We briefly survey recent proposals that seek to capture in numerical terms the resilience and the robustness of a graph. After a brief introduction and the establishment of notation and terminology, we catalogue characterizations proposed in journal articles published within the last two decades. We then describe some of the numerous application areas for such characterizations. We experiment with implementations of numerous characteristics on several graph-generation models, after which we conclude with a discussion of open problems and future directions.

Robustness is a desired feature for any backbone network. As a minimum requirement to be considered robust, a network must remain connected after any single node or link failure, so that each node is able to communicate with all other nodes. It turns out that some national optical backbones do not satisfy this condition, the Chilean internet backbone being an example. To solve this problem, it is needed the addition of link(s) to the network, which can usually be done in different ways, leaving room to do that while minimizing other metrics of interest. This paper discusses metrics to evaluate robustness of such networks (specifically, edge betweenness centrality, number of link cutsets and node Wiener impact), and proposes a Variable Neighbourhood Search heuristic to improve it by adding a few well placed links. As a case of study, results are presented for the Chilean internet backbone, considering three and four extra links.

The Domain Name System (DNS) is a critical component of Internet infrastructure, as almost every activity on the Internet starts with a DNS query. Given its importance, there is increasing concern over its vulnerability to attacks and failures, as they can negatively affect all Internet-based resources. Thus, detecting these events is crucial to preserve the correct functioning of all DNS components, such as high-volume name servers for top-level domains (TLD). This article presents a near real-time Anomaly Detection Based on Prediction (AD-BoP) method, providing a useful and easily explainable methodology to effectively detect DNS anomalies. AD-BoP is based on the prediction of expected DNS traffic statistics, and could be especially helpful for TLD registry operators to preserve their services' reliability. After an exhaustive analysis, AD-BoP is shown to improve the current state-of-the-art for anomaly detection in authoritative TLD name servers.

Due to its huge impact on the overall quality of service (QoS) of wireless networks, both academic and industrial research have actively focused on analyzing the received signal strength in areas of particular interest. In this paper, we propose the improvement of signal-strength aggregation with a special focus on Mobile Crowdsourcing scenarios by avoiding common issues related to the mishandling of log-scaled signal values, and by the proposal of a novel aggregation method based on interpolation. Our paper presents two clear contributions. First, we discuss the misuse of log-scaled signal-strength values, which is a persistent problem within the mobile computing community. We present the physical and mathematical formalities on how signal-strength values must be handled in a scientific environment. Second, we present a solution to the difficulties of aggregating signal strength in Mobile Crowdsourcing scenarios, as a low number of measurements and nonuniformity in spatial distribution. Our proposed method obtained consistently lower Root Mean Squared Error (RMSE) values than other commonly used methods at estimating the expected value of signal strength over an area. Both contributions of this paper are important for several recent pieces of research that characterize signal strength for an area of interest.