Biblio

The concept of Virtualized Network Functions (VNFs) aims to move Network Functions (NFs) out of dedicated hardware devices into software that runs on commodity hardware. A single NF consists of multiple VNF instances, usually running on virtual machines in a cloud infrastructure. The elastic management of an NF refers to load management across the VNF instances and the autonomic scaling of the number of VNF instances as the load on the NF changes. In this paper, we present EL-SEC, an autonomic framework to elastically manage security NFs on a virtualized infrastructure. As a use case, we deploy the Snort Intrusion Detection System as the NF on the GENI testbed. Concepts from control theory are used to create an Elastic Manager, which implements various controllers - in this paper, Proportional Integral (PI) and Proportional Integral Derivative (PID) - to direct traffic across the VNF Snort instances by monitoring the current load. RINA (a clean-slate Recursive InterNetwork Architecture) is used to build a distributed application that monitors load and collects Snort alerts, which are processed by the Elastic Manager and an Attack Analyzer, respectively. Software Defined Networking (SDN) is used to steer traffic through the VNF instances, and to block attack traffic. Our results show that virtualized security NFs can be easily deployed using our EL-SEC framework. With the help of real-time graphs, we show that PI and PID controllers can be used to easily scale the system, which leads to quicker detection of attacks.

The Software Defined Networks (SDN) paradigm, often referred to as a radical new idea in networking, promises to dramatically simplify network management by enabling innovation through network programmability. However, notable security issues, such as app-to-control threats, remain a significant concern that impedes SDN from being widely adopted. To cope with those app-to-control threats, this paper proposes a solution to securely deploy valid network applications while protecting the SDN controller against the injection of the malicious application. This problem is mitigated by proposing a novel SDN architecture, dubbed SENAD, which splits the well-known SDN controller into: (1) a data plane controller (DPC), and (2) an application plane controller (APC), to secure this latter by design. The role of the DPC is dedicated for interpreting the network rules into OpenFlow entries and maintaining the communication with the data plane. The role of the APC, however, is to provide a secured runtime for deploying the network applications, including authentication, access control, resource isolation, control, and monitoring applications. We show that this approach can easily shield against any deny of service, caused for instance by the resource exhaustion attack or the malicious command injection, that is caused by the co-existence of a malicious application on the controller's runtime. The evaluation of our architecture shows that the packet\_in messages take less than 5 ms to be delivered from the data plane to the application plane on the long range.

Industrial Internet of Things (IIoT) is a trend of the smart industry. By collecting field data from sensors, the industry can make decisions dynamically in time for better performance. In most cases, IIoT is built on private networks and cannot be reached from the Internet. Currently, data transmission in most of IIoT network protocols is in plaintext without encryption protection. Once an attacker breaks into the field, the attacker can intercept data and injects malicious commands to field agents. In this paper, we propose a compound approach for defending command injection attacks in IIOT. First, we leverage the power of Software Defined Networking (SDN) to detect the injection attack. When the injection attack event is detected, the system owner is alarmed that someone tries to pretend a controller or a field agent to deceive the other entity. Second, we develop a lightweight authentication scheme to ensure the identity of the command sender. Command receiver can verify commands first before processing commands.

Software Defined Networking (SDN) has emerged as a revolutionary paradigm to manage cloud infrastructure. SDN lacks scalable trust setup and verification mechanism between Data Plane-Control Plane elements, Control Plane elements, and Control Plane-Application Plane. Trust management schemes like Public Key Infrastructure (PKI) used currently in SDN are slow for trust establishment in a larger cloud environment. We propose a distributed trust mechanism - TRUFL to establish and verify trust in SDN. The distributed framework utilizes parallelism in trust management, in effect faster transfer rates and reduced latency compared to centralized trust management. The TRUFL framework scales well with the number of OpenFlow rules when compared to existing research works.

