Biblio

The Software Defined Networking (SDN) is a solution for Data Center Networks (DCN). This solution offers a centralized control that helps to simplify the management and reduce the big data issues of storage management and data analysis. This paper investigates the performance of deploying an SDN controller in DCN. The paper considers the network topology with a different number of hosts using the Mininet emulator. The paper evaluates the performance of DCN based on Python SDN controllers with a different number of hosts. This evaluation compares POX and RYU controllers as DCN solutions using the throughput, delay, overhead, and convergence time. The results show that the POX outperforms the RYU controller and is the best choice for DCN.

Software Defined Vehicular Networking (SDVN) could be the future of the vehicular networks, enabling interoperability between heterogeneous networks and mobility management. Thus, the deployment of large SDVN is considered. However, SDVN is facing major security issues, in particular, authentication and access control issues. Indeed, an unauthorized SDN controller could modify the behavior of switches (packet redirection, packet drops) and an unauthorized switch could disrupt the operation of the network (reconnaissance attack, malicious feedback). Due to the SDVN features (decentralization, mobility) and the SDVN requirements (flexibility, scalability), the Blockchain technology appears to be an efficient way to solve these authentication and access control issues. Therefore, many Blockchain-based approaches have already been proposed. However, two key challenges have not been addressed: authentication and access control for SDN controllers and high scalability for the underlying Blockchain network. That is why in this paper we propose an innovative and scalable architecture, based on a set of interconnected Blockchain sub-networks. Moreover, an efficient access control mechanism and a cross-sub-networks authentication/revocation mechanism are proposed for all SDVN devices (vehicles, roadside equipment, SDN controllers). To demonstrate the benefits of our approach, its performances are compared with existing solutions in terms of throughput, latency, CPU usage and read/write access to the Blockchain ledger. In addition, we determine an optimal number of Blockchain sub-networks according to different parameters such as the number of certificates to store and the number of requests to process.

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host's network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network's vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts' network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.

Through time inference attacks, adversaries fingerprint SDN controllers, estimate switches flow-table size, and perform flow state reconnaissance. In fact, timing a SDN and analyzing its results can expose information which later empowers SDN resource-consumption or saturation attacks. In the real world, however, launching such attacks is not easy. This is due to some challenges attackers may encounter while attacking an actual SDN deployment. These challenges, which are not addressed adequately in the related literature, are investigated in this paper. Accordingly, practical solutions to mitigate such attacks are also proposed. Discussed challenges are clarified by means of conducting extensive experiments on an actual cloud data center testbed. Moreover, mitigation schemes have been implemented and examined in details. Experimental results show that proposed countermeasures effectively block time inference attacks.

The widespread adoption of social networking and cloud computing has transformed today's Internet to a trove of personal information. As a consequence, data breaches are expected to increase in gravity and occurrence. To counteract unintended data disclosure, a great deal of effort has been dedicated in devising methods for uncovering privacy leaks. Existing solutions, however, have not addressed the time- and data-intensive nature of leak detection. The shift from hardware-specific implementation to software-based solutions is the core idea behind the concept of Network Function Virtualization (NFV). On the other hand, the Software Defined Networking (SDN) paradigm is characterized by the decoupling of the forwarding and control planes. In this paper, an SDN/NFV-enabled architecture is proposed for improving the efficiency of leak detection systems. Employing a previously developed identification strategy, Personally Identifiable Information detector (PIID) and load balancer VNFs are packaged and deployed in OpenStack through an NFV MANO. Meanwhile, SDN controllers permit the load balancer to dynamically redistribute traffic among the PIID instances. In a physical testbed, tests are conducted to evaluate the proposed architecture. Experimental results indicate that the proportions of forwarding and parsing on total overhead is influenced by the traffic intensity. Furthermore, an NFV-enabled system with scalability features was found to outperform a non-virtualized implementation in terms of latency (85.1%), packet loss (98.3%) and throughput (8.41%).

