Observations of IPv6 Addresses

IPv6 addresses are longer than IPv4 addresses, and are so capable of greater expression. Given an IPv6 address, conventions and standards allow us to draw conclusions about how IPv6 is being used on the node with that address. We show a technique for analysing IPv6 addresses and apply it to a number of datasets. The datasets include addresses seen at a busy mirror server, at an IPv6-enabled TLD DNS server and when running traceroute across the production IPv6 network. The technique quantifies differences in these datasets that we intuitively expect, and shows that IPv6 is being used in different ways by different groups

Observations of IPv6 Addresses

IPv6 addresses are longer than IPv4 addresses, and are so capable of greater expression. Given an IPv6 address, conventions and standards allow us to draw conclusions about how IPv6 is being used on the node with that address. We show a technique for analysing IPv6 addresses and apply it to a number of datasets. The datasets include addresses seen at a busy mirror server, at an IPv6-enabled TLD DNS server and when running traceroute across the production IPv6 network. The technique quantifies differences in these datasets that we intuitively expect, and shows that IPv6 is being used in different ways by different groups

ROVER: a DNS-based method to detect and prevent IP hijacks

2013 Fall.Includes bibliographical references.The Border Gateway Protocol (BGP) is critical to the global internet infrastructure. Unfortunately BGP routing was designed with limited regard for security. As a result, IP route hijacking has been observed for more than 16 years. Well known incidents include a 2008 hijack of YouTube, loss of connectivity for Australia in February 2012, and an event that partially crippled Google in November 2012. Concern has been escalating as critical national infrastructure is reliant on a secure foundation for the Internet. Disruptions to military, banking, utilities, industry, and commerce can be catastrophic. In this dissertation we propose ROVER (Route Origin VERification System), a novel and practical solution for detecting and preventing origin and sub-prefix hijacks. ROVER exploits the reverse DNS for storing route origin data and provides a fail-safe, best effort approach to authentication. This approach can be used with a variety of operational models including fully dynamic in-line BGP filtering, periodically updated authenticated route filters, and real-time notifications for network operators. Our thesis is that ROVER systems can be deployed by a small number of institutions in an incremental fashion and still effectively thwart origin and sub-prefix IP hijacking despite non-participation by the majority of Autonomous System owners. We then present research results supporting this statement. We evaluate the effectiveness of ROVER using simulations on an Internet scale topology as well as with tests on real operational systems. Analyses include a study of IP hijack propagation patterns, effectiveness of various deployment models, critical mass requirements, and an examination of ROVER resilience and scalability

Mitigating sampling error when measuring internet client IPv6 capabilities

Despite the predicted exhaustion of unallocated IPv4 addresses be- tween 2012 and 2014, it remains unclear how many current clients can use its successor, IPv6, to access the Internet. We propose a refinement of previous measurement studies that mitigates intrin- sic measurement biases, and demonstrate a novel web-based tech- nique using Google ads to perform IPv6 capability testing on a wider range of clients. After applying our sampling error reduction, we find that 6% of world-wide connections are from IPv6-capable clients, but only 1–2% of connections preferred IPv6 in dual-stack (dual-stack failure rates less than 1%). Except for an uptick around IPv6-day 2011 these proportions were relatively constant, while the percentage of connections with IPv6-capable DNS resolvers has in- creased to nearly 60%. The percentage of connections from clients with native IPv6 using happy eyeballs has risen to over 20

A Macroscopic Study of Network Security Threats at the Organizational Level.

Defenders of today's network are confronted with a large number of malicious activities such as spam, malware, and denial-of-service attacks. Although many studies have been performed on how to mitigate security threats, the interaction between attackers and defenders is like a game of Whac-a-Mole, in which the security community is chasing after attackers rather than helping defenders to build systematic defensive solutions. As a complement to these studies that focus on attackers or end hosts, this thesis studies security threats from the perspective of the organization, the central authority that manages and defends a group of end hosts. This perspective provides a balanced position to understand security problems and to deploy and evaluate defensive solutions. This thesis explores how a macroscopic view of network security from an organization's perspective can be formed to help measure, understand, and mitigate security threats. To realize this goal, we bring together a broad collection of reputation blacklists. We first measure the properties of the malicious sources identified by these blacklists and their impact on an organization. We then aggregate the malicious sources to Internet organizations and characterize the maliciousness of organizations and their evolution over a period of two and half years. Next, we aim to understand the cause of different maliciousness levels in different organizations. By examining the relationship between eight security mismanagement symptoms and the maliciousness of organizations, we find a strong positive correlation between mismanagement and maliciousness. Lastly, motivated by the observation that there are organizations that have a significant fraction of their IP addresses involved in malicious activities, we evaluate the tradeoff of one type of mitigation solution at the organization level --- network takedowns.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116714/1/jingzj_1.pd

