High-Performance Packet Processing Engines Using Set-Associative Memory Architectures

The emergence of new optical transmission technologies has led to ultra-high Giga bits per second (Gbps) link speeds. In addition, the switch from 32-bit long IPv4 addresses to the 128-bit long IPv6 addresses is currently progressing. Both factors make it hard for new Internet routers and firewalls to keep up with wire-speed packet-processing. By packet-processing we mean three applications: packet forwarding, packet classification and deep packet inspection. In packet forwarding (PF), the router has to match the incoming packet's IP address against the forwarding table. It then directs each packet to its next hop toward its final destination. A packet classification (PC) engine examines a packet header by matching it against a database of rules, or filters, to obtain the best matching rule. Rules are associated with either an ``action'' (e.g., firewall) or a ``flow ID'' (e.g., quality of service or QoS). The last application is deep packet inspection (DPI) where the firewall has to inspect the actual packet payload for malware or network attacks. In this case, the payload is scanned against a database of rules, where each rule is either a plain text string or a regular expression. In this thesis, we introduce a family of hardware solutions that combine the above requirements. These solutions rely on a set-associative memory architecture that is called CA-RAM (Content Addressable-Random Access Memory). CA-RAM is a hardware implementation of hash tables with the property that each bucket of a hash table can be searched in one memory cycle. However, the classic hashing downsides have to be dealt with, such as collisions that lead to overflow and worst-case memory access time. The two standard solutions to the overflow problem are either to use some predefined probing (e.g., linear or quadratic) or to use multiple hash functions. We present new hash schemes that extend both aforementioned solutions to tackle the overflow problem efficiently. We show by experimenting with real IP lookup tables, synthetic packet classification rule sets and real DPI databases that our schemes outperform other previously proposed schemes

Intrusion Detection System against Denial of Service attack in Software-Defined Networking

