Biblio

Nowadays, lives are very much easier with the help of IoT. Due to lack of protection and a greater number of connections, the management of IoT becomes more difficult To manage the network flow, a Software Defined Networking (SDN) has been introduced. The SDN has a great capability in automatic and dynamic distribution. For harmful attacks on the controller a centralized SDN architecture unlocks the scope. Therefore, to reduce these attacks in real-time, a securing SDN enabled IoT scenario infrastructure of Fog networks is preferred. The virtual switches have network enforcement authorized decisions and these are executed through the SDN network. Apart from this, SDN switches are generally powerful machines and simultaneously these are used as fog nodes. Therefore, SDN looks like a good selection for Fog networks of IoT. Moreover, dynamically distributing the necessary crypto keys are allowed by the centralized and software channel protection management solution, in order to establish the Datagram Transport Layer Security (DTIS) tunnels between the IoT devices, when demanded by the cyber security framework. Through the extensive deployment of this combination, the usage of CPU is observed to be 30% between devices and the latencies are in milliseconds range, and thus it presents the system feasibility with less delay. Therefore, by comparing with the traditional SDN, it is observed that the energy consumption is reduced by more than 90%.

The dynamic state of networks presents a challenge for the deployment of distributed applications and protocols. Ad-hoc schedules in the updating phase might lead to a lot of ambiguity and issues. By separating the control and data planes and centralizing control, Software Defined Networking (SDN) offers novel opportunities and remedies for these issues. However, software-based centralized architecture for distributed environments introduces significant challenges. Security is a main and crucial issue in SDN. This paper presents a deep study of the state-of-the-art of security challenges and solutions for the SDN paradigm. The conducted study helped us to propose a dynamic approach to efficiently detect different security violations and incidents caused by network updates including forwarding loop, forwarding black hole, link congestion, network policy violation, etc. Our solution relies on an intelligent approach based on the use of Machine Learning and Artificial Intelligence Algorithms.

Since the advent of the Software Defined Networking (SDN) in 2011 and formation of Open Networking Foundation (ONF), SDN inspired projects have emerged in various fields of computer networks. Almost all the networking organizations are working on their products to be supported by SDN concept e.g. openflow. SDN has provided a great flexibility and agility in the networks by application specific control functions with centralized controller, but it does not provide security guarantees for security vulnerabilities inside applications, data plane and controller platform. As SDN can also use third party applications, an infected application can be distributed in the network and SDN based systems may be easily collapsed. In this paper, a security threats assessment model has been presented which highlights the critical areas with security requirements in SDN. Based on threat assessment model a proposed Security Threats Assessment and Diagnostic System (STADS) is presented for establishing a reliable SDN framework. The proposed STADS detects and diagnose various threats based on specified policy mechanism when different components of SDN communicate with controller to fulfil network requirements. Mininet network emulator with Ryu controller has been used for implementation and analysis.

Middlebox is primarily used in Software-Defined Network (SDN) to enhance operational performance, policy compliance, and security operations. Therefore, security of the middlebox itself is essential because incorrect use of the middlebox can cause severe cybersecurity problems for SDN. Existing attacks against middleboxes in SDN (for instance, middleboxbypass attack) use methods such as cloned tags from the previous packets to justify that the middlebox has processed the injected packet. Flowcloak as the latest solution to defeat such an attack creates a defence using a tag by computing the hash of certain parts of the packet header. However, the security mechanisms proposed to mitigate these attacks are compromise-able since all parts of the packet header can be imitated, leaving the middleboxes insecure. To demonstrate our claim, we introduce a novel attack against SDN middleboxes by hijacking TCP/IP headers. The attack uses crafted TCP/IP headers to receive the tags and signatures and successfully bypasses the middleboxes.

Software-Defined Networking (SDN) can be a good option to support Industry 4.0 (4IR) and 5G wireless networks. SDN can also be a secure networking solution that improves the security, capability, and programmability in the networks. In this paper, we present and analyze an SDN-based security architecture for 4IR with 5G. SDN is used for increasing the level of security and reliability of the network by suitably dividing the whole network into data, control, and applications planes. The SDN control layer plays a beneficial role in 4IR with 5G scenarios by managing the data flow properly. We also evaluate the performance of the proposed architecture in terms of key parameters such as data transmission rate and response time.

