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  • 1. Hossain, Md Billal QoS-Aware Intelligent Routing For Software Defined Networking

    Master of Science in Engineering, University of Akron, 2020, Electrical Engineering

    This thesis proposes a reinforcement learning (RL) driven software-defined networking (SDN) routing scheme for the situation-awareness and intelligent networking management. Firstly, the existing SDN network monitoring technique is applied to track the quality of service (QoS) parameters (link delay and packet loss). Afterward, the QoS data are fed to the RL algorithm in order to achieve situation awareness in SDN routing. The performance of the proposed RL-enabled routing scheme is evaluated in the simulation section by considering various network scenarios, including network congestion. Finally, the end-to-end delay, the episode reward, and the probability of path selection are recorded for each case. According to the outcomes, the proposed scheme intelligently select the efficient data path according to the current state of the network. Moreover, the end-to-end delay is compared with the Dijkstra algorithm, demonstrating the superiority of RL-enabled dynamic routing strategy over static. Additionally, the scalability of the algorithm is tested with multiple controller SDN.

    Committee: Jin Wei-Kocsis (Advisor); Kye-Shin Lee (Committee Member); Hamid Bahrami (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering
  • 2. Barritt, Brian The Modeling, Simulation, and Operational Control of Aerospace Communication Networks

    Doctor of Philosophy, Case Western Reserve University, 2017, EECS - Computer Engineering

    A paradigm shift is taking place in aerospace communications. Traditionally, aerospace systems have relied upon circuit switched communications; geostationary communications satellites act as bent-pipe transponders and are not burdened with packet processing and the complexity of mobility in the network topology. But factors such as growing mission complexity and NewSpace development practices are driving the rapid adoption of packet-based network protocols in aerospace networks. Meanwhile, several new aerospace networks are being designed to provide either low latency, high-resolution imaging or low-latency Internet access while operating in non-geostationary orbits -- or even lower, in the upper atmosphere. The need for high data-rate communications in these networks is simultaneously driving greater reliance on beamforming, directionality, and narrow beamwidths in RF communications and free-space optical communications. This dissertation explores the challenges and offers novel solutions in the modeling, simulation, and operational control of these new aerospace networks. In the concept, design, and development phases of such networks, the dissertation motivates the use of network simulators to model network protocols and network application traffic instead of relying solely on link budget calculations. It also contributes a new approach to network simulation that can integrate with spatial temporal information systems for high-fidelity modeling of time-dynamic geometry, antenna gain patterns, and wireless signal propagation in the physical layer. And towards the operational control of such networks, the dissertation introduces Temporospatial Software Defined Networking (TS-SDN), a new approach that leverages predictability in the propagated motion of platforms and high-fidelity wireless link modeling to build a holistic, predictive view of the accessible network topology and provides SDN applications with the ability to optimize the network topology and routi (open full item for complete abstract)

    Committee: Frank Merat (Committee Chair); Rabinovich Michael (Committee Member); Daniel Saab (Committee Member); Mark Allman (Committee Member) Subjects: Aerospace Engineering; Computer Engineering; Computer Science
  • 3. Alqallaf, Maha Software Defined Secure Ad Hoc Wireless Networks

    Doctor of Philosophy (PhD), Wright State University, 2016, Computer Science and Engineering PhD

    Software defined networking (SDN), a new networking paradigm that separates the network data plane from the control plane, has been considered as a flexible, layered, modular, and efficient approach to managing and controlling networks ranging from wired, infrastructure-based wireless (e.g., cellular wireless networks, WiFi, wireless mesh net- works), to infrastructure-less wireless networks (e.g. mobile ad-hoc networks, vehicular ad-hoc networks) as well as to offering new types of services and to evolving the Internet architecture. Most work has focused on the SDN application in traditional and wired and/or infrastructure based networks. Wireless networks have become increasingly more heterogeneous. Secure and collab- orative operation of mobile wireless ad-hoc networks poses significant challenges due to the decentralized nature of mobile ad hoc wireless networks, mobility of nodes, and re- source constraints. Recent developments in software defined networking shed new light on how to control and manage an ad hoc wireless network. Given the wide deployment and availability of heterogeneous wireless technologies, the control and management of ad hoc wireless networks with the new software defined networking paradigm is offered more flexibility and opportunities to deal with trust and security issues and to enable new features and services. This dissertation focuses on the SDN MANET architecture design issues for provid- ing secure collaborative operation. Specifically, (I) We have proposed four design options for software defined secure collaborative ad hoc wireless network architecture. The de- sign options are organized into (a) centralized SDN controller architecture with controller replication and (b) distributed SDN controller architecture. While these proposed architec- ture options exhibit different characteristics, many common challenges are shared amongst these options. Challenges include fault-tolerance, scalability, efficiency, and security. The unstr (open full item for complete abstract)

