The Domain Name Service (DNS) is a central point in the functioning of the internet. Just as organizations use domain names to enable the access to their computational services, malicious actors make use of domain names to point to the services under their control. Distinguishing between non-malicious and malicious domain names is extremely important, as it allows to grant or block the access to external services, maximizing the security of the organization and users. Nowadays there are many DNS firewall solutions. Most of these are based on known malicious domain lists that are being constantly updated. However, in this way, it is only possible to block known malicious communications, leaving out many others that can be malicious but are not known. Adopting machine learning to classify domains contributes to the detection of domains that are not yet on the block list. The dataset described in this manuscript is meant for supervised machine learning-based analysis of malicious and non-malicious domain names. The dataset was created from scratch, using publicly DNS logs of both malicious and non-malicious domain names. Using the domain name as input, 34 features were obtained. Features like the domain name entropy, number of strange characters and domain name length were obtained directly from the domain name. Other features like, domain name creation date, Internet Protocol (IP), open ports, geolocation were obtained from data enrichment processes (e.g. Open Source Intelligence (OSINT)). The class was determined considering the data source (malicious DNS log files and non-malicious DNS log files). The dataset consists of data from approximately 90000 domain names and it is balanced between 50% non-malicious and 50% of malicious domain names.

Nowadays there are many DNS firewall solutions to prevent users accessing malicious domains. These can provide real-time protection and block illegitimate communications, contributing to the cybersecurity posture of the organizations. Most of these solutions are based on known malicious domain lists that are being constantly updated. However, in this way, it is only possible to block malicious communications for known malicious domains, leaving out many others that are malicious but have not yet been updated in the blocklists. This work provides a study to implement a DNS firewall solution based on ML and so improve the detection of malicious domain requests on the fly. For this purpose, a dataset with 34 features and 90 k records was created based on real DNS logs. The data were enriched using OSINT sources. Exploratory analysis and data preparation steps were carried out, and the final dataset submitted to different Supervised ML algorithms to accurately and quickly classify if a domain request is malicious or not. The results show that the ML algorithms were able to classify the benign and malicious domains with accuracy rates between 89% and 96%, and with a classification time between 0.01 and 3.37 s. The contributions of this study are twofold. In terms of research, a dataset was made public and the methodology can be used by other researchers. In terms of solution, the work provides the baseline to implement an in band DNS firewall.