine further analyzes, using a machine-learning based algorithm in a virtual security environment, the executable code to identify whether the executable code comprises one or more environment variables.

Abstract: A network security computing system includes a steganographic communications analysis engine monitoring incoming and outgoing messages on a secure computing network. The steganographic communications analysis engine identifies a pattern of file transfers between a first computing device on the secure computing network and an internal or external message recipient. When a pattern is identified, the steganographic communications analysis engine quarantines an associated computing device from the secure network. The steganographic communications analysis engine analyzes files transferred between the computing device and the recipient for indications of steganographic information and causes display, based on an identified indication of steganography, an indication that the computing device had been compromised by command and control malware.

Abstract: A network traffic correlation engine monitors inbound and/or outbound connection information received from on each host computer system on a network. Each host device on the network store data logs corresponding to information corresponding to communications sent by the device and received by the device. The network traffic correlation engine correlates connections between different hosts throughout the network. If the network traffic correlation engine identified unmatched outbound and inbound connections, the network traffic correlation engine generates an alert to initiate further investigation and may also provide a mapping of the communications showing a possible start device for the connection and/or a type of access that the connections may now be providing.

Abstract: A network security computing system includes a steganographic communications analysis engine monitoring incoming and outgoing messages on a secure computing network. The steganographic communications analysis engine identifies a pattern of file transfers between a first computing device on the secure computing network and an internal or external message recipient. When a pattern is identified, the steganographic communications analysis engine quarantines an associated computing device from the secure network. The steganographic communications analysis engine analyzes files transferred between the computing device and the recipient for indications of steganographic information and causes display, based on an identified indication of steganography, an indication that the computing device had been compromised by command and control malware.

Abstract: A system determines baseline deployment properties of operating system deployments stored by a deployment repository and endpoint deployment properties of a deployed operating system executed by an endpoint device. An artificial intelligence model is configured to determine a security response based at least in part on the endpoint deployment properties of the endpoint device. By providing the endpoint deployment properties to the artificial intelligence model, a mismatch value is determined that corresponds to an amount that the endpoint deployment properties are different than the baseline deployment properties. Based on the mismatch value, an action is determined to improve security of the deployed operating system executed by the endpoint device. The determined action is executed to improve security of the deployed operating system.

Abstract: A lateral movement identification tool analyzes communications sent and received from a local host to identify potential instances of lateral movement. When the host-based lateral movement identification tool identifies a host to host connection, the tool processes one or more artificial intelligence algorithms to analyze information from local network resources including a directory service, a local network system such as a network basic input/output system, a domain name system, and event logs. The lateral movement identification tool correlates the aggregated information with identified host to host messaging and sends alerts when lateral movement is suspected. Alerts may be either presented locally or provided to a central console based on configuration information.

Abstract: A network traffic correlation engine monitors inbound and/or outbound connection information received from on each host computer system on a network. Each host device on the network store data logs corresponding to information corresponding to communications sent by the device and received by the device. The network traffic correlation engine correlates connections between different hosts throughout the network. If the network traffic correlation engine identified unmatched outbound and inbound connections, the network traffic correlation engine generates an alert to initiate further investigation and may also provide a mapping of the communications showing a possible start device for the connection and/or a type of access that the connections may now be providing.

Abstract: A lateral movement identification tool analyzes communications sent and received from a local host to identify potential instances of lateral movement. When the host-based lateral movement identification tool identifies a host to host connection, the tool processes one or more artificial intelligence algorithms to analyze information from local network resources including a directory service, a local network system such as a network basic input/output system, a domain name system, and event logs. The lateral movement identification tool correlates the aggregated information with identified host to host messaging and sends alerts when lateral movement is suspected. Alerts may be either presented locally or provided to a central console based on configuration information.

Abstract: Aspects of the disclosure relate to email verification. A computing platform may receive an electronic message and identify one or more portions of content in the message. Then, the computing platform may generate and embed one or more message-specific identifiers into the electronic message and store electronic message information associating the one or more portions of content with the one or more embedded message-specific identifiers. Thereafter, the computing platform may receive an electronic message verification request to verify authenticity of an identified electronic message received by a computing device. The computing platform may prompt a user of the computing device to provide authentication information associated with one or more portions of content of the identified electronic message.

Abstract: A lateral movement identification tool analyzes communications sent and received from a local host to identify potential instances of lateral movement. When the host-based lateral movement identification tool identifies a host to host connection, the tool processes one or more artificial intelligence algorithms to analyze information from local network resources including a directory service, a local network system such as a network basic input/output system, a domain name system, and event logs. The lateral movement identification tool correlates the aggregated information with identified host to host messaging and sends alerts when lateral movement is suspected. Alerts may be either presented locally or provided to a central console based on configuration information.

Abstract: A system determines baseline deployment properties of operating system deployments stored by a deployment repository and endpoint deployment properties of a deployed operating system executed by an endpoint device. An artificial intelligence model is configured to determine a security response based at least in part on the endpoint deployment properties of the endpoint device. By providing the endpoint deployment properties to the artificial intelligence model, a mismatch value is determined that corresponds to an amount that the endpoint deployment properties are different than the baseline deployment properties. Based on the mismatch value, an action is determined to improve security of the deployed operating system executed by the endpoint device. The determined action is executed to improve security of the deployed operating system.

Abstract: A network traffic correlation engine monitors inbound and/or outbound connection information received from on each host computer system on a network. Each host device on the network store data logs corresponding to information corresponding to communications sent by the device and received by the device. The network traffic correlation engine correlates connections between different hosts throughout the network. If the network traffic correlation engine identified unmatched outbound and inbound connections, the network traffic correlation engine generates an alert to initiate further investigation and may also provide a mapping of the communications showing a possible start device for the connection and/or a type of access that the connections may now be providing.

Abstract: A lateral movement identification tool analyzes communications sent and received from a local host to identify potential instances of lateral movement. When the host-based lateral movement identification tool identifies a host to host connection, the tool processes one or more artificial intelligence algorithms to analyze information from local network resources including a directory service, a local network system such as a network basic input/output system, a domain name system, and event logs. The lateral movement identification tool correlates the aggregated information with identified host to host messaging and sends alerts when lateral movement is suspected. Alerts may be either presented locally or provided to a central console based on configuration information.

Abstract: A network traffic correlation engine monitors inbound and/or outbound connection information received from on each host computer system on a network. Each host device on the network store data logs corresponding to information corresponding to communications sent by the device and received by the device. The network traffic correlation engine correlates connections between different hosts throughout the network. If the network traffic correlation engine identified unmatched outbound and inbound connections, the network traffic correlation engine generates an alert to initiate further investigation and may also provide a mapping of the communications showing a possible start device for the connection and/or a type of access that the connections may now be providing.

Abstract: A network security computing system includes a steganographic communications analysis engine monitoring incoming and outgoing messages on a secure computing network. The steganographic communications analysis engine identifies a pattern of file transfers between a first computing device on the secure computing network and an internal or external message recipient. When a pattern is identified, the steganographic communications analysis engine quarantines an associated computing device from the secure network. The steganographic communications analysis engine analyzes files transferred between the computing device and the recipient for indications of steganographic information and causes display, based on an identified indication of steganography, an indication that the computing device had been compromised by command and control malware.