Cloud Computing Security
Leadsec is committed to be the security expert for cloud computing users, and it uses the network security strategy planning technology to design cloud computing deep-defense system, and create a secure, compliant, trusted cloud computing operating environment.
Secure Big Data
Enterprises or organizations have deployed a large number of security detection devices, protection devices, and auditing devices. These hardware and software deployed in bypass, in series, or on servers and terminals continuously generate various kinds of audit data like alarm data and original operations, which cannot be stored in the data devices for a long time. Many enterprises require direct collection and storage of data such as logs and services for key protection systems or services. In recent years, many enterprises or organizations have collected, stored, and analyzed raw data after parsed by the stream protocol, such as DNS and Http access with large amount.
Industrial Internet of Things (Warehouse Logistics Industry) Security Solutions
In the overall network architecture of a warehousing company, the application of industrial IoT mainly includes material management system at the platform level, networking technologies such as WiFi, ZigBee and 4G-LTE at the network level, and operating devices like various sensors, intelligent terminals and robots at the sensing level. In this scenario, the attack surface is extensive, and many attack paths pose challenges to the security scope and protection grit of the Internet of Things network. In addition, the intelligent
Mobile Internet Security
In the background of the information age, the mobile Internet has developed rapidly, and mobile applications have stepped into the national era. At the same time, the State vigorously promotes the innovation driven development strategy, and mobile applications are widely used in all walks of life. As people are more and more dependent on the mobile Internet, the security problems of mobile applications are becoming more and more obvious, which not only restrict the healthy development of the mobile Internet to a large extent and but also bring great harm to the social public's study, life and work such as disclosure of enterprise internal data or privacy information, malicious theft of sensitive data, loss of user volume or economic property. Solutions: Based on the "one center, three layers of defense" idea in the Classified Protection of Cybersecurity, a mobile application security management center should be set up to carry out the mobile application security trend prediction and situational awareness by reasonably constructing the three-dimensional active early warning and defense capability through intelligent analysis, with the integration of “cloud-management-terminal" data by virtue of the big data technology, to form three layers of defense, namely detection, reinforcement and protection. Through the full-life defense and guarantee system of mobile applications, the "centralized security protection", "centralized security management and control" and "centralized security operation" of mobile applications form a closed-loop of data through the "cloud-management-terminal" scheme to promote the improvement of the security level and management level of mobile applications in the form of rolling evolution.
Application of Artificial intelligence Technologies
The IDS construction methods based on anomalies can be divided into three major categories, namely, supervised, semi-supervised and unsupervised. Of the three, the first two categories need to have the annotation data as the basis, but it is usually unrealistic to obtain enough annotation data in the actual production environment. Even for semi-supervised methods that only require normal behavioral data, it is still a very difficult task to acquire or construct various normal behavioral instance data that reflect the real scene. In contrast, unsupervised methods do not require any annotation data and are therefore more feasible in practice. The clustering and abnormal point detection algorithms are used to analyze the unlabeled traffic data, so as to directly detect and identify the abnormal behavior contained therein. Specifically, in the system, we first use the Spark distributed computing processing framework to preprocess and extract the traffic data, and then use the rich clustering and anomaly detection algorithms provided by ELKI for analysis, and finally, with the excellent drawing of the R platform, a visual representation of the detection recognition results could be presented.