It can classify a variety of phishing websites, and output the results by type, region, industry and other dimensions.

The domain name is analyzed in the intelligent way of autonomous learning.

1) Research and judge the domain name features: extracts and analyzes domain name semantics and page structure based on page code;

2) Feature training and strengthening: using historical data, unsupervised training and strengthening of domain name features are carried out;

3) Self updating mechanism: the trained features are added to the system by the self updating mechanism for subsequent detection.

Comprehensive, generalized black and white list are supported to meet the needs of different identification research.

Whitelist: it includes not only the domain names of mainstream websites, but also the sub domain names; according to the separation of dynamic and static contents, various images, audio and video domain names of mainstream websites are added to the whitelist.

Blacklist: including illegal and malicious website domain name, ISP service provider, domain name registrant, domain name contact person, IP and geographical location.

Grey list: Based on the access threshold, the grey list is distinguished. Through the domain name characteristics and source sensitive content, the grey list is studied and judged.

1) Intelligent research and judgment

Intelligent research and judgment on the trend of malicious websites, master the global network security situation, and solve the problem of low efficiency of traditional website security detection products.

2) Focus on business

In order to solve the passive problem of traditional website detection and identification, we use machine learning and other technologies to conduct in-depth security examination and perception of traffic URL from the aspects of business attribute and importance. 

3) unified management

Integrating threat intelligence database, unified management of URL features, establishing website threat view, to solvie the problem that malicious website threat information is not commonly used.

4) Support multi-source data

Support IDC data, metrometropolitan area network data, CDN data, Internet log and other data sources. 

5) flexible configurable architecture

Hadoop + streaming processing is taken as the overall big data technology architecture. At the same time, it has the feature of loose coupling in the functional modules. In addition, the product supports local or cloud deployment.

6) Intelligent multilayer filtering

Supports black and white list matching, DGA algorithm detection, domain name feature recognition.

7) Autonomous Learning Mechanism

It has the mechanism of feature extraction, key information research and judgment, feature strengthening training and independent updating.

8) Multidimensional result output

Supports result output by type, industry object and region.