Abstract: A method of supporting decision-making of security control includes: (a) when an system for automatically analyzing a security threat receives a security warning from a security device, collecting security threat events generating the security warning from the security device; (b) when the collected security threat events exceed a preset event processing threshold, generating, by the system for automatically analyzing a security threat, a first request message for preferentially processing a security event; (c) when receiving the first request message generated from the system, determining, by the system for supporting priority of security control, a priority processing order of the security threat events, and notifying the system; and (d) when receiving the second request message generated from the system, determining, by the system for supporting priority of security control, a priority processing order and notifying the system for automatically analyzing a security threat of the determined priority process

Abstract: Disclosed are a system and method for automatically generating a playbook and verifying validity of a playbook based on artificial intelligence, wherein the system present invention includes a system for automatically generating a playbook that automatically generates the playbook, and a system for verifying validity of a playbook that is connected to the system for automatically generating a playbook through a network to perform the verification of the validity on the playbook received from the system for automatically generating a playbook.

Abstract: A device for automatically sorting a cyber attack includes an event feature generator that extracts a unique attacker IP by analyzing attacker IPs for each of the different kinds of security devices, and generates AI learning features of the security events of the different kinds of security devices including feature numerical data quantifying at least two or more features through attack information analysis recorded in the different kinds of security devices based on the information on the security events of the different kinds of security devices mapped to the extracted unique attacker IP, and an attack type sorter that learns the generated feature numerical data using an unsupervised learning algorithm, generates clustering data by sorting the feature numerical data into similar attack data and clustering sorted feature numerical data, and then analyzes the generated clustering data to identify a short-term or long-term attacker's cyber attack type.

Abstract: The present invention relates to a wireless sensor network, and more particularly, to a head node selection method for clustering in a wireless sensor network and a wireless sensor network. The wireless sensor network for determining a head node that includes: a first node; and a second node wirelessly being in communication with the first node, wherein the first node broadcasts a final head node message which is a message indicating that the first node itself is a final head node to the second node if the residual energy of the first node is larger than the residual energy of the neighboring nodes. It is therefore possible to minimize clustering process time.