In case of deploying additional network security equipment in a new location, network service providers face difficulties such as precise management of large number of network security equipment and expensive network operation costs. Accordingly, there is a need for a method for security-aware network service provisioning using the existing network security equipment. In order to solve this problem, there is an existing reinforcement learning-based routing decision method fixed for each node. This method performs repeatedly until a routing decision satisfying end-to-end security constraints is achieved. This generates a disadvantage of longer network service provisioning time. In this paper, we propose security constraints reinforcement learning based routing (SCRR) algorithm that generates routing decisions, which satisfies end-to-end security constraints by giving conditional reward values according to the agent state-action pairs when performing reinforcement learning.

SDN represents a significant advance for the telecom world, since the decoupling of the control and data planes offers numerous advantages in terms of management dynamism and programmability, mainly due to its software-based centralized control. Unfortunately, these features can be exploited by malicious entities, who take advantage of the centralized control to extend the scope and consequences of their attacks. When this happens, both the legal and network technical fields are concerned with gathering information that will lead them to the root cause of the problem. Although forensics and incident response processes share their interest in the event information, both operate in isolation due to the conceptual and pragmatic challenges of integrating them into SDN environments, which impacts on the resources and time required for information analysis. Given these limitations, the current work focuses on proposing a framework for SDNs that combines the above approaches to optimize the resources to deliver evidence, incorporate incident response activation mechanisms, and generate assumptions about the possible origin of the security problem.

In this paper we present techniques for enhancing the security of south bound infrastructure in SDN which includes OpenFlow switches and end hosts. In particular, the proposed security techniques have three main goals: (i) validation and secure configuration of flow rules in the OpenFlow switches by trusted SDN controller in the domain; (ii) securing the flows from the end hosts; and (iii) detecting attacks on the switches by malicious entities in the SDN domain. We have implemented the proposed security techniques as an application for ONOS SDN controller. We have also validated our application by detecting various OpenFlow switch specific attacks such as malicious flow rule insertions and modifications in the switches over a mininet emulated network.

Scalability is one of the effective features of the Software Defined Network (SDN) that allows several devices to communicate with each other. In SDN scalable networks, the number of hosts keeps increasing as per networks need. This increment makes network administrators take a straightforward action to ensure these hosts' authenticity in the network. To address this issue, we proposed a technique to authenticate SDN hosts before permitting them to establish communication with the SDN controller. In this technique, we used the Kerberos authentication protocol to ensure the authenticity of the hosts. Kerberos verifies the hosts' credentials using a centralized server contains all hosts IDs and passwords. This technique eases the secure communication between the hosts and controller and allows the hosts to safely get network rules and policies. The proposed technique ensures the immunity of the network against network attacks.

Modern Intrusion Detection Systems are able to identify and check all traffic crossing the network segments that they are only set to monitor. Traditional network infrastructures use static detection mechanisms that check and monitor specific types of malicious traffic. To mitigate this potential waste of resources and improve scalability across an entire network, we propose a methodology which deploys distributed IDS in a Software Defined Network allowing them to be used for specific types of traffic as and when it appears on a network. The core of our work is the creation of an SDN application that takes input from a Snort IDS instances, thus working as a classifier for incoming network traffic with a static ruleset for those classifications. Our application has been tested on a virtualised platform where it performed as planned holding its position for limited use on static and controlled test environments.