In Software Defined Networking (SDN) control plane of forwarding devices is concentrated in the SDN controller, which assumes the role of a network operating system. Big share of today's commercial SDN controllers are based on OpenDaylight, an open source SDN controller platform, whose bug repository is publicly available. In this article we provide a first insight into 8k+ bugs reported in the period over five years between March 2013 and September 2018. We first present the functional components in OpenDaylight architecture, localize the most vulnerable modules and measure their contribution to the total bug content. We provide high fidelity models that can accurately reproduce the stochastic behaviour of bug manifestation and bug removal rates, and discuss how these can be used to optimize the planning of the test effort, and to improve the software release management. Finally, we study the correlation between the code internals, derived from the Git version control system, and software defect metrics, derived from Jira issue tracker. To the best of our knowledge, this is the first study to provide a comprehensive analysis of bug characteristics in a production grade SDN controller.

Being able to describe a specific network as consistent is a large step towards resiliency. Next to the importance of security lies the necessity of consistency verification. Attackers are currently focusing on targeting small and crutial goals such as network configurations or flow tables. These types of attacks would defy the whole purpose of a security system when built on top of an inconsistent network. Advances in Artificial Intelligence (AI) are playing a key role in ensuring a fast responce to the large number of evolving threats. Software Defined Networking (SDN), being centralized by design, offers a global overview of the network. Robustness and adaptability are part of a package offered by programmable networking, which drove us to consider the integration between both AI and SDN. The general goal of our series is to achieve an Artificial Intelligence Resiliency System (ARS). The aim of this paper is to propose a new AI-based consistency verification system, which will be part of ARS in our future work. The comparison of different deep learning architectures shows that Convolutional Neural Networks (CNN) give the best results with an accuracy of 99.39% on our dataset and 96% on our consistency test scenario.

The communication security issue is of great importance and should not be ignored in backbone optical networks which is undergoing the evolution toward software defined networks (SDN). With the aim to solve this problem, this paper conducts deep analysis into the security challenge of software defined optical networks (SDON) and proposes a so-called security-enhanced signaling scheme of SDON. The proposed scheme makes full advantage of current OpenFIow protocol with some necessary extensions and security improvement, by combining digital signatures and message feedback with efficient PKI (Public Key Infrastructure) in signaling procedure of OpenFIow interaction. Thus, this security-enhanced signaling procedure is also designed in details to make sure the end-to-end trusted service connection. Simulation results show that this proposed approach can greatly improve the security level of large-scale optical network for Energy Internet services with better performance in term of connection success rate performance.

The motivation behind Software-Defined Networking (SDN) is to allow services and network capabilities to be managed through a central control point. Moving Target Defense (MTD) introduces a constantly changing environment in order to delay or prevent attacks on a system. For the effective use of MTD, SDN can be used to help confuse the attacker from gathering legitimate information about the network. This paper investigates how SDN can be used for some network based MTD techniques and evaluate the benefits of integrating techniques in SDN and MTD. In the experiment, network assets are kept hidden from inside and outside attackers. Furthermore, the SDN controller is programed to perform IP mutation to keep changing real IP addresses of the underlying hosts by assigning each host a virtual IP address at a configured mutation rate to prevent attackers from stealing the real IP addresses or using fake IP addresses. The paper demonstrates experimental evaluation of the MTD technique using the Ryu controller and mininet. The results show that the MTD technique can be easily integrated into the SDN environment to use virtual IP addresses for hosts to reduce the chance of poisoning attacks.

Route randomization is an important research focus for moving target defense which seeks to proactively and dynamically change the forwarding routes in the network. In this paper, the difficulties of implementing route randomization in traditional networks are analyzed. To solve these difficulties and achieve effective route randomization, a novel route randomization approach is proposed, which is implemented by adding a mapping layer between routers' physical interfaces and their corresponding logical addresses. The design ideas and the details of proposed approach are presented. The effectiveness and performance of proposed approach are verified and evaluated by corresponding experiments.

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.