Das exponentielle Wachstum der Online-Dienste und des über die Kommunikationsnetze übertragenen Datenvolumens macht es erforderlich, die Struktur traditioneller Netzwerke durch ein neues Paradigma zu ersetzen, das sich den aktuellen Anforderungen anpasst. Software-Defined Networking (SDN) ist hierfür eine fortschrittliche Netzwerkarchitektur, die darauf abzielt, das traditionelle Netzwerk in ein flexibleres Netzwerk umzuwandeln, das sich an die wachsenden Anforderungen anpasst. Im Gegensatz zum traditionellen Netzwerk ermöglicht SDN die Entkopplung von Steuer- und Datenebene, um Netzwerkressourcen effizient zu überwachen, zu konfigurieren und zu optimieren. Es verfügt über einen zentralisierten Controller mit einer globalen Netzwerksicht, der seine Ressourcen über programmierbare Schnittstellen verwaltet. Die zentrale Steuerung bringt jedoch neue Sicherheitsschwachstellen mit sich und fungiert als Single Point of Failure, den ein böswilliger Benutzer ausnutzen kann, um die normale Netzwerkfunktionalität zu stören. So startet der Angreifer einen massiven Datenverkehr, der als Distributed-Denial-of-Service Angriff (DDoSAngriff) von der SDN-Infrastrukturebene in Richtung des Controllers bekannt ist. Dieser DDoS-Angriff führt zu einer Sättigung der Steuerkanal-Bandbreite und belegt die Ressourcen des Controllers. Darüber hinaus erbt die SDN-Architektur einige Angriffsarten aus den traditionellen Netzwerken. Der Angreifer fälscht beispielweise die Pakete, um gutartig zu erscheinen, und zielt dann auf die traditionellen DDoS-Ziele wie Hosts, Server, Anwendungen und Router ab. In dieser Arbeit wird das Verhalten von böswilligen Benutzern untersucht. Anschließend wird ein Intrusion Detection System (IDS) zum Schutz der SDN-Umgebung vor DDoS-Angriffen vorgestellt. Das IDS berücksichtigt dabei drei Ansätze, um ausreichendes Feedback über den laufenden Verkehr durch die SDN-Architektur zu erhalten: die Informationen von einem externen Gerät, den OpenFlow-Kanal und die Flow-Tabelle. Daher besteht das vorgeschlagene IDS aus drei Komponenten. Das Inspector Device verhindert, dass böswillige Benutzer einen Sättigungsangriff auf den SDN-Controller starten. Die Komponente Convolutional Neural Network (CNN) verwendet eindimensionale neuronale Faltungsnetzwerke (1D-CNN), um den Verkehr des Controllers über den OpenFlow-Kanal zu analysieren. Die Komponente Deep Learning Algorithm(DLA) verwendet Recurrent Neural Networks (RNN), um die vererbten DDoS-Angriffe zu erkennen. Sie unterstützt auch die Unterscheidung zwischen bösartigen und gutartigen Benutzern als neue Gegenmaßnahme. Am Ende dieser Arbeit werden alle vorgeschlagenen Komponenten mit dem Netzwerkemulator Mininet und der Programmiersprache Python modelliert, um ihre Machbarkeit zu testen. Die Simulationsergebnisse zeigen hierbei, dass das vorgeschlagene IDS im Vergleich zu mehreren Benchmarking- und State-of-the-Art-Vorschlägen überdurchschnittliche Leistungen erbringt.The exponential growth of online services and the data volume transferred over the communication networks raises the need to change the structure of traditional networks to a new paradigm that adapts to the development’s demands. Software- Defined Networking (SDN) is an advanced network architecture aiming to evolve and transform the traditional network into a more flexible network that responds to the new requirements. In contrast to the traditional network, SDN allows decoupling of the control and data planes functionalities to monitor, configure, and optimize network resources efficiently. It has a centralized controller with a global network view to manage its resources using programmable interfaces. The central control brings new security vulnerabilities and acts as a single point of failure, which the malicious user might exploit to disrupt the network functionality. Thus, the attacker launches massive traffic known as Distributed Denial of Service (DDoS) attack from the SDN infrastructure layer towards the controller. This DDoS attack leads to saturation of control channel bandwidth and destroys the controller resources. Furthermore, the SDN architecture inherits some attacks types from the traditional networks. Therefore, the attacker forges the packets to appear benign and then targets the traditional DDoS objectives such as hosts, servers, applications, routers. This work observes the behavior of malicious users. It then presents an Intrusion Detection System (IDS) to safeguard the SDN environment against DDoS attacks. The IDS considers three approaches to obtain sufficient feedback about the ongoing traffic through the SDN architecture: the information from an external device, the OpenFlow channel, and the flow table. Therefore, the proposed IDS consists of three components; Inspector Device prevents the malicious users from launching the saturation attack towards the SDN controller. Convolutional Neural Network (CNN) Component employs the One- Dimensional Convolutional Neural Networks (1D-CNN) to analyze the controller’s traffic through the OpenFlow Channel. The Deep Learning Algorithm (DLA) component employs Recurrent Neural Networks (RNN) to detect the inherited DDoS attacks. The IDS also supports distinguishing between malicious and benign users as a new countermeasure. At the end of this work, the network emulator Mininet and the programming language python model all the proposed components to test their feasibility. The simulation results demonstrate that the proposed IDS outperforms compared several benchmarking and state-of-the-art suggestions

Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results

Fixed and mobile telecom operators, enterprise network operators and cloud providers strive to face the challenging demands coming from the evolution of IP networks (e.g. huge bandwidth requirements, integration of billions of devices and millions of services in the cloud). Proposed in the early 2010s, Segment Routing (SR) architecture helps face these challenging demands, and it is currently being adopted and deployed. SR architecture is based on the concept of source routing and has interesting scalability properties, as it dramatically reduces the amount of state information to be configured in the core nodes to support complex services. SR architecture was first implemented with the MPLS dataplane and then, quite recently, with the IPv6 dataplane (SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering of packets across nodes to a general network programming approach, making it very suitable for use cases such as Service Function Chaining and Network Function Virtualization. In this paper we present a tutorial and a comprehensive survey on SR technology, analyzing standardization efforts, patents, research activities and implementation results. We start with an introduction on the motivations for Segment Routing and an overview of its evolution and standardization. Then, we provide a tutorial on Segment Routing technology, with a focus on the novel SRv6 solution. We discuss the standardization efforts and the patents providing details on the most important documents and mentioning other ongoing activities. We then thoroughly analyze research activities according to a taxonomy. We have identified 8 main categories during our analysis of the current state of play: Monitoring, Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL

