Validating User Flows to Protect Software Defined Network Environments

Software Defined Network is a promising network paradigm which has led to several security threats in SDN applications that involve user flows, switches, and controllers in the network. Threats as spoofing, tampering, information disclosure, Denial of Service, flow table overloading, and so on have been addressed by many researchers. In this paper, we present novel SDN design to solve three security threats: flow table overloading is solved by constructing a star topology-based architecture, unsupervised hashing method mitigates link spoofing attack, and fuzzy classifier combined with L1-ELM running on a neural network for isolating anomaly packets from normal packets. For effective flow migration Discrete-Time Finite-State Markov Chain model is applied. Extensive simulations using OMNeT++ demonstrate the performance of our proposed approach, which is better at preserving holding time than are other state-of-the-art works from the literature

AUTOMATED NETWORK SECURITY WITH EXCEPTIONS USING SDN

Campus networks have recently experienced a proliferation of devices ranging from personal use devices (e.g. smartphones, laptops, tablets), to special-purpose network equipment (e.g. firewalls, network address translation boxes, network caches, load balancers, virtual private network servers, and authentication servers), as well as special-purpose systems (badge readers, IP phones, cameras, location trackers, etc.). To establish directives and regulations regarding the ways in which these heterogeneous systems are allowed to interact with each other and the network infrastructure, organizations typically appoint policy writing committees (PWCs) to create acceptable use policy (AUP) documents describing the rules and behavioral guidelines that all campus network interactions must abide by. While users are the audience for AUP documents produced by an organization\u27s PWC, network administrators are the responsible party enforcing the contents of such policies using low-level CLI instructions and configuration files that are typically difficult to understand and are almost impossible to show that they do, in fact, enforce the AUPs. In other words, mapping the contents of imprecise unstructured sentences into technical configurations is a challenging task that relies on the interpretation and expertise of the network operator carrying out the policy enforcement. Moreover, there are multiple places where policy enforcement can take place. For example, policies governing servers (e.g., web, mail, and file servers) are often encoded into the server\u27s configuration files. However, from a security perspective, conflating policy enforcement with server configuration is a dangerous practice because minor server misconfigurations could open up avenues for security exploits. On the other hand, policies that are enforced in the network tend to rarely change over time and are often based on one-size-fits-all policies that can severely limit the fast-paced dynamics of emerging research workflows found in campus networks. This dissertation addresses the above problems by leveraging recent advances in Software-Defined Networking (SDN) to support systems that enable novel in-network approaches developed to support an organization\u27s network security policies. Namely, we introduce PoLanCO, a human-readable yet technically-precise policy language that serves as a middle-ground between the imprecise statements found in AUPs and the technical low-level mechanisms used to implement them. Real-world examples show that PoLanCO is capable of implementing a wide range of policies found in campus networks. In addition, we also present the concept of Network Security Caps, an enforcement layer that separates server/device functionality from policy enforcement. A Network Security Cap intercepts packets coming from, and going to, servers and ensures policy compliance before allowing network devices to process packets using the traditional forwarding mechanisms. Lastly, we propose the on-demand security exceptions model to cope with the dynamics of emerging research workflows that are not suited for a one-size-fits-all security approach. In the proposed model, network users and providers establish trust relationships that can be used to temporarily bypass the policy compliance checks applied to general-purpose traffic -- typically by network appliances that perform Deep Packet Inspection, thereby creating network bottlenecks. We describe the components of a prototype exception system as well as experiments showing that through short-lived exceptions researchers can realize significant improvements for their special-purpose traffic

From the edge to the core : towards informed vantage point selection for internet measurement studies