    Committee: Bin Wang Ph.D. (Advisor); Yong Pei Ph.D. (Committee Member); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Zhiqiang Wu Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science
  • 4. Robin, DEBOBROTO DAS LEVERAGING PISA SWITCHES FOR TRAFFIC-AWARE IN NETWORK LOAD BALANCING IN DATA CENTER NETWORKS

    PHD, Kent State University, 2023, College of Arts and Sciences / Department of Computer Science

    Over the last two decades, the exponential growth in data consumption, applications to facilitate them, and the evolution of application architectures have given rise to high-entropy traffic patterns through data center networks. Load balancing plays a pivotal role in managing this complex traffic pattern by link layer and application layer load balancing. The conventional load balancing meth- ods, while effective to some extent, have faced challenges in responding to the dynamic nature of the traffic pattern. To tackle this challenge, the load balancing concept has transformed, with the emergence of traffic awareness as a critical factor in its implementation. It requires the dynamic weight assignment/update for the paths while load balancing to adapt to high traffic variation. Re- cently emerging ”Protocol Independent Switch Architecture” (PISA) based programmable switches have unleashed the scope of implementing custom load balancing algorithms inside the network fab- ric. These in-network load balancers have the potential to achieve scalable, high throughput, and highly traffic-aware load balancing at the data center scale. However, PISA switches are designed with a limited per-packet computational budget to achieve extremely high throughput in the 6-100 Tbps range. Data center switches need to implement a large set of complex protocols and features. Delegating load-balancing logic to these switches consumes a costly computational budget, making implementing other primary features infeasible. As a result, existing PISA switch-based in-network load balancers are primarily designed for either link or application layer load balancing to meet this budget constraint. Moreover, they can not offer scalability for the same reason. This dissertation takes the next step to fill this critical gap and designs a weighted cost mechanism-based configurable load balancer (CLB)using PISA switches. CLB can offer scalable load balancing for both the link and ap (open full item for complete abstract)

    Committee: Javed I. Khan (Advisor) Subjects: Communication; Computer Science
  • 5. Perry, Nicholas Neural Network-Based Crossfire Attack Detection in SDN-Enabled Cellular Networks

    Master of Science, Miami University, 2023, Computer Science and Software Engineering

    In today's networked world, cybersecurity threats pose a significant challenge to the integrity and reliability of communication networks. One such threat is the crossfire attack, where adversaries exploit network vulnerabilities by injecting malicious packets into traffic flows. To address this, we present a novel crossfire detection scheme that solely inspects packet headers, reducing the computational overhead associated with packet inspection. Our proposed detection scheme includes both analysis of variance (ANOVA) and neural networks to identify anomalous packet behaviors indicative of crossfire attacks. To evaluate the effectiveness of our approach, we conducted experiments on a real ATT backbone topology, simulating a crossfire attack in the Mininet simulation environment. The results demonstrate that our detection scheme achieves an accuracy of 95.3\% in detecting adversarial packets, effectively mitigating the crossfire threat. Furthermore, we introduce a traffic optimization model to adapt routing decisions in response to crossfire or link flooding attacks. Leveraging the detection scheme's real-time analysis, our optimization model dynamically alters routing paths to minimize the impact of attacks on network performance. Overall, our research presents an innovative and comprehensive framework that combines efficient crossfire detection using packet headers, high-accuracy detection using ANOVA and neural networks, and an adaptive traffic optimization model.