Software-Defined Networking (SDN) represents a major shift from ossified hardware-based networks to programmable software-based networks. It introduces significant granularity, visibility, and flexibility into networking, but at the same time brings new security challenges. Although the research community is making progress in addressing both the opportunities in SDN and the accompanying security challenges, very few educational materials have been designed to incorporate the latest research results and engage students in learning about SDN security. In this paper, we presents our newly designed SDN security education materials, which can be used to meet the ever-increasing demand for high quality cybersecurity professionals with expertise in SDN security. The designed security education materials incorporate the latest research results in SDN security and are integrated into CloudLab, an open cloud platform, for effective hands-on learning. Through a user study, we demonstrate that students have a better understanding of SDN security after participating in these well-designed CloudLab-based security labs, and they also acquired strong research interests in SDN security.

The rapid development of mobile networks has revolutionized the way of accessing the Internet. The exponential growth of mobile subscribers, devices and various applications frequently brings about excessive traffic in mobile networks. The demand for higher data rates, lower latency and seamless handover further drive the demand for the improved mobile network design. However, traditional methods can no longer offer cost-efficient solutions for better user quality of experience with fast time-to-market. Recent work adopts SDN in LTE core networks to meet the requirement. In these software defined LTE core networks, scalability and security become important design issues that must be considered seriously. In this paper, we propose a scalable channel security scheme for the software defined LTE core network. It applies the VxLAN for scalable tunnel establishment and MACsec for security enhancement. According to our evaluation, the proposed scheme not only enhances the security of the channel communication between different network components, but also improves the flexibility and scalability of the core network with little performance penalty. Moreover, it can also shed light on the design of the next generation cellular network.

The growing interest in the smart device/home/city has resulted in increasing popularity of Internet of Things (IoT) deployment. However, due to the open and heterogeneous nature of IoT networks, there are various challenges to deploy an IoT network, among which security and scalability are the top two to be addressed. To improve the security and scalability for IoT networks, we propose a Software-Defined Virtual Private Network (SD-VPN) solution, in which each IoT application is allocated with its own overlay VPN. The VPN tunnels used in this paper are VxLAN based tunnels and we propose to use the SDN controller to push the flow table of each VPN to the related OpenvSwitch via the OpenFlow protocol. The SD-VPN solution can improve the security of an IoT network by separating the VPN traffic and utilizing service chaining. Meanwhile, it also improves the scalability by its overlay VPN nature and the VxLAN technology.

Software Defined Network (SDN) is getting popularity both from academic and industry. Lot of researches have been made to combine SDN with future Internet paradigms to manage and control networks efficiently. SDN provides better management and control in a network through decoupling of data and control plane. Named Data Networking (NDN) is a future Internet technique with aim to replace IPv4 addressing problems. In NDN, communication between different nodes done on the basis of content names rather than IP addresses. Vehicular Ad-hoc Network (VANET) is a subtype of MANET which is also considered as a hot area for future applications. Different vehicles communicate with each other to form a network known as VANET. Communication between VANET can be done in two ways (i) Vehicle to Vehicle (V2V) (ii) Vehicle to Infrastructure (V2I). Combination of SDN and NDN techniques in future Internet can solve lot of problems which were hard to answer by considering a single technique. Security in VANET is always challenging due to unstable topology of VANET. In this paper, we merge future Internet techniques and propose a new scheme to answer timing attack problem in VANETs named as Timing Attack Prevention (TAP) protocol. Proposed scheme is evaluated through simulations which shows the superiority of proposed protocol regarding detection and mitigation of attacker vehicles as compared to normal timing attack scenario in NDN based VANET.

Intrusion Prevention System (IPS) is a tool for securing networks from any malicious packet that could be sent from specific host. IPS can be installed on SDN network that has centralized logic architecture, so that IPS doesnt need to be installed on lots of nodes instead it has to be installed alongside the controller as center of logic network. IPS still has a flaw and that is the block duration would remain the same no matter how often a specific host attacks. For this reason, writer would like to make a system that not only integrates IPS on the SDN, but also designs an adaptive IPS by utilizing a fuzzy logic that can decide how long blocks are based on the frequency variable and type of attacks. From the results of tests that have been done, SDN network that has been equipped with adaptive IPS has the ability to detect attacks and can block the attacker host with the duration based on the frequency and type of attacks. The final result obtained is to make the SDN network safer by adding 0.228 milliseconds as the execute time required for the fuzzy algorithm in one process.