Robust Header Compression (ROHC) in Next-Generation Network Processors

Robust Header Compression (ROHC) provides for more efficient use of radio links for wireless communication in a packet switched network. Due to its potential advantages in the wireless access area andthe proliferation of network processors in access infrastructure, there exists a need to understand the resource requirements and architectural implications of implementing ROHC in this environment. We presentan analysis of the primary functional blocks of ROHC and extract the architectural implications on next-generation network processor design for wireless access. The discussion focuses on memory space andbandwidth dimensioning as well as processing resource budgets. We conclude with an examination of resource consumption and potential performance gains achievable by offloading computationally intensiveROHC functions to application specific hardware assists. We explore the design tradeoffs for hardware as-sists in the form of reconfigurable hardware, Application-Specific Instruction-set Processors (ASIPs), andApplication-Specific Integrated Circuits (ASICs)

Accurate and Resource-Efficient Monitoring for Future Networks

Monitoring functionality is a key component of any network management system. It is essential for profiling network resource usage, detecting attacks, and capturing the performance of a multitude of services using the network. Traditional monitoring solutions operate on long timescales producing periodic reports, which are mostly used for manual and infrequent network management tasks. However, these practices have been recently questioned by the advent of Software Defined Networking (SDN). By empowering management applications with the right tools to perform automatic, frequent, and fine-grained network reconfigurations, SDN has made these applications more dependent than before on the accuracy and timeliness of monitoring reports. As a result, monitoring systems are required to collect considerable amounts of heterogeneous measurement data, process them in real-time, and expose the resulting knowledge in short timescales to network decision-making processes. Satisfying these requirements is extremely challenging given today’s larger network scales, massive and dynamic traffic volumes, and the stringent constraints on time availability and hardware resources. This PhD thesis tackles this important challenge by investigating how an accurate and resource-efficient monitoring function can be realised in the context of future, software-defined networks. Novel monitoring methodologies, designs, and frameworks are provided in this thesis, which scale with increasing network sizes and automatically adjust to changes in the operating conditions. These achieve the goal of efficient measurement collection and reporting, lightweight measurement- data processing, and timely monitoring knowledge delivery

Telecommunications Hardware and Software Systems made in CMEA Countries and Yugoslavia

The telecommunications hardware and software systems used in CMEA (Council of Mutual Economic Assistance) countries and Yugoslavia are a most complex field of investigation. For this reason in this study the following approach has been adopted: Rather than collecting and presenting all CMEA telecommunications hardware and software systems in a directory type of form, which would neither be complete nor fully up to date (even at the time of data collection), a general analysis is given, with sufficient detailed information to make it useful. During the analysis we will discuss in depth the different classes of telecommunications hardware and software systems, their past, present, and potential future. In order to do this, the analysis has to include all major levels of the International Standardization Organization's Open System Interconnection (ISO/OSI) Reference Model--and this is the way we handle the telecommunications hardware and software systems of the CMEA countries and of Yugoslavia

Analyzing performance of openstate in software defined network with multiple failures scenarios

Software Defined Network (SDN) is an emerging network that decouples the control plane and data planes. Like other networks, SDN undergoes a recovery process upon occurrences of link or node failures. Openflow is considered as the popular standard used in SDN. In Openflow, the process of detecting the failure and communications with controller to recompute alternative path result to long recovery time. However, there is limit with regards time taken to recover from the failures. If it takes more than 50 msec, a lot of packet will be lost, and communication overhead and Round Trip Time (RTT) between switch – controller may be high. Openstate is an Openflow extension that allows a programmer to specify how forwarding rules should be adapted in a stateful fashion. Openstate has been tested only on single failure. This research conduct experiment based on Openstate pipeline design that provides detections mechanism based on switches periodic link probing and fast reroute of traffic flow even when controller is not reachable. In this research, the experiments use Mininet simulation software to analyse and evaluate the performance of Openstate with multiple failure scenarios. The research has compared Overhead communication, Round Trip Time (RTT) between switch – controller and number of packet loss with Openflow and Openstate. On the average, in Openstate packet loss is zero when the recovery time is less than or equal to 70 msec while communication overhead involves 60 packet-in. In Openflow, packet loss is zero when the recovery time is less than or equal to 85 msec while communication overhead involves 100 packet-in. Finally, the average RTTs for Openstate and Openflow are 65 msec and 90 msec respectively. Based on the results obtained, it can be concluded that Openstate has better performance compare to Openflow