There is no doubt that we have entered the era of big data. Human productive life produces a lot of data every day, and it produces more and more rapidly. According to IDC and EMC's joint survey, the total global data will reach 40ZB by 2020. At present, the security data in the network and information security area also has the characteristics of big data, including the data volume is increasing, the speed is more and more fast, the variety is more and more. The rapid expansion of the number, speed and type of security data leads to not only the problem of the fusion, storage and management of massive heterogeneous data, but even the traditional security analysis system and methods. Most of the current security analysis tools and methods are designed for small data volumes and are unsustainable in the face of large data volumes. New attacks have emerged, with more data to be detected and the existing analysis technology overwhelmed. How can we perceive the network security posture more quickly in the face of the security element information of the day quantity? Traditional analysis methods mostly adopt rules and features based analysis engine, must have rule library and feature library to work, while rules and features can only describe known attacks and threats, do not recognize unknown attacks or are not yet described as regular attacks and threats. In the face of unknown attacks and complex attacks such as apt, more effective analytical methods and techniques are needed. How do you know the unknown? We need a more proactive, smarter approach to analytics. In the face of the security data of the day, the traditional centralized security analysis platform (such as Siem, security management platform, etc.) also encountered a number of bottlenecks, including outstanding performance problems, limited analytical capacity, lack of active, intelligent analysis means, not the ability to secure data mining day, it is difficult to identify the changeable, unknown security problems. In order to meet the above challenges, as the leading manufacturers of information security star, relying on more than more than 10 years in the field of information security analysis accumulated rich experience and leading technology, in the domestic first launched with independent intellectual property rights of the Venus Chen Tai TM Big Data security analysis platform. The platform helps customers realize the security attacks and threats that traditional security products cannot detect by means of various analytical methods and technologies such as the popular correlation analysis, machine learning, mathematical statistics, real-time analysis, historical analysis and human-computer interaction in the expanding heterogeneous mass data such as events, streams, network raw traffic, files, etc. So as to further protect the customer's information is not destroyed, to protect the customer's business security and stable operation, to achieve the core strategy for customers to create value. Reprint: http://www.d1net.com/bigdata/news/318126.html
The Big Data era requires a new security analytics platform-reproduced