The Software Defined Networking paradigm has enabled dynamic configuration and control of large networks. Although the division of the control and data planes on networks has lead to dynamic reconfigurability of large networks, finding the minimal and optimal set of controllers that can adapt to the changes in the network has proven to be a challenging problem. Recent research tends to favor small solution sets with a focus on either propagation latency or controller load distribution, and struggles to find large balanced solution sets. In this paper, we propose a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing. We demonstrate that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks. We further discuss the relevance and application of a diversity focused genetic algorithm for a moving target defense security model.

Software defined networking (SDN) is a networking paradigm to provide automated network management at run time through network orchestration and virtualization. SDN can also enhance system resilience through recovery from failures and maintaining critical operations during cyber attacks. SDN's self-healing mechanisms can be leveraged to realized autonomous attack containment, which dynamically modifies access control rules based on configurable trust levels. In this paper, we present an approach to aid in selection of security countermeasures dynamically in an SDN enabled Energy Delivery System (EDS) and achieving tradeoff between providing security and QoS. We present the modeling of security cost based on end-to-end packet delay and throughput. We propose a non-dominated sorting based multi-objective optimization framework which can be implemented within an SDN controller to address the joint problem of optimizing between security and QoS parameters by alleviating time complexity at O(M N2), where M is the number of objective functions and N is the number of population for each generation respectively. We present simulation results which illustrate how data availability and data integrity can be achieved while maintaining QoS constraints.

This paper presents a review on how to benefit from software-defined networking (SDN) to enhance smart grid security. For this purpose, the attacks threatening traditional smart grid systems are classified according to availability, integrity, and confidentiality, which are the main cyber-security objectives. The traditional smart grid architecture is redefined with SDN and a conceptual model for SDN-based smart grid systems is proposed. SDN based solutions to the mentioned security threats are also classified and evaluated. Our conclusions suggest that SDN helps to improve smart grid security by providing real-time monitoring, programmability, wide-area security management, fast recovery from failures, distributed security and smart decision making based on big data analytics.

Software Defined Network (SDN) architecture is a new and novel way of network management mechanism. In SDN, switches do not process the incoming packets like conventional network computing environment. They match for the incoming packets in the forwarding tables and if there is none it will be sent to the controller for processing which is the operating system of the SDN. A Distributed Denial of Service (DDoS) attack is a biggest threat to cyber security in SDN network. The attack will occur at the network layer or the application layer of the compromised systems that are connected to the network. In this paper a machine learning based intelligent method is proposed which can detect the incoming packets as infected or not. The different machine learning algorithms adopted for accomplishing the task are Naive Bayes, K-Nearest neighbor (KNN) and Support vector machine (SVM) to detect the anomalous behavior of the data traffic. These three algorithms are compared according to their performances and KNN is found to be the suitable one over other two. The performance measure is taken here is the detection rate of infected packets.

Wireless communication based on optical spectrum has been a promising technology to support increasing bandwidth demand in the recent years. Light fidelity, optical camera communication, visible light communication, underwater optical wireless communication, free space optical communication are such technologies those have been already deployed to support the challenges in wireless communications. Those technologies create massive data traffic as lots of infrastructures and servers are connected with the internet. Software defined optical wireless networks have been introduced in this paper as a solution to this phenomenon. An architecture has been designed where we provide the general software defined networking (SDN) structure and describe the possible tasks which can be performed by the SDN for optical wireless communication.