Since the early days of the Internet, measurement scientists are trying to keep up with the fast-paced development of the Internet. As the Internet grew organically over time and without build-in measurability, this process requires many workarounds and due diligence. As a result, every measurement study is only as good as the data it relies on. Moreover, data quality is relative to the research question—a data set suitable to analyze one problem may be insufficient for another. This is entirely expected as the Internet is decentralized, i.e., there is no single observation point from which we can assess the complete state of the Internet. Because of that, every measurement study needs specifically selected vantage points, which fit the research question. In this thesis, we present three different vantage points across the Internet topology— from the edge to the Internet core. We discuss their specific features, suitability for different kinds of research questions, and how to work with the corresponding data. The data sets obtained at the presented vantage points allow us to conduct three different measurement studies and shed light on the following aspects: (a) The prevalence of IP source address spoofing at a large European Internet Exchange Point (IXP), (b) the propagation distance of BGP communities, an optional transitive BGP attribute used for traffic engineering, and (c) the impact of the global COVID-19 pandemic on Internet usage behavior at a large Internet Service Provider (ISP) and three IXPs.Seit den frühen Tagen des Internets versuchen Forscher im Bereich Internet Measu- rement, mit der rasanten Entwicklung des des Internets Schritt zu halten. Da das Internet im Laufe der Zeit organisch gewachsen ist und nicht mit Blick auf Messbar- keit entwickelt wurde, erfordert dieser Prozess eine Meg Workarounds und Sorgfalt. Jede Measurement Studie ist nur so gut wie die Daten, auf die sie sich stützt. Und Datenqualität ist relativ zur Forschungsfrage - ein Datensatz, der für die Analyse eines Problems geeiget ist, kann für ein anderes unzureichend sein. Dies ist durchaus zu erwarten, da das Internet dezentralisiert ist, d. h. es gibt keinen einzigen Be- obachtungspunkt, von dem aus wir den gesamten Zustand des Internets beurteilen können. Aus diesem Grund benötigt jede Measurement Studie gezielt ausgewählte Beobachtungspunkte, die zur Forschungsfrage passen. In dieser Arbeit stellen wir drei verschiedene Beobachtungspunkte vor, die sich über die gsamte Internet-Topologie erstrecken— vom Rand bis zum Kern des Internets. Wir diskutieren ihre spezifischen Eigenschaften, ihre Eignung für verschiedene Klas- sen von Forschungsfragen und den Umgang mit den entsprechenden Daten. Die an den vorgestellten Beobachtungspunkten gewonnenen Datensätze ermöglichen uns die Durchführung von drei verschiedenen Measurement Studien und damit die folgenden Aspekte zu beleuchten: (a) Die Prävalenz von IP Source Address Spoofing bei einem großen europäischen Internet Exchange Point (IXP), (b) die Ausbreitungsdistanz von BGP-Communities, ein optionales transitives BGP-Attribut, das Anwendung im Bereich Traffic-Enigneering findet sowie (c) die Auswirkungen der globalen COVID- 19-Pandemie auf das Internet-Nutzungsverhalten an einem großen Internet Service Provider (ISP) und drei IXPs

Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

Analysis and design of security mechanisms in the context of Advanced Persistent Threats against critical infrastructures

Industry 4.0 can be defined as the digitization of all components within the industry, by combining productive processes with leading information and communication technologies. Whereas this integration has several benefits, it has also facilitated the emergence of several attack vectors. These can be leveraged to perpetrate sophisticated attacks such as an Advanced Persistent Threat (APT), that ultimately disrupts and damages critical infrastructural operations with a severe impact. This doctoral thesis aims to study and design security mechanisms capable of detecting and tracing APTs to ensure the continuity of the production line. Although the basic tools to detect individual attack vectors of an APT have already been developed, it is important to integrate holistic defense solutions in existing critical infrastructures that are capable of addressing all potential threats. Additionally, it is necessary to prospectively analyze the requirements that these systems have to satisfy after the integration of novel services in the upcoming years. To fulfill these goals, we define a framework for the detection and traceability of APTs in Industry 4.0, which is aimed to fill the gap between classic security mechanisms and APTs. The premise is to retrieve data about the production chain at all levels to correlate events in a distributed way, enabling the traceability of an APT throughout its entire life cycle. Ultimately, these mechanisms make it possible to holistically detect and anticipate attacks in a timely and autonomous way, to deter the propagation and minimize their impact. As a means to validate this framework, we propose some correlation algorithms that implement it (such as the Opinion Dynamics solution) and carry out different experiments that compare the accuracy of response techniques that take advantage of these traceability features. Similarly, we conduct a study on the feasibility of these detection systems in various Industry 4.0 scenarios