System and method for self-adjusting cybersecurity analysis and score generation
A system and method for self-adjusting cybersecurity analysis and score generation, wherein a reconnaissance engine gathers data about a client's computer network from the client, from devices and systems on the client's network, and from the Internet regarding various aspects of cybersecurity. Each of these aspects is evaluated independently, weighted, and cross-referenced to generate a cybersecurity score by aggregating individual vulnerability and risk factors together to provide a comprehensive characterization of cybersecurity risk using a transparent and traceable methodology. The scoring system itself can be used as a state machine with the cybersecurity score acting as a feedback mechanism, in which a cybersecurity score can be set at a level appropriate for a given organization, and data from clients or groups of clients with more extensive reporting can be used to supplement data for clients or groups of clients with less extensive reporting to enhance cybersecurity analysis and scoring.
1. A system for self-adjusting cybersecurity analysis and rating based on heterogeneous data and reconnaissance, comprising:
a computing device comprising a memory, a processor, and a network interface;
an automated planning service module, comprising a first plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to periodically or continuously establish a score for one or more of the following aspects of cybersecurity analysis by:
defining a target network and assigning it an Internet reconnaissance score;
collecting domain name system leak information by identifying improper network configurations in the internet protocol addresses and subdomains of the target network, and assigning a domain name system leak information score;
analyzing web applications used by within target network to identify vulnerabilities in the web applications that could allow unauthorized access to the target network, and assigning a web application security score;
searching social media networks for information of concern related to personnel present within the target network, and assigning a social network score;
conducting a scan of the target network for open TCP/UDP ports, and assigning an open port score;
identifying leaked credentials associated with the target network that are found to be disclosed in previous breach incidents, and assigning a credential score;
checking version and update information for hardware and software systems within the target network, and assigning a patching frequency score; and
performing an Internet search to identify references to content of interest, and assigning an open-source intelligence score; and
a cybersecurity scoring engine comprising a second plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to:
compute a composite cybersecurity score by combining, via a model, each of the Internet reconnaissance score, the domain name system leak information score, the web application security score, the social network score, the open port score, the credential score, the patching frequency score, and the open-source intelligence score into a single score;
when the composite cybersecurity score falls below a threshold score, coordinate with a task scheduling engine to effectuate a specific change impacting the target network to raise the composite cybersecurity score, thereby establishing a continuous feedback loop to maintain the system's composite cybersecurity score above the threshold value.
2. A method for self-adjusting cybersecurity analysis and rating based on heterogeneous data and reconnaissance, comprising the steps of:
computing a score for one or more of the following aspects of cybersecurity analysis by:
verifying domain name system information for each internet protocol address and subdomain of a target network, and assigning an Internet reconnaissance score;
collecting domain name system leak information by identifying improper network configurations in the internet protocol addresses and subdomains of the target network, and assigning a domain name system leak information score;
analyzing web applications used within the target network to identify vulnerabilities in the web applications that could allow unauthorized access to the target network, and assigning a web application security score;
searching social media networks for information of concern related to personnel present within the target network, and assigning a social network score;
conducting a scan of the target network for open TCP/UDP ports, and assigning an open port score;
identifying leaked credentials associated with the target network that are found to be disclosed in previous breach incidents, and assigning a credential score;
checking version and update information for hardware and software systems within the target network, and assigning a patching frequency score; and
performing an Internet search to identify references to content of interest, and assigning an open-source intelligence score;
creating a composite cybersecurity score by combining, with respective weights, each of the Internet reconnaissance score, the domain name system leak information score, the web application security score, the social network score, the open port score, the credential score, the patching frequency score, and the open-source intelligence score into a single composite score; and
when the composite cybersecurity score falls below a threshold score, coordinating with a task scheduling engine to effectuate a specific change impacting the target network to raise the composite cybersecurity score, thereby establishing a continuous feedback loop to maintain the system's composite cybersecurity score above the threshold value.