Network Traffic Measurements, Applications to Internet Services and Security

The Internet has become along the years a pervasive network interconnecting billions of users and is now playing the role of collector for a multitude of tasks, ranging from professional activities to personal interactions. From a technical standpoint, novel architectures, e.g., cloud-based services and content delivery networks, innovative devices, e.g., smartphones and connected wearables, and security threats, e.g., DDoS attacks, are posing new challenges in understanding network dynamics. In such complex scenario, network measurements play a central role to guide traffic management, improve network design, and evaluate application requirements. In addition, increasing importance is devoted to the quality of experience provided to final users, which requires thorough investigations on both the transport network and the design of Internet services. In this thesis, we stress the importance of users’ centrality by focusing on the traffic they exchange with the network. To do so, we design methodologies complementing passive and active measurements, as well as post-processing techniques belonging to the machine learning and statistics domains. Traffic exchanged by Internet users can be classified in three macro-groups: (i) Outbound, produced by users’ devices and pushed to the network; (ii) unsolicited, part of malicious attacks threatening users’ security; and (iii) inbound, directed to users’ devices and retrieved from remote servers. For each of the above categories, we address specific research topics consisting in the benchmarking of personal cloud storage services, the automatic identification of Internet threats, and the assessment of quality of experience in the Web domain, respectively. Results comprise several contributions in the scope of each research topic. In short, they shed light on (i) the interplay among design choices of cloud storage services, which severely impact the performance provided to end users; (ii) the feasibility of designing a general purpose classifier to detect malicious attacks, without chasing threat specificities; and (iii) the relevance of appropriate means to evaluate the perceived quality of Web pages delivery, strengthening the need of users’ feedbacks for a factual assessment

Machine Learning and Big Data Methodologies for Network Traffic Monitoring

Over the past 20 years, the Internet saw an exponential grown of traffic, users, services and applications. Currently, it is estimated that the Internet is used everyday by more than 3.6 billions users, who generate 20 TB of traffic per second. Such a huge amount of data challenge network managers and analysts to understand how the network is performing, how users are accessing resources, how to properly control and manage the infrastructure, and how to detect possible threats. Along with mathematical, statistical, and set theory methodologies machine learning and big data approaches have emerged to build systems that aim at automatically extracting information from the raw data that the network monitoring infrastructures offer. In this thesis I will address different network monitoring solutions, evaluating several methodologies and scenarios. I will show how following a common workflow, it is possible to exploit mathematical, statistical, set theory, and machine learning methodologies to extract meaningful information from the raw data. Particular attention will be given to machine learning and big data methodologies such as DBSCAN, and the Apache Spark big data framework. The results show that despite being able to take advantage of mathematical, statistical, and set theory tools to characterize a problem, machine learning methodologies are very useful to discover hidden information about the raw data. Using DBSCAN clustering algorithm, I will show how to use YouLighter, an unsupervised methodology to group caches serving YouTube traffic into edge-nodes, and latter by using the notion of Pattern Dissimilarity, how to identify changes in their usage over time. By using YouLighter over 10-month long races, I will pinpoint sudden changes in the YouTube edge-nodes usage, changes that also impair the end users’ Quality of Experience. I will also apply DBSCAN in the deployment of SeLINA, a self-tuning tool implemented in the Apache Spark big data framework to autonomously extract knowledge from network traffic measurements. By using SeLINA, I will show how to automatically detect the changes of the YouTube CDN previously highlighted by YouLighter. Along with these machine learning studies, I will show how to use mathematical and set theory methodologies to investigate the browsing habits of Internauts. By using a two weeks dataset, I will show how over this period, the Internauts continue discovering new websites. Moreover, I will show that by using only DNS information to build a profile, it is hard to build a reliable profiler. Instead, by exploiting mathematical and statistical tools, I will show how to characterize Anycast-enabled CDNs (A-CDNs). I will show that A-CDNs are widely used either for stateless and stateful services. That A-CDNs are quite popular, as, more than 50% of web users contact an A-CDN every day. And that, stateful services, can benefit of A-CDNs, since their paths are very stable over time, as demonstrated by the presence of only a few anomalies in their Round Trip Time. Finally, I will conclude by showing how I used BGPStream an open-source software framework for the analysis of both historical and real-time Border Gateway Protocol (BGP) measurement data. By using BGPStream in real-time mode I will show how I detected a Multiple Origin AS (MOAS) event, and how I studies the black-holing community propagation, showing the effect of this community in the network. Then, by using BGPStream in historical mode, and the Apache Spark big data framework over 16 years of data, I will show different results such as the continuous growth of IPv4 prefixes, and the growth of MOAS events over time. All these studies have the aim of showing how monitoring is a fundamental task in different scenarios. In particular, highlighting the importance of machine learning and of big data methodologies

