IGLOO's Press

This is the latest news on
IGLOO SECURITY

This is the latest news on IGLOO SECURITY, Inc.
having been reported in press.

This is the latest news on IGLOO SECURITY, Inc. having been reported in press.

IGLOO's Press Details
IGLOO SECURITY Co., Ltd. Acquired 6 patents related to AI and Security Monitoring 2020.04.28 70

IGLOO SECURITY Co., Ltd. Acquired 6 patents related to AI and Security Monitoring

 

 

 

 

- IGLOO SECURITY acquires AI and Security monitoring related patents. Reinforcing position as a leading in AI and Cyber Security company in Korea.

- The AI will be applied to SPiDER TM AI Edition, the first AI security monitoring solution in Korea.

 

 

[Apr. 28, 2020] IGLOO SECURITY secures its position as a leader in AI security monitoring by acquiring AI and security monitoring patents. IGLOO SECURITY announced that it has completed the registration of six patents related to AI and security monitoring including 'an intelligent security monitoring system and method using a combination of supervised learning-based alert analysis and non-supervised learning-based anomaly detection techniques.'

The patents aimed at minimizing false positives and increasing visibility into security threats that are under cover over the long term with increasing security events. IGLOO SECURITY plans to apply this patented technology to SPiDER TM AI Edition, an AI security monitoring solution.

The patent for 'an intelligent security monitoring system and method using a combination of supervised learning-based alert analysis and non-supervised learning-based anomaly detection techniques.' improves the accuracy of alert prediction and prevents unknown attacks by combining supervised learning and unsupervised learning. By analyzing high-risk events and abnormalities automatically identified by AI algorithms by time flow and attack stages, it will be able to accurately select high-risk events that need to be addressed first and detect new and mutated threats that are difficult to detect with existing security equipment to minimize security gaps.

The patent for 'the artificial intelligence-based security event analysis system and its method through semi-supervised learning' is focusing on minimizing the time required for labeling tasks that give security events the correct answer and improving the accuracy of security policies. 
This is a method of labeling only some of the clustered data and letting the algorithm make judgments on the rest of the data based on the labeled data. The accuracy of the algorithm and security policy is improved through continuous feedback on the results of the supervised learning.

The patent for 'a model selection system and method for unsupervised learning anomaly detection' is a technique to select the best anomaly detection model by predicting and evaluating the learning results provided by the anomaly detection model when a new unlabeled dataset is received based on the criteria for evaluating the anomaly detection model that has learned the dataset labeled with the target value for the input value. 
Through this, it is possible to solve the difficulty of evaluating the results of unsupervised learning, and to increase the visibility of abnormal behaviors that can develop into high-risk threats.

The goal of a patent for 'graph database-based log data similar pattern matching and risk management method' is effectively find a 'frequent pattern' by connecting the vast amount of log data collected from security device according to the direction of the relationship between data and the strength of association according to the characteristics of objects in the data. 
Security agents can respond to attacks by quickly identifying frequent patterns without having to interpret graph data or compare it with other graph data. 

The patent for the 'risk index correction system and method according to the attack frequency, asset importance and vulnerability level' is a technology that automatically corrects the risk index to numerically index the threat level of information assets to increase the accuracy of the risk index value. 
Unlike the existing risk index, which was calculated based on the analysis of threats over a period of time and the discretion of the manager, The index has significantly increased the real-time and reliability of the risk index by calculating risk indices by asset and threat events, asset importance weights, and vulnerability weights, depending on time and situation of attack.

The patent for ‘the event-based security policy real-time optimization system and method' uses a machine learning algorithm to generate and apply the optimized security policy in real time, so that the security officer can analyze the environment to determine the optimal filter value for each security policy.
This is a method in which the machine learning algorithm learns the preprocessed log to create an event-based security policy, derives the optimal filter value regularly, evaluates it, and automatically applies it.

Mr. Lee, the CEO of IGLOO SECURITY, said “As a leading company in AI security monitoring and the research and development of technologies that can improve the effectiveness of security monitoring, It is expected that through the six patent technologies acquired this time, we will be able to enhance our ability to respond to advanced cyber infringement attempts.”​

 

Prev, Next List
Next No Next Article
Prev CEO Lee Deuk Choon of IGLOO SECURITY, INC. Honored with Industry Medal as a Contributor to Science, Technology, and ICT