AUID (Application usage identification)

Application usage identification (AUID) is a technique used to identify the applications being used on a particular network. The goal of AUID is to provide a complete understanding of the applications running on the network and to enable network administrators to take appropriate actions based on that understanding.

With the increasing use of cloud services, mobile devices, and remote workers, the traditional network perimeter is becoming less defined. Organizations are struggling to identify the applications that are being used on their networks, which can create security vulnerabilities and other issues. AUID helps to address these issues by providing visibility into the applications that are running on the network.

There are several techniques used for AUID, each with its own strengths and weaknesses. These techniques include port-based identification, signature-based identification, behavior-based identification, and machine learning-based identification. In this article, we will discuss each of these techniques in more detail.

Port-based identification

Port-based identification is the most basic technique used for AUID. It involves identifying the application based on the port number it is using. For example, HTTP traffic typically uses port 80, while HTTPS traffic typically uses port 443. By analyzing the port numbers being used on the network, network administrators can identify the applications that are running.

However, port-based identification has several limitations. First, many applications use non-standard port numbers, which makes it difficult to identify them using this technique. Second, port-based identification cannot identify encrypted traffic, as the port numbers are not visible in encrypted traffic.

Signature-based identification

Signature-based identification involves identifying applications based on their unique characteristics or signatures. Each application has a unique set of characteristics that can be used to identify it, such as the HTTP headers it sends or the user-agent string it uses. By analyzing these characteristics, network administrators can identify the application even if it is using a non-standard port or encrypted traffic.

However, signature-based identification is not foolproof. Some applications may use the same or similar characteristics as other applications, which can lead to false positives or false negatives. Additionally, new applications may not yet have a signature, which can make them difficult to identify using this technique.

Behavior-based identification

Behavior-based identification involves identifying applications based on their behavior. Each application has a unique set of behaviors that can be used to identify it, such as the types of traffic it generates, the frequency of that traffic, and the destinations it communicates with. By analyzing these behaviors, network administrators can identify the application even if it is using a non-standard port, encrypted traffic, or is masquerading as another application.

Behavior-based identification is more advanced than port-based or signature-based identification, as it can identify applications that are trying to hide their identity. However, it can be more complex to implement, as it requires a deep understanding of the behavior of each application on the network.

Machine learning-based identification

Machine learning-based identification involves using machine learning algorithms to identify applications based on a large set of features or characteristics. Machine learning algorithms can be trained on large datasets of network traffic to identify patterns that are unique to each application. Once trained, the algorithm can identify applications in real-time based on the features it has learned.

Machine learning-based identification is the most advanced technique used for AUID, as it can identify applications with a high degree of accuracy and can adapt to new applications as they emerge. However, it can also be the most complex to implement, as it requires a large dataset of network traffic and advanced machine learning skills.

Conclusion

In conclusion, AUID is a critical technique for identifying the applications running on a network. The four techniques discussed in this article – port-based identification, signature-based identification, behavior-based identification, and machine learning-based identification – each have their own strengths and weaknesses. By using a combination of these techniques, network administrators can achieve a more complete understanding of the applications running on their network and take appropriate actions to improve network security and performance.