Abstract: A computer system includes one or more processors and machine readable storage media coupled to the one or more processors having instructions stored therein that cause the computer system to: receive a payment request packet from an Internet of Things (IoT) device; extract a signature of the payment request packet that includes metadata; identify a firmware update status from the metadata of the signature; determine that the IoT device has a first version of the firmware installed on the IoT device based on the firmware update status; cause a second version of the firmware to be installed on the IoT device in response to determining the IoT device has the first version of the firmware; and initiate a transaction based on the payment request packet responsive to the second version of the firmware being installed on the IoT device.

Abstract: A system and method for retrieving and extracting security information is provided. The method includes (i) extracting seed Uniform Resource Locators (URLs) from social media based on keywords that are identified for each sub-domain, (ii) crawling a security related content in the extracted seed URLs to determine relevant URLs that are related to a security domain from the extracted seed URLs, (iii) classifying the security related content into sub-domains of security to obtain domain coverage, (iv) extracting text that include acronyms from the relevant URLs, (v) automatically evolving a security ontology based on extracted text using a Long Short-Term Memory (LSTM) deep Learning model, (vi) ranking search results by accessing credibility of the URLs that include the security related content based on domain relevance and (vii) providing the ranked search results that includes trends.

Abstract: A system and method for retrieving and extracting security information is provided. The method includes (i) extracting seed Uniform Resource Locators (URLs) from social media based on keywords that are identified for each sub-domain, (ii) crawling a security related content in the extracted seed URLs to determine relevant URLs that are related to a security domain from the extracted seed URLs, (iii) classifying the security related content into sub-domains of security to obtain domain coverage, (iv) extracting text that include acronyms from the relevant URLs, (v) automatically evolving a security ontology based on extracted text using a Long Short-Term Memory (LSTM) deep Learning model, (vi) ranking search results by accessing credibility of the URLs that include the security related content based on domain relevance and (vii) providing the ranked search results that includes trends.