Software-defined networking (SDN) nowadays is extensively being used in a variety of practical settings, provides a new way to manage networks by separating the data plane from its control plane. However, SDN is particularly vulnerable to Distributed Denial of Service (DDoS) attacks because of its centralized control logic. Many studies have been proposed to tackle DDoS attacks in an SDN design using machine-learning-based schemes; however, these feature-based detection schemes are highly resource-intensive and they are unable to perform reliably in such a large-scale SDN network where a massive amount of traffic data is generated from both control and data planes. This can deplete computing resources, degrade network performance, or even shut down the network systems owing to being exhausting resources. To address the above challenges, this paper proposes a big data framework to overcome traditional data processing limitations and to exploit distributed resources effectively for the most compute-intensive tasks such as DDoS attack detection using machine learning techniques, etc. We demonstrate the robustness, scalability, and effectiveness of our framework through practical experiments.

With the new coronavirus crisis, medical devices' workload has increased dramatically, leaving them growingly vulnerable to security threats and in need of a comprehensive solution. In this work, we take advantage of the flexible and highly manageable nature of Software Defined Networks (SDN) to design a thoroughgoing security framework that covers a health organization's various security requirements. Our solution comes to be an advanced SDN firewall that solves the issues facing traditional firewalls. It enables the partitioning of the organization's network and the enforcement of different filtering and monitoring behaviors on each partition depending on security conditions. We pursued the network's efficient and dynamic security management with the least human intervention in designing our model which makes it generally qualified to use in networks with different security requirements.

Software Defined Networking (SDN) has changed the way of designing and managing networks through programmability. However, programmability also introduces security threats. In this work we address the issue of malicious hosts running malicious applications that bypass the standard SDN based detection mechanisms. The SDN security system we are proposing periodically monitors the system calls utilization of the different SDN applications installed, learns from past system behavior using machine learning classifiers, and thus accurately detects the existence of an unusual activity or a malicious application.

Cloud computing is a modern computing mode based on network, which is widely participated by the public, and provides virtualized dynamic computing resources in the form of services. Cloud computing builds an effective communication platform with the help of computer internet, so that users can get the same computing resources even if they are in different areas. With its unique technical characteristics and advantages, cloud computing has been deployed to practical applications more and more, and the consequent security problems of cloud computing have become increasingly prominent. In addition to the original cloud computing environment, this paper proposes to build a secure cloud with cloud technology, deploy security agents in the business cloud, connect the business cloud, security cloud and security agents through SDN (software defined network) technology, and dynamically divide the business cloud into logically isolated business areas through security agents. Therefore, security is separated from the specific implementation technology and deployment scheme of business cloud, and an information security protection scheme under cloud computing environment is proposed according to the characteristics of various factors, so as to enhance the security of network information.

The growing adoption of IoT devices is creating a huge positive impact on human life. However, it is also making the network more vulnerable to security threats. One of the major threats is malicious traffic injection attack, where the hacked IoT devices overwhelm the application servers causing large-scale service disruption. To address such attacks, we propose a Software Defined Networking based predictive alarm manager solution for malicious traffic detection and mitigation at the IoT Gateway. Our experimental results with the proposed solution confirms the detection of malicious flows with nearly 95% precision on average and at its best with around 99% precision.

Industrial Internet is widely used in the production field. As the openness of networks increases, industrial networks facing increasing security risks. Information and communication technologies are now available for most industrial manufacturing. This industry-oriented evolution has driven the emergence of cloud systems, the Internet of Things (IoT), Big Data, and Industry 4.0. However, new technologies are always accompanied by security vulnerabilities, which often expose unpredictable risks. Industrial safety has become one of the most essential and challenging requirements. In this article, we highlight the serious challenges facing Industry 4.0, introduce industrial security issues and present the current awareness of security within the industry. In this paper, we propose solutions for the anomaly detection and defense of the industrial Internet based on the demand characteristics of network security, the main types of intrusions and their vulnerability characteristics. The main work is as follows: This paper first analyzes the basic network security issues, including the network security needs, the security threats and the solutions. Secondly, the security requirements of the industrial Internet are analyzed with the characteristics of industrial sites. Then, the threats and attacks on the network are analyzed, i.e., system-related threats and process-related threats; finally, the current research status is introduced from the perspective of network protection, and the research angle of this paper, i.e., network anomaly detection and network defense, is proposed in conjunction with relevant standards. This paper proposes a software-defined network (SDN)-based industrial Internet security gateway for the security protection of the industrial Internet. Since there are some known types of attacks in the industrial network, in order to fully exploit the effective information, we combine the ExtratreesClassifier to enhance the detection rate of anomaly detection. In order to verify the effectiveness of the algorithm, this paper simulates an industrial network attack, using the acquired training data for testing. The test data are industrial network traffic datasets, and the experimental results show that the algorithm is suitable for anomaly detection in industrial networks.

