One challenge in understanding the evolution of the Internet infrastructure is the lack of systematic mechanisms for monitoring the extent to which allocated IP addresses are actually used. In this paper, we advance the science of inferring IPv4 address space utilization by proposing a novel taxonomy and analyzing and correlating results obtained through different types of measurements. We have previously studied an approach based on passive measurements that can reveal used portions of the address space unseen by active approaches. In this paper, we study such passive approaches in detail, extending our methodology to new types of vantage points and identifying traffic components that most significantly contribute to discovering used IPv4 network blocks. We then combine the results we obtained through passive measurements together with data from active measurement studies, as well as measurements from Border Gateway Protocol and additional data sets available to researchers. Through the analysis of this large collection of heterogeneous data sets, we substantially improve the state of the art in terms of: 1) understanding the challenges and opportunities in using passive and active techniques to study address utilization and 2) knowledge of the utilization of the IPv4 space.

Internet background radiation (IBR) is a very interesting piece of Internet traffic as it is the result of attacks and misconfigurations. Previous work has primarily analyzed IBR traffic to large unused IP address blocks called network telescopes. In this work, we build new techniques for monitoring one-way traffic in live networks with the main goals of 1) expanding our understanding of this interesting type of traffic towards live networks as well as of 2) making it useful for detecting and analyzing the impact of outages. Our first contribution is a classification scheme for dissecting one-way traffic into useful classes, including one-way traffic due to unreachable services, scanning, peer-to-peer applications, and backscatter. Our classification scheme is helpful for monitoring IBR traffic in live networks solely based on flow level data. After thoroughly validating our classifier, we use it to analyze a massive data-set that covers 7.41 petabytes of traffic from a large backbone network to shed light into the composition of one-way traffic. We find that the main sources of one-way traffic are malicious scanning, peer-to-peer applications, and outages. In addition, we report a number of interesting observations including that one-way traffic makes a very large fraction, i.e., between 34% and 67%, of the total number of flows to the monitored network, although it only accounts for only 3.4% of the number of packets, which suggests a new conceptual model for Internet traffic in which IBR is dominant in terms of flows. Finally, we demonstrate the utility of one-way traffic of the particularly interesting class of unreachable services for monitoring network and service outages by analyzing the impact of interesting events we detected in the network of our university.

In this work we analyze a massive data-set that captures 5.23 petabytes of traffic to shed light into the composition of one-way traffic towards a large network based on a novel one-way traffic classifier. We find that one-way traffic makes a very large fraction of all traffic in terms of flows, it can be primarily attributed to malicious causes, and it has declined since 2004 because of relative decrease of scan traffic. In addition, we show how our classifier is useful for detecting network outages.

Gaining an overview of host activities is hard when a host is busily exchanging hundreds or thousands of flows over a network. This makes investigating traffic of a suspicious host a tedious task for a security analyst. We propose a novel host traffic visualization technique that reduces this cognitive burden by i) representing traffic through an annotated k-partite graph reflecting familiar Berkeley socket model semantics, ii) employing a host role summarization for effective removal of ephemeral traffic features, and iii) providing classification and filtering techniques for unwanted traffic, which are important for identifying the functional role of port numbers and for visualization. We present the open-source tool HAPviewer and demonstrate how it can visualize a large number of flows through a compact and easily interpretable graph.