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Darknets are ranges of IP addresses advertised without answering any traffic. Darknets help to uncover interesting network events, such as misconfigurations and network scans. Interpreting darknet traffic helps against cyber-attacks -e.g., malware often reaches darknets when scanning the Internet for vulnerable devices. The traffic reaching darknets is however voluminous and noisy, which calls for efficient ways to represent the data and highlight possibly important events. This paper evaluates a methodology to summarize packets reaching darknets. We represent the darknet activity as a graph, which captures remote hosts contacting the darknet nodes ports, as well as the frequency at which each port is reached. From these representations, we apply community detection algorithms in the search for patterns that could represent coordinated activity. By highlighting such activities we are able to group together, for example, groups of IP addresses that predominantly engage in contacting specific targets, or, vice versa, to identify targets which are frequently contacted together, for exploiting the vulnerabilities of a given service. The network analyst can recognize from the community detection results, for example, that a group of hosts has been infected by a botnet and it is currently scanning the network in search of vulnerable services (e.g., SSH and Telnet among the most commonly targeted). Such piece of information is impossible to obtain when analyzing the behavior of single sources, or packets one by one. All in all, our work is a first step towards a comprehensive aggregation methodology to automate the analysis of darknet traffic, a fundamental aspect for the recognition of coordinated and anomalous events.

Darknets are sets of IP addresses that are advertised but do not host any client or server. By passively recording the incoming packets, they assist network monitoring activities. Since packets they receive are unsolicited by definition, darknets help to spot misconfigurations as well as important security events, such as the appearance and spread of botnets, DDoS attacks using spoofed IP address, etc. A number of organizations worldwide deploys darknets, ranging from a few dozens of IP addresses to large /8 networks. We here investigate how similar is the visibility of different darknets. By relying on traffic from three darknets deployed in different contintents, we evaluate their exposure in terms of observed events given their allocated IP addresses. The latter is particularly relevant considering the shortage of IPv4 addresses on the Internet. Our results suggest that some well-known facts about darknet visibility seem invariant across deployments, such as the most commonly contacted ports. However, size and location matter. We find significant differences in the observed traffic from darknets deployed in different IP ranges as well as according to the size of the IP range allocated for the monitoring.

Knowing domain names associated with traffic allows eavesdroppers to profile users without accessing packet payloads. Encrypting domain names transiting the network is, therefore, a key step to increase network confidentiality. Latest efforts include encrypting the TLS Server Name Indication (eSNI extension) and encrypting DNS traffic, with DNS over HTTPS (DoH) representing a prominent proposal. In this paper, we show that an attacker able to observe users' traffic relying on plain-text DNS can uncover the domain names of users relying on eSNI or DoH. By relying on large-scale network traces, we show that simplistic features and off-the-shelf machine learning models are sufficient to achieve surprisingly high precision and recall when recovering encrypted domain names. The triviality of the attack calls for further actions to protect privacy, in particular considering transient scenarios in which only a fraction of users will adopt these new privacy-enhancing technologies.

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