    Committee: Suman Bhunia (Advisor); Daniela Inclezan (Committee Member); Vaskar Raychoudhary (Committee Member) Subjects: Computer Science
  • 6. Al-Mafrachi, Basheer Detection of DDoS Attacks against the SDN Controller using Statistical Approaches

    Master of Science in Computer Engineering (MSCE), Wright State University, 2017, Computer Engineering

    In traditional networks, switches and routers are very expensive, complex, and inflexible because forwarding and handling of packets are in the same device. However, Software Defined Networking (SDN) makes networks design more flexible, cheaper, and programmable because it separates the control plane from the data plane. SDN gives administrators of networks more flexibility to handle the whole network by using one device which is the controller. Unfortunately, SDN faces a lot of security problems that may severely affect the network operations if not properly addressed. Threat vectors may target main components of SDN such as the control plane, the data plane, and/or the application. Threats may also target the communication among these components. Among the threats that can cause significant damages include attacks on the control plane and communication between the controller and other networks components by exploiting the vulnerabilities in the controller or communication protocols. Controllers of SDN and their communications may be subjected to different types of attacks. DDoS attacks on the SDN controller can bring the network down. In this thesis, we have studied various form of DDoS attacks against the controller of SDN. We conducted a comparative study of a set of methods for detecting DDoS attacks on the SDN controller and identifying compromised switch interfaces. These methods are sequential probability ratio test (SPRT), count-based detection (CD), percentage-based detection (PD), and entropy-based detection (ED). We implemented the detection methods and evaluated the performance of the methods using publicly available DARPA datasets. Finally, we found that SPRT is the only one that has the highest accuracy and F score and detect almost all DDoS attacks without producing false positive and false negative.

    Committee: Bin Wang Ph.D. (Advisor); Yong Pei Ph.D. (Committee Member); Mateen Rizki Ph.D. (Committee Member) Subjects: Computer Engineering
  • 7. Niyaz, Quamar Design and Implementation of a Deep Learning based Intrusion Detection System in Software-Defined Networking Environment

    Doctor of Philosophy, University of Toledo, 2017, Engineering

    Network management becomes difficult when the size of the network grows. An ill-managed network opens several ways for the adversaries to exploit the security vulnerabilities for intrusions. Also, low-priced Internet subscriptions and publicly available attack tools enable the attackers to launch undiscovered or zero-day attacks in a network. Machine learning based approaches are well-suited to detect such kinds of undiscovered attacks. However, the hand-engineering involved in machine learning approaches for the proper selection of features from the network traffic puts a constraint on the accuracy of attack detection. The recently emerged networking paradigm named as software-defined networks (SDN) and the reincarnation of the neural network as deep learning (DL) promise to revolutionize the relevant industries. The SDN centralizes the network management and controls the network from a logically single point. The DL-based approach significantly improves the selection of features for the classification or prediction in an unsupervised manner. In our work, we utilize the benefits offered by the SDN and DL for the design and implementation of a network intrusion detection system (NIDS). The NIDS, implemented as an SDN application, can monitor the entire network for intrusions from a single point. Using the DL-based approach for the implementation helps in proper feature selection from a large traffic feature set and produces high accuracy with very low false alarms in intrusion detection. Before a real-world implementation of the NIDS, we develop a DL-based NIDS using a benchmark intrusion dataset (NSL-KDD) to explore the applicability of a DL-based approach for the NIDS implementation. An evaluation of the attack impact on network services running in the SDN environment is also performed. We analyze the response time and loss of service delivery in different attack scenarios. Finally, we discuss the implementation of a light-weight testbed for network security exper (open full item for complete abstract)

    Committee: Weiqing Sun (Committee Chair); Ahmad Y Javaid (Committee Co-Chair); Mansoor Alam (Committee Member); Junghwan Kim (Committee Member); Mohammed Niamat (Committee Member); Hong Wang (Committee Member) Subjects: Computer Engineering; Computer Science
  • 8. Elamin, Mohamed PERFORMANCE ANALYSIS OF SOFTWARE DEFINED NETWORK CONCEPTS IN NETWORKED EMBEDDED SYSTEMS

