Data Mining Techniques for Improving Performance of Proxy Servers

Proxy servers are often used to mine data. But data mining techniques are also used in various complicated proxy configurations to improve their performances. These data sets are generally collected from the residential proxy nodes which are monitored and analyzed. However, these techniques are hard to implement and sustain. 

In this article, we will go through data mining techniques in brief and how they can be used to improve the performance of residential proxies. But before that, let’s understand what data mining and proxy servers are. 

What Are Proxy Servers

Used for anonymity, data scraping, content distribution, and security, proxy servers such as residential proxies are configurations that catch and forward your requests through a number of physical or virtual devices. Depending on the requirement, the servers can be different and can be configured exclusively. However, most proxy servers fundamentally are the same. 

What Is Data Mining

In this data-driven world, data mining might be one of the most, if not the most, critical elements for any business succeeding in the online market. Data mining refers to the act of harvesting raw data from sources relevant to the purpose and cleaning them before use. 

Data scraping is an essential part of data mining. In case you don’t have the means to source your own data due to a lack of sources or infrastructure, data scraping is used to harvest data from external sources like competitors’ websites and social media handles. 

However, most external sources don’t want to be scrapped. Therefore, residential proxies are used to keep your requests anonymous and keep you from being blocked by security features. 

Data mining techniques are fundamental methods that are used to retrieve, classify, observe, and identify relations between variables that are relevant to the cause. In the case of proxies, the variables may include time, duration, result codes, bytes, and client address. We’ll try to understand the techniques and how they can be used to improve proxy performance.  

Using Data Mining Techniques 

Classification Analysis

Popularly used in emails to filter spam, classification analysis is employed for extracting relevant information about data and metadata. This analysis method classifies the homogenous data as classes. The classes are unique in their own characteristics. The analyst uses algorithms to determine which data should go in which class. 

Proxy servers are connected through a network of devices that are referred to as nodes. These nodes can be used to collect relevant data about the traffic and client address. Organizations, that use residential proxies for content distribution and cache storage through a residential IP address, use these data to determine which segment of traffic should be redirected to where. 

Classification analysis and appropriate algorithms are used to automate these processes and reduce redirection time—improving proxy performance. However, raw data can’t be used for this purpose. You must clean and organize data before using them for classification analysis and developing the algorithm.

Association Rule

Association rules are generally used in behavior analysis and developing machine learning algorithms. From cart abandonment to store layout, the association rule is used almost everywhere to understand the pattern of customers and push them to make more purchases. The Association rule typically is based on dependency modeling and identifies interesting relations between variables in large datasets. 

In general, the association rule is used in residential proxies to refine the search results and product recommendations. As it’s not practical to expect every user to know the exact term for every element, search results are refined by developing algorithms depending on the association of variables. Moreover, depending on the interests of a user, the algorithms can recommend products to them through the data collected from the proxy servers. 

Anomaly Detection

More than the common patterns of usage, anomalies are critical for online businesses. Fraud detection, intrusion, and other cyber attacks are generally identified through anomalies in the network. These patterns are often analyzed and regulated to mitigate DDoS, phishing, and brute force attacks. Anomaly detection is used to identify these issues. 

Proxies are often used as a buffer between the network servers and the user requests. The security and performance of the proxies can be improved by detecting malicious behavior patterns of single or multiple requests

However, most attacks come in clusters to confuse the security teams. These irregular datasets collected from the proxy nodes are used to analyze the extent of the threat before it can occur. Machine learning algorithms are often used to mitigate them.

Cluster Analysis

Almost similar to the association rule, cluster analysis is the arrangement of the collection of data elements. Creating ideal customer profiles is the main purpose of cluster analysis. The data objects in the same cluster are similar to each other, but they’re rather different from other objects in different clusters.

As organizations often have a few ideal customer profiles that are different from each other in some aspects and in others, cluster analysis is a great way to identify them. 

As with the association rule, proxies are often used with data mining from these nodes to develop algorithms based on cluster analysis. Marketers and developers often use these datasets to present a personalized experience to different customer segments. It improves the performance of the residential proxies in a way that generates higher ROI and market share. 

Regression Analysis

When a variable is dependent on another, but not vice versa, regression analysis is used on available data to determine the characteristic of the dependent variables. As busy networks often see spikes and dips depending on the usage pattern, algorithms are developed to tweak variables according to them with regression analysis. 

As these variable changes can be dangerous to the servers if not adjusted before they can hit the servers, proxy servers are used to buffer the requests. Regression analysis and machine learning algorithms are used on the proxy nodes that buffer these requests to make changes to the servers before the requests can stress them. 

The Bottom Line

Classification analysis, association rule, anomaly detection, cluster analysis, and regression analysis are used in residential proxies to improve their performance. However, you need to develop algorithms to analyze these datasets to make them work.