Measuring internet activity: a (selective) review of methods and metrics

Two Decades after the birth of the World Wide Web, more than two billion people around the world are Internet users. The digital landscape is littered with hints that the affordances of digital communications are being leveraged to transform life in profound and important ways. The reach and influence of digitally mediated activity grow by the day and touch upon all aspects of life, from health, education, and commerce to religion and governance. This trend demands that we seek answers to the biggest questions about how digitally mediated communication changes society and the role of different policies in helping or hindering the beneficial aspects of these changes. Yet despite the profusion of data the digital age has brought upon us—we now have access to a flood of information about the movements, relationships, purchasing decisions, interests, and intimate thoughts of people around the world—the distance between the great questions of the digital age and our understanding of the impact of digital communications on society remains large. A number of ongoing policy questions have emerged that beg for better empirical data and analyses upon which to base wider and more insightful perspectives on the mechanics of social, economic, and political life online. This paper seeks to describe the conceptual and practical impediments to measuring and understanding digital activity and highlights a sample of the many efforts to fill the gap between our incomplete understanding of digital life and the formidable policy questions related to developing a vibrant and healthy Internet that serves the public interest and contributes to human wellbeing. Our primary focus is on efforts to measure Internet activity, as we believe obtaining robust, accurate data is a necessary and valuable first step that will lead us closer to answering the vitally important questions of the digital realm. Even this step is challenging: the Internet is difficult to measure and monitor, and there is no simple aggregate measure of Internet activity—no GDP, no HDI. In the following section we present a framework for assessing efforts to document digital activity. The next three sections offer a summary and description of many of the ongoing projects that document digital activity, with two final sections devoted to discussion and conclusions

A Survey Of IPv6 Address Usage In The Public Domain Name System

The IPv6 protocol has been slowly increasing in use on the Internet. The main reason for the development of the protocol is that the address space provided by IPv4 is nearing exhaustion. The pool of addresses provided by IPv6 is 296 times larger than IPv4, and should be sufficient to provide an address for every device for the foreseeable future. Another potential advantage of this significantly large address space is the use of randomly assigned addresses as a security barrier as part of a defence in depth strategy. This research examined the addresses allocated by those implementing IPv6 to determine what method or pattern of allocation was being used by adopters of the protocol. This examination was done through the use of DNS queries of the AAAA IPv6 host record using public DNS servers. It was observed that 55.84% of IPv6 addresses were in the range of 0 to (232 − 1). For those addresses with unique interface identifier (IID) portions, a nearly equal number of sequential and random IIDs were observed. Hong Kong and Germany were found to have the greatest number of IPv6 addresses. These results suggest that adopters are allocating most addresses sequentially, meaning that no security advantage is being obtained. It is unclear as to whether this is through design or the following of accepted practice. Future research will continue to survey the IPv6 address space to determine whether the patterns observed here remain constant

QuLa: service selection and forwarding table population in service-centric networking using real-life topologies

The amount of services located in the network has drastically increased over the last decade which is why more and more datacenters are located at the network edge, closer to the users. In the current Internet it is up to the client to select a destination using a resolution service (Domain Name System, Content Delivery Networks ...). In the last few years, research on Information-Centric Networking (ICN) suggests to put this selection responsibility at the network components; routers find the closest copy of a content object using the content name as input. We extend the principle of ICN to services; service routers forward requests to service instances located in datacenters spread across the network edge. To solve this problem, we first present a service selection algorithm based on both server and network metrics. Next, we describe a method to reduce the state required in service routers while minimizing the performance loss caused by this data reduction. Simulation results based on real-life networks show that we are able to find a near-optimal load distribution with only minimal state required in the service routers

The Growing Complexity of Internet Interconnection

End-to-End (E2E) packet delivery in the Internet is achieved through a system of interconnections between heterogeneous entities called Autonomous Systems (ASes). The initial pattern of AS interconnection in the Internet was relatively simple, involving mainly ISPs with a balanced mixture of inbound and outbound traffic. Changing market conditions and industrial organization of the Internet have jointly forced interconnections and associated contracts to become significantly more diverse and complex. The diversity of interconnection contracts is significant because efficient allocation of costs and revenues across the Internet value chain impacts the profitability of the industry. Not surprisingly, the challenges of recovering the fixed and usage-sensitive costs of network transport give rise to more complex settlements mechanisms than the simple bifurcated (transit and peering) model described in many earlier analyses of Internet interconnection (see BESEN et al., 2001; GREENSTEIN, 2005; or LAFFONT et al., 2003). In the following, we provide insight into recent operational developments, explaining why interconnection in the Internet has become more complex, the nature of interconnection bargaining processes, the implications for cost/revenue allocation and hence interconnection incentives, and what this means for public policy. This paper offers an abbreviated version of the original paper (see FARATIN et al., 2007b).internet interconnection, economics, public policy, routing, peering.

Survey of End-to-End Mobile Network Measurement Testbeds, Tools, and Services

Mobile (cellular) networks enable innovation, but can also stifle it and lead to user frustration when network performance falls below expectations. As mobile networks become the predominant method of Internet access, developer, research, network operator, and regulatory communities have taken an increased interest in measuring end-to-end mobile network performance to, among other goals, minimize negative impact on application responsiveness. In this survey we examine current approaches to end-to-end mobile network performance measurement, diagnosis, and application prototyping. We compare available tools and their shortcomings with respect to the needs of researchers, developers, regulators, and the public. We intend for this survey to provide a comprehensive view of currently active efforts and some auspicious directions for future work in mobile network measurement and mobile application performance evaluation.Comment: Submitted to IEEE Communications Surveys and Tutorials. arXiv does not format the URL references correctly. For a correctly formatted version of this paper go to http://www.cs.montana.edu/mwittie/publications/Goel14Survey.pd

Machine Learning DDoS Detection for Consumer Internet of Things Devices

An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g. limited number of endpoints and regular time intervals between packets) to inform feature selection can result in high accuracy DDoS detection in IoT network traffic with a variety of machine learning algorithms, including neural networks. These results indicate that home gateway routers or other network middleboxes could automatically detect local IoT device sources of DDoS attacks using low-cost machine learning algorithms and traffic data that is flow-based and protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep Learning and Security (DLS '18