API Security in Cloud Native Applications

Indigenous cloud applications, based on microservice architecture, interact by sending requests or feedback through APIs. API communication plays an essential role in the integration of native cloud applications. Therefore, API security is an important part of traditional application security. The API-related security issues shown below directly impact the security of native cloud applications: What API service is featured on this system? What is an API implementation? Does all this call for the co-operation of essential services to address the need? Is there API testing or API abuse? Is there a service denial attack against the service? How can you identify unusual deviant requests among a large number of standard requesting services?

To ensure API security for applications under the traditional cloud structure, we can use API monitoring and protection throughout the API lifecycle system, including API identification, API risk detection, API complaint tracking, and unusual API appeal monitoring -API.

API. Application Programming Interface, software development tool, information technology and business concept on virtual screen. Robotic hand touching digital interface. AI. Vector illustration

API Identification and Discovery

APIs are often requested among complex and comprehensive microservices. To ensure the safety of these APIs, we need to first identify the APIs for the following:

  • What APIs are displayed by services throughout the system?
  • How are the APIs designed, including which API URL and which issues the APIs used?
  • How are APIs verified?

To answer these questions, we can classify APIs as a type of security asset by integrating them with pods, services, applications, and employers.

API Analysis and Assessment

We need to perform a security analysis and evaluate all available API data, including API design risk assessments, service concept security, and compliance testing.

To assess risk, we may use traditional API security solutions or API security detection tools such as 42Crunch or API security scanning products to detect encrypted connections, authentication and authentication, parameter testing, and feedback content.

Acquisition of service concept compliance involves the following:

  • That additional APIs other than those required for access to the service are displayed.
  • Whether these disclosed APIs help increases the attack area.
  • Whether logic control is performed on the network side.

For example, the service should only access a specific GET API for help but access the POST API, PUT API, etc. The L7 firewall is usually installed in the network layer for compliance and analysis application layer to prevent such issues.

API Invocation Monitoring

The above identification of the “assets” API and the vulnerability of the API, and the compliance findings can be seen as a consistent analysis of the security of individual APIs. Based on static analysis, we will collect API requests among all resources across the system.

With the identification of the API, we will continue to monitor and compile the API pleading behavior. During the identification phase, we will determine which APIs each service provides and what risks are present in each API. In contrast, our monitoring component, such as distributed tracking, is based on a complete application process, i.e., tracking, focusing on which API pleading series is requested and any tracking request.

API Behavior Pattern Learning

After collecting API pleading data, the distributed tracking system will process data to select appropriate fields and use them as learning tools or statistical methods to analyze historical data to train API pleading methods in a business plan. Including API pleading chain, time, time of response, and the limits of persuasion. Finally, the program will generate a standard profile of the API pleading behavior and signatures consistent with the standard business plan behavior.

According to our previous experience, most of the many behaviors in a business plan are common, and only a few are uncommon. During the training process, we will evaluate the training results according to the expertise and adjust the parameters of the training model accordingly.

API Anomaly Detection

At this point, with the general details of the business plan’s API behavior on hand, we will match the current data against the data in the signature field by retrieving and applying the sequence calculation sequence method to find the best match.

The discovery engine will compare the similarities between the signatures and the current data with the original base. If the deviation falls at the base level, the API code of conduct is considered normal; otherwise, it is rare.

When it comes to the basic foundation, we need to define the appropriate foundation in terms of expertise, adapted to various situations.

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