Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data from these scans. The security management system may also receive data from other sources, and, as a result, the system may handle data having many different formats and attributes. When the security management system tries to associate data to assets, there may not be a globally unique identifier that is applicable for all received data. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly correlate data with assets based on attribute information.

Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data from these scans. The security management system may also receive data from other sources, and, as a result, the system may handle data having many different formats and attributes. When the security management system tries to associate data to assets, there may not be a globally unique identifier that is applicable for all received data. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly correlate data with assets based on attribute information.

Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data chunks from these scans. The security management system may also receive data chunks from other sources, and, as a result, the system may handle data chunks having many different formats and attributes. When the security management system tries to associate data chunks to assets, there may not be a globally unique identifier that is applicable for all received data chunks. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly match data chunks to assets based on the attribute or attributes that are available within the data chunks.

Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data chunks from these scans. The security management system may also receive data chunks from other sources, and, as a result, the system may handle data chunks having many different formats and attributes. When the security management system tries to associate data chunks to assets, there may not be a globally unique identifier that is applicable for all received data chunks. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly match data chunks to assets based on the attribute or attributes that are available within the data chunks.

Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data chunks from these scans. The security management system may also receive data chunks from other sources, and, as a result, the system may handle data chunks having many different formats and attributes. When the security management system tries to associate data chunks to assets, there may not be a globally unique identifier that is applicable for all received data chunks. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly match data chunks to assets based on the attribute or attributes that are available within the data chunks.

Abstract: Described herein is a system and method for detecting vulnerability state deltas, the method comprising the steps of: receiving data related to a network connected device; determining a vulnerability state of the network connected device based upon the data; storing the vulnerability state in a vulnerability state database; receiving additional data related to the network connected device; determining an updated vulnerability state of the network connected device based upon the additional data; determining one or more deltas based upon differences between the vulnerability stare and the updated vulnerability state; and updating the stored vulnerability state with the updated vulnerability state.

Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data chunks from these scans. The security management system may also receive data chunks from other sources, and, as a result, the system may handle data chunks having many different formats and attributes. When the security management system tries to associate data chunks to assets, there may not be a globally unique identifier that is applicable for all received data chunks. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly match data chunks to assets based on the attribute or attributes that are available within the data chunks.

Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data chunks from these scans. The security management system may also receive data chunks from other sources, and, as a result, the system may handle data chunks having many different formats and attributes. When the security management system tries to associate data chunks to assets, there may not be a globally unique identifier that is applicable for all received data chunks. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly match data chunks to assets based on the attribute or attributes that are available within the data chunks.

Abstract: A security management system may be remotely deployed (e.g., using a cloud-based architecture) to add security to an enterprise network. For example, the security management system may scan assets within the enterprise network for vulnerabilities and may receive data chunks from these scans. The security management system may also receive data chunks from other sources, and, as a result, the system may handle data chunks having many different formats and attributes. When the security management system tries to associate data chunks to assets, there may not be a globally unique identifier that is applicable for all received data chunks. Provided in the present disclosure are exemplary techniques for tracking assets across a network using an asset correlation engine that can flexibly match data chunks to assets based on the attribute or attributes that are available within the data chunks.

Abstract: Described herein is a system and method for detecting vulnerability state deltas, the method comprising the steps of: receiving data related to a network connected device; determining a vulnerability state of the network connected device based upon the data; storing the vulnerability state in a vulnerability state database; receiving additional data related to the network connected device; determining an updated vulnerability state of the network connected device based upon the additional data; determining one or more deltas based upon differences between the vulnerability stare and the updated vulnerability state; and updating the stored vulnerability state with the updated vulnerability state.