Methods for revealing and reshaping the African Internet Ecosystem as a case study for developing regions: from isolated networks to a connected continent

Mención Internacional en el título de doctorWhile connecting end-users worldwide, the Internet increasingly promotes local development by making challenges much simpler to overcome, regardless of the field in which it is used: governance, economy, education, health, etc. However, African Network Information Centre (AfriNIC), the Regional Internet Registry (RIR) of Africa, is characterized by the lowest Internet penetration: 28.6% as of March 2017 compared to an average of 49.7% worldwide according to the International Telecommunication Union (ITU) estimates [139]. Moreover, end-users experience a poor Quality of Service (QoS) provided at high costs. It is thus of interest to enlarge the Internet footprint in such under-connected regions and determine where the situation can be improved. Along these lines, this doctoral thesis thoroughly inspects, using both active and passive data analysis, the critical aspects of the African Internet ecosystem and outlines the milestones of a methodology that could be adopted for achieving similar purposes in other developing regions. The thesis first presents our efforts to help build measurements infrastructures for alleviating the shortage of a diversified range of Vantage Points (VPs) in the region, as we cannot improve what we can not measure. It then unveils our timely and longitudinal inspection of the African interdomain routing using the enhanced RIPE Atlas measurements infrastructure for filling the lack of knowledge of both IPv4 and IPv6 topologies interconnecting local Internet Service Providers (ISPs). It notably proposes reproducible data analysis techniques suitable for the treatment of any set of similar measurements to infer the behavior of ISPs in the region. The results show a large variety of transit habits, which depend on socio-economic factors such as the language, the currency area, or the geographic location of the country in which the ISP operates. They indicate the prevailing dominance of ISPs based outside Africa for the provision of intracontinental paths, but also shed light on the efforts of stakeholders for traffic localization. Next, the thesis investigates the causes and impacts of congestion in the African IXP substrate, as the prevalence of this endemic phenomenon in local Internet markets may hinder their growth. Towards this end, Ark monitors were deployed at six strategically selected local Internet eXchange Points (IXPs) and used for collecting Time-Sequence Latency Probes (TSLP) measurements during a whole year. The analysis of these datasets reveals no evidence of widespread congestion: only 2.2% of the monitored links experienced noticeable indication of congestion, thus promoting peering. The causes of these events were identified during IXP operator interviews, showing how essential collaboration with stakeholders is to understanding the causes of performance degradations. As part of the Internet Society (ISOC) strategy to allow the Internet community to profile the IXPs of a particular region and monitor their evolution, a route-collector data analyzer was then developed and afterward, it was deployed and tested in AfriNIC. This open source web platform titled the “African” Route-collectors Data Analyzer (ARDA) provides metrics, which picture in real-time the status of interconnection at different levels, using public routing information available at local route-collectors with a peering viewpoint of the Internet. The results highlight that a small proportion of Autonomous System Numbers (ASNs) assigned by AfriNIC (17 %) are peering in the region, a fraction that remained static from April to September 2017 despite the significant growth of IXPs in some countries. They show how ARDA can help detect the impact of a policy on the IXP substrate and help ISPs worldwide identify new interconnection opportunities in Africa, the targeted region. Since broadening the underlying network is not useful without appropriately provisioned services to exploit it, the thesis then delves into the availability and utilization of the web infrastructure serving the continent. Towards this end, a comprehensive measurement methodology is applied to collect data from various sources. A focus on Google reveals that its content infrastructure in Africa is, indeed, expanding; nevertheless, much of its web content is still served from the United States (US) and Europe, although being the most popular content source in many African countries. Further, the same analysis is repeated across top global and regional websites, showing that even top African websites prefer to host their content abroad. Following that, the primary bottlenecks faced by Content Providers (CPs) in the region such as the lack of peering between the networks hosting our probes and poorly configured DNS resolvers are explored to outline proposals for further ISP and CP deployments. Considering the above, an option to enrich connectivity and incentivize CPs to establish a presence in the region is to interconnect ISPs present at isolated IXPs by creating a distributed IXP layout spanning the continent. In this respect, the thesis finally provides a four-step interconnection scheme, which parameterizes socio-economic, geographical, and political factors using public datasets. It demonstrates that this constrained solution doubles the percentage of continental intra-African paths, reduces their length, and drastically decreases the median of their Round Trip Times (RTTs) as well as RTTs to ASes hosting the top 10 global and top 10 regional Alexa websites. We hope that quantitatively demonstrating the benefits of this framework will incentivize ISPs to intensify peering and CPs to increase their presence, for enabling fast, affordable, and available access at the Internet frontier.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: David Fernández Cambronero.- Secretario: Alberto García Martínez.- Vocal: Cristel Pelsse