    Master of Science in Engineering, University of Akron, 2017, Electrical Engineering

    Software-Defined Networks (SDN) have been introduced for wired networks that typically have point-to-point connectivity between nodes. The main idea in this approach is to separate the control plane and the data plane in order to allow users to programmatically change the networking capabilities of the system. This investigation focused on understanding how SDN concepts could be applied in a networked embedded systems environment where the nodes have limited capabilities and the wireless links have limited bandwidth. The goals were to realize the benefits of SDN while maintaining the network's topology and connectivity. A SDN controller was implemented on BeagleBone Black Board and interfaced with a networked embedded system via a sink node. The approach was validated through simulations and xperiments based on a physical testbed in multiple scenarios. In the future, the design can be extended to a fully capable Wireless-SDN system for use in a variety of applications such as Healthcare Systems, Internet of Things and Advanced Manufacturing Systems.

    Committee: Shivakumar Sastry Dr. (Advisor); Nghi Tran Dr. (Committee Co-Chair); Jin Kocsis Dr. (Committee Member); Hamid Bahrami Dr. (Committee Member) Subjects: Engineering
  • 9. Gruesen, Michael Towards an Ideal Execution Environment for Programmable Network Switches

    Master of Science, University of Akron, 2016, Computer Science

    Software Defined Networking (SDN) aims to create more powerful, intelligent networks that are managed using programmed switching devices. Applications for these SDN switches should be target independent, while being efficiently translated to the platform's native machine code. However network switch vendors do not conform to any standard, and contain different capabilities and features that vary between manufacturers. The Freeflow Virtual Machine (FFVM) is a modular, fully programmable virtual switch that can host compiled network applications. Applications are compiled to native object libraries and dynamically loaded at run time. The FFVM provides the necessary data and computing resources required by applications to process packets. This work details the many implementation approaches investigated and evaluated in order to define a suitable execution environment for hosted network applications.

    Committee: Andrew Sutton Dr. (Advisor) Subjects: Computer Science
  • 10. Jamaliannasrabadi, Saba High Performance Computing as a Service in the Cloud Using Software-Defined Networking

    Master of Science (MS), Bowling Green State University, 2015, Computer Science

    Benefits of Cloud Computing (CC) such as scalability, reliability, and resource pooling have attracted scientists to deploy their High Performance Computing (HPC) applications on the Cloud. Nevertheless, HPC applications can face serious challenges on the cloud that could undermine the gained benefit, if care is not taken. This thesis targets to address the shortcomings of the Cloud for the HPC applications through a platform called HPC as a Service (HPCaaS). Further, a novel scheme is introduced to improve the performance of HPC task scheduling on the Cloud using the emerging technology of Software-Defined Networking (SDN). The research introduces “ASETS: A SDN-Empowered Task Scheduling System” as an elastic platform for scheduling HPC tasks on the cloud. In addition, a novel algorithm called SETSA is developed as part of the ASETS architecture to manage the scheduling task of the HPCaaS platform. The platform monitors the network bandwidths to take advantage of the changes when submitting tasks to the virtual machines. The experiments and benchmarking of HPC applications on the Cloud identified the virtualization overhead, cloud networking, and cloud multi-tenancy as the primary shortcomings of the cloud for HPC applications. A private Cloud Test Bed (CTB) was set up to evaluate the capabilities of ASETS and SETSA in addressing such problems. Subsequently, Amazon AWS public cloud was used to assess the scalability of the proposed systems. The obtained results of ASETS and SETSA on both private and public cloud indicate significant performance improvement of HPC applications can be achieved. Furthermore, the results suggest that proposed system is beneficial both to the cloud service providers and the users since ASETS performs better the degree of multi-tenancy increases. The thesis also proposes SETSAW (SETSA Window) as an improved version of SETSA algorism. Unlike other proposed solutions for HPCaaS which have either optimized the cloud to make it more HPC-fr (open full item for complete abstract)

    Committee: Hassan Rajaei Ph.D (Advisor); Robert Green Ph.D (Committee Member); Jong Kwan Lee Ph.D (Committee Member) Subjects: Computer Engineering; Computer Science; Technology