Software-Defined Network (SDN) is a novel architecture created to address the issues of traditional and vertically integrated networks. To increase cost-effectiveness and enable logical control, SDN provides high programmability and centralized view of the network through separation of network traffic delivery (the "data plane") from network configuration (the "control plane"). SDN controllers and related protocols are rapidly evolving to address the demands for scaling in complex enterprise networks. Because of the evolution of modern SDN technologies, production networks employing SDN are prone to several security vulnerabilities. The rate at which SDN frameworks are evolving continues to overtake attempts to address their security issues. According to our study, existing defense mechanisms, particularly SDN-based firewalls, face new and SDN-specific challenges in successfully enforcing security policies in the underlying network. In this paper, we identify problems associated with SDN-based firewalls, such as ambiguous flow path calculations and poor scalability in large networks. We survey existing SDN-based firewall designs and their shortcomings in protecting a dynamically scaling network like a data center. We extend our study by evaluating one such SDN-specific security solution called FlowGuard, and identifying new attack vectors and vulnerabilities. We also present corresponding threat detection techniques and respective mitigation strategies.

Since the 1990s, the concept of QoE has been increasingly present and many scientists take it into account within different fields of application. Taking for example the case of video streaming, the QoE has been well studied in this case while for the web the study of its QoE is relatively neglected. The Quality of Experience (QoE) is the set of objective and subjective characteristics that satisfy retain or give confidence to a user through the life cycle of a service. There are researches that take the different measurement metrics of QoE as a subject, others attack new ways to improve this QoE in order to satisfy the customer and gain his loyalty. In this paper, we focus on the web QoE that is declined by researches despite its great importance given the complexity of new web pages and their utility that is increasingly critical. The wealth of new web pages in images, videos, audios etc. and their growing significance prompt us to write this paper, in which we discuss a new method that aims to improve the web QoE in a software-defined network (SDN). Our proposed method consists in automating and making more flexible the management of the QoE improvement of the web pages and this by writing an algorithm that, depending on the case, chooses the necessary treatment to improve the web QoE of the page concerned and using both web prefetching and caching to accelerate the data transfer when the user asks for it. The first part of the paper discusses the advantages and disadvantages of existing works. In the second part we propose an automatic algorithm that treats each case with the appropriate solution that guarantees its best performance. The last part is devoted to the evaluation of the performance.

The software defined networking framework facilitates flexible and reliable internet of things networks by moving the network intelligence to a centralized location while enabling low power wireless network in the edge. In this paper, we present SD-WSN6Lo, a novel software-defined wireless management solution for 6LoWPAN networks that aims to reduce the management complexity in WSN's. As an example of the technique, a simulation of controlling the power consumption of sensor nodes is presented. The results demonstrate improved energy consumption of approximately 15% on average per node compared to the baseline condition.

Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are transforming modern networks towards a service-oriented architecture. At the same time, the cybersecurity industry is rapidly adopting Machine Learning (ML) algorithms to improve detection and mitigation of complex attacks. Traditional intrusion detection systems perform signature-based detection, based on well-known malicious traffic patterns that signify potential attacks. The main drawback of this method is that attack patterns need to be known in advance and signatures must be preconfigured. Hence, typical systems fail to detect a zero-day attack or an attack with unknown signature. This work considers the use of machine learning for advanced anomaly detection, and specifically deploys the Apache Spot ML framework on an SDN/NFV-enabled testbed running cybersecurity services as Virtual Network Functions (VNFs). VNFs are used to capture traffic for ingestion by the ML algorithm and apply mitigation measures in case of a detected anomaly. Apache Spot utilises Latent Dirichlet Allocation to identify anomalous traffic patterns in Netflow, DNS and proxy data. The overall performance of Apache Spot is evaluated by deploying Denial of Service (Slowloris, BoNeSi) and a Data Exfiltration attack (iodine).

In this paper, we propose a scheme to protect the Software Defined Network(SDN) controller from Distributed Denial-of-Service(DDoS) attacks. We first predict the amount of new requests for each openflow switch periodically based on Taylor series, and the requests will then be directed to the security gateway if the prediction value is beyond the threshold. The requests that caused the dramatic decrease of entropy will be filtered out and rules will be made in security gateway by our algorithm; the rules of these requests will be sent to the controller. The controller will send the rules to each switch to make them direct the flows matching with the rules to the honey pot. The simulation shows the averages of both false positive and false negative are less than 2%.