Software-Defined Networking (SDN) introduces a centralized network control and management by separating the data plane from the control plane which facilitates traffic flow monitoring, security analysis and policy formulation. However, it is challenging to choose a proper degree of traffic flow handling granularity while proactively protecting forwarding devices from getting overloaded. In this paper, we propose a novel traffic flow matching control framework called Q-DATA that applies reinforcement learning in order to enhance the traffic flow monitoring performance in SDN based networks and prevent traffic forwarding performance degradation. We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity. It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm. Next, we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require. Our novel approach is realized as a REST SDN application and evaluated in an SDN environment. Through comprehensive experiments, the results show that-compared to the default behavior of common SDN controllers and to our previous DATA mechanism-the new Q-DATA framework yields a remarkable improvement in terms of traffic forwarding performance degradation protection of SDN switches while still providing the most detailed traffic flow information on demand.

Software Defined Networking (SDN) has introduced both innovative opportunities and additional risks in the computer networking. Among disadvantages of SDNs one can mention their susceptibility to vulnerabilities associated with both virtualization and the traditional networking. Selecting a proper controller for an organization may not be a trivial task as there is a variety of SDN controllers on the market and each of them may come with its own pros and cons from the security point of view. This research proposes a comprehensive methodology for organizations to evaluate security-related features available in SDN controllers. The methodology can serve as a guideline in the decisions related to SDN choice. The proposed security assessment follows a structured approach to evaluate each layer of the SDN architecture and each metrics defined in presented research has been matched with the security controls defined in NIST 800-53. Through the tests on actual controllers the paper provides an example on how the proposed methodology can be used to evaluate existing SDN solutions.

Network Management is a critical process for an enterprise to configure and monitor the network devices using cost effective methods. It is imperative for it to be robust and free from adversarial or accidental security flaws. With the advent of cloud computing and increasing demands for centralized network control, conventional management protocols like SNMP appear inadequate and newer techniques like NMDA and NETCONF have been invented. However, unlike SNMP which underwent improvements concentrating on security, the new data management and storage techniques have not been scrutinized for the inherent security flaws. In this paper, we identify several vulnerabilities in the widely used critical infrastructures which leverage the Network Management Datastore Architecture design (NMDA). Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. We base our research on the security challenges put forth by the existing datastore's design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using our threat detection methodology, we demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. We finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.

GENI (Global Environment for Network Innovations) is a National Science Foundation (NSF) funded program which provides a virtual laboratory for networking and distributed systems research and education. It is well suited for exploring networks at a scale, thereby promoting innovations in network science, security, services and applications. GENI allows researchers obtain compute resources from locations around the United States, connect compute resources using 100G Internet2 L2 service, install custom software or even custom operating systems on these compute resources, control how network switches in their experiment handle traffic flows, and run their own L3 and above protocols. GENI architecture incorporates cloud federation. With the federation, cloud resources can be federated and/or community of clouds can be formed. The heart of federation is user identity and an ability to “advertise” cloud resources into community including compute, storage, and networking. GENI administrators can carve out what resources are available to the community and hence a portion of GENI resources are reserved for internal consumption. GENI architecture also provides “stitching” of compute and storage resources researchers request. This provides L2 network domain over Internet2's 100G network. And researchers can run their Software Defined Networking (SDN) controllers on the provisioned L2 network domain for a complete control of networking traffic. This capability is useful for large science data transfer (bypassing security devices for high throughput). Renaissance Computing Institute (RENCI), a research institute in the state of North Carolina, has developed ORCA (Open Resource Control Architecture), a GENI control framework. ORCA is a distributed resource orchestration system to serve science experiments. ORCA provides compute resources as virtual machines and as well as baremetals. ORCA based GENI ra- k was designed to serve both High Throughput Computing (HTC) and High Performance Computing (HPC) type of computes. Although, GENI is primarily used in various universities and research entities today, GENI architecture can be leveraged in the commercial, aerospace and government settings. This paper will go over the architecture of GENI and discuss the GENI architecture for scientific computing experiments.