BGP-Multipath Routing in the Internet

BGP-Multipath, or BGP-M, is a routing technique for balancing traffic load in the Internet. It enables a Border Gateway Protocol (BGP) border router to install multiple ‘equally-good’ paths to a destination prefix. While other multipath routing techniques are deployed at internal routers, BGP-M is deployed at border routers where traffic is shared on multiple border links between Autonomous Systems (ASes). Although there are a considerable number of research efforts on multipath routing, there is so far no dedicated measurement or study on BGP-M in the literature. This thesis presents the first systematic study on BGP-M. I proposed a novel approach to inferring the deployment of BGP-M by querying Looking Glass (LG) servers. I conducted a detailed investigation on the deployment of BGP-M in the Internet. I also analysed BGP-M’s routing properties based on traceroute measurements using RIPE Atlas probes. My research has revealed that BGP-M has already been used in the Internet. In particular, Hurricane Electric (AS6939), a Tier-1 network operator, has deployed BGP-M at border routers across its global network to hundreds of its neighbour ASes on both IPv4 and IPv6 Internet. My research has provided the state-of-the-art knowledge and insights in the deployment, configuration and operation of BGP-M. The data, methods and analysis introduced in this thesis can be immensely valuable to researchers, network operators and regulators who are interested in improving the performance and security of Internet routing. This work has raised awareness of BGP-M and may promote more deployment of BGP-M in future because BGP-M not only provides all benefits of multipath routing but also has distinct advantages in terms of flexibility, compatibility and transparency

Performance Evaluation of Distributed Security Protocols Using Discrete Event Simulation

The Border Gateway Protocol (BGP) that manages inter-domain routing on the Internet lacks security. Protective measures using public key cryptography introduce complexities and costs. To support authentication and other security functionality in large networks, we need public key infrastructures (PKIs). Protocols that distribute and validate certificates introduce additional complexities and costs. The certification path building algorithm that helps users establish trust on certificates in the distributed network environment is particularly complicated. Neither routing security nor PKI come for free. Prior to this work, the research study on performance issues of these large-scale distributed security systems was minimal. In this thesis, we evaluate the performance of BGP security protocols and PKI systems. We answer the questions about how the performance affects protocol behaviors and how we can improve the efficiency of these distributed protocols to bring them one step closer to reality. The complexity of the Internet makes an analytical approach difficult; and the scale of Internet makes empirical approaches also unworkable. Consequently, we take the approach of simulation. We have built the simulation frameworks to model a number of BGP security protocols and the PKI system. We have identified performance problems of Secure BGP (S-BGP), a primary BGP security protocol, and proposed and evaluated Signature Amortization (S-A) and Aggregated Path Authentication (APA) schemes that significantly improve efficiency of S-BGP without compromising security. We have also built a simulation framework for general PKI systems and evaluated certification path building algorithms, a critical part of establishing trust in Internet-scale PKI, and used this framework to improve algorithm performance