Back to blog

A Study of the Best Residential Proxies Service Dataset (Series 3)

Keywords: Residential Proxies, IP Classifiers, Penetration Framework, Dataset Analysis

Residential Proxy have become an increasing research concern in the field of Internet security and privacy. How to maintain anonymity, web crawling, and even market research through Residential proxy has become a hot topic. In this article, the author will explore the research methodology of residential proxies and its dataset analysis in detail, showing the whole process from penetration framework to IP classifier.

1. Establishment and application of penetration framework

Keywords: penetration framework, web crawler, DNS server

In the study of residential proxies, the first thing to be mentioned is the application of penetration framework. Penetration framework is an advanced technique that consists of three main components: client, target server, and DNS server. The client is usually the one that sends labeled requests to the target site through the residential proxy service, using a web crawler tool. The target server, on the other hand, is the site that receives these requests, while the DNS server is used to determine whether DNS resolution is performed on the residential proxy host or on the proxy gateway. This sophisticated framework not only helped the author capture and analyze traffic, but also revealed the complex operational mechanisms within the residential proxy service.

During the research process, the author discovered 17 different residential proxy services through search engines and black hat SEO forums, and selected five services for in-depth study based on their size, service model, popularity, and time of discovery. To ensure the accuracy and reliability of the data, the author regularly accessed pre-registered servers through these services and logged all labeled requests. In this way, the author was able to identify the residential address of the server and further analyze the characteristics of these IPs.

2. construction and optimization of residential classifier

Keywords: residential IP classifier, feature selection, dataset

Determining whether an address belongs to a residential network is a complex task. Although commercial services can provide labeled queries for IPs, scalability and reliability over large datasets are still problematic. For this reason, the author develops a new residential IP classifier, which is based on a unique set of features that can accurately distinguish residential IPs from non-residential IPs.

In order to construct this classifier, the author first needs to obtain the labeling dataset. The author successfully collected widely distributed residential IP data through personal devices, using device search engines (e.g., Shodan, Zoomeye), and Trace My IP query logs. These data provide a solid foundation for the author's subsequent feature selection and classifier training.

In feature selection, the author focuses on features related to IP Whois records or active DNS records. Compared with non-residential IPs, residential IPs are usually directly assigned and managed by ISPs, and the IP blocks are relatively stable. By analyzing these features, the author's classifier performs well in 5-fold cross-validation with 95.61% accuracy and 97.12% recall.

3. Result Analysis and Evaluation

Keywords: result evaluation, classifier accuracy, residential IP detection

During the research process, the author captured a large number of different residential IPs, which provided a rich data base for the author's study. When analyzing this data, the author found that about 95.22% of the IPs were identified as residential IPs, while 4.78% were non-residential IPs.

Through manual validation and sampling analysis, the author's findings show that the classifier's predictions are highly consistent with the nature of the dataset, with particularly strong performance on the unlabeled dataset. Notably, when applying the classifier to 6.2M residential proxies IPs, it exhibits extremely high accuracy, further demonstrating the validity of the author's research methodology.

4. Technical challenges of penetration and analysis

Keywords: technical challenges, network security, penetration strategies

Throughout the research process, the author faced the challenge of avoiding detection by residential proxies services. To do so, the author employed several strategies, including deploying crawlers and target servers in different geographic locations, encrypting communication traffic, and dealing with the complexity of multiple gateways. Through these measures, the author succeeded in obtaining a large amount of accurate data and laid the foundation for subsequent analysis.

5. Practical applications and prospects of the research

Keywords: application prospect, network privacy, IP classification

With the increase of network privacy and security needs, the research on residential proxies has a wide range of application prospects. The author's research results can not only help enterprises better manage their network traffic, but also further optimize the author's classifier and penetration framework, and these techniques will provide more possibilities for future network research

Conclusion

Keywords: residential proxies research, classifier, dataset analysis

In the study of residential proxies, the author reveals the internal operation mechanism of residential proxy services by establishing a penetration framework, constructing a residential IP classifier and conducting a large-scale dataset analysis. These studies not only improve the author's understanding of residential proxies services, but also provide new directions for future research on online privacy and security. Through in-depth analysis and continuous optimization, the author believes that these techniques will play an important role in ensuring network security.

Through this paper, the author not only discusses the core technologies of residential proxies in depth, but also demonstrates their wide application in the field of network security.