Abstract: Methods and systems for assessing and evaluating vulnerabilities of a networked system are presented. A list of known vulnerabilities that have been disclosed in the public may be obtained. The networked system may be scanned from an external perspective to obtain network information of the networked system. A subset of the known vulnerabilities may be determined to be relevant to the networked system based on correlations between the vulnerabilities and the network information. The networked system may also be analyzed from an internal perspective to determine impacts of the relevant known vulnerabilities to the networked system. The impact of a vulnerability may be determined based on the type of data and/or the type of services that may be accessible in an attack that exploits the vulnerability. The vulnerabilities may then be ranked and addressed based on the impacts.

Abstract: Various embodiments of systems and methods for an at-risk system identification via analysis of discussions from various online hacker communities are disclosed herein.

Abstract: Various embodiments for predicting which software vulnerabilities will be exploited by malicious hackers and hence prioritized by patching are disclosed.

Abstract: Various embodiments of a system and methods for reasoning about enterprise-related external cyber threats using a rule-leaning approach are disclosed.

Abstract: Embodiments for systems and methods for third party assessment related to the evaluation of risks associated with third parties in which hacker conversations on websites are filtered and analyzed using keywords as input to uncover relevant forum and marketplace discussions are disclosed herein.

Abstract: Embodiments of a system and method for identifying malware tasks using a controlled environment to run malicious software to generate analysis reports, a parser to extract features from the analysis reports and a cognitively inspired learning algorithm to predict tasks associated with the malware are disclosed.

Abstract: Embodiments of a system and method for identifying malware tasks using a controlled environment to run malicious software to generate analysis reports, a parser to extract features from the analysis reports and a cognitively inspired learning algorithm to predict tasks associated with the malware are disclosed.