Internet of Things (IoT) is penetrating into every aspect of our personal lives including our body, our home and our living environment which poses numerous security challenges. The number of heterogeneous connected devices is increasing exponentially in IoT, which in turn increases the attack surface of IoT. This forces the need for uniform, distributed security mechanism which can efficiently detect the attack at faster rate in highly scalable IoT environment. The proposed work satisfies this requirement by providing a security framework which combines Fog computing and Software Defined Networking (SDN). The experimental results depicts the effectiveness in protecting the IoT applications at faster rate

The 5G network systems are evolving and have complex network infrastructures. There is a great deal of work in this area focused on meeting the stringent service requirements for the 5G networks. Within this context, security requirements play a critical role as 5G networks can support a range of services such as healthcare services, financial and critical infrastructures. 3GPP and ETSI have been developing security frameworks for 5G networks. Our work in 5G security has been focusing on the design of security architecture and mechanisms enabling dynamic establishment of secure and trusted end to end services as well as development of mechanisms to proactively detect and mitigate security attacks in virtualised network infrastructures. The focus of this paper is on the latter, namely the facilities and mechanisms, and the design of a security architecture providing facilities and mechanisms to detect and mitigate specific security attacks. We have developed a simplified version of the security architecture using Software Defined Networks (SDN) and Network Function Virtualisation (NFV) technologies. The specific security functions developed in this architecture can be directly integrated into the 5G core network facilities enhancing its security.

Thanks to advances in network architecture with Software-Defined Networking (SDN) paradigm, there are various approaches for eliminating attack surface in the largescale networks relied on the essence of the SDN principle. They are ranging from intrusion detection to moving target defense, and cyber deception that leverages the network programmability. Therein, cyber deception is considered as a proactive defense strategy for the usual network operation since it makes attackers spend more time and effort to successfully compromise network systems. In this paper, we concentrate on reconnaissance attacks in SDN-enabled networks to collect the sensitive information for hackers to conduct further attacks. In more details, we introduce SDNRecon tool to perform reconnaissance attacks, which can be useful in evaluating cyber deception techniques deployed in SDN-aware networks.

IoT networks today face a myriad of security vulnerabilities in their infrastructure due to its wide attack surface. Large-scale networks are increasingly adopting a Software-Defined Networking approach, it allows for simplified network control and management through network virtualization. Since traditional security mechanisms are incapable of handling virtualized environments, SDSec or Software-Defined Security is introduced as a solution to support virtualized infrastructure, specifically aimed at providing security solutions to SDN frameworks. To further aid large scale design and development of SDN frameworks, Model-Driven Engineering (MDE) has been proposed to be used at the design phase, since abstraction, automation and analysis are inherently key aspects of MDE. This provides an efficient approach to reducing large problems through models that abstract away the complex technicality of the total system. Making adaptations to these models to address security issues faced in IoT networks, largely reduces cost and improves efficiency. These models can be simulated, analysed and supports architecture model adaptation; model changes are then reflected back to the real system. We propose a model-driven security approach for SDSec networks that can self-adapt using machine learning to mitigate security threats. The overall design time changes can be monitored at run time through machine learning techniques (e.g. deep, reinforcement learning) for real time analysis. This approach can be tested in IoT simulation environments, for instance using the CAPS IoT modeling and simulation framework. Using self-adaptation of models and advanced machine learning for data analysis would ensure that the SDSec architecture adapts and improves over time. This largely reduces the overall attack surface to achieve improved end-to-end security in IoT environments.

Software-defined network (SDN) is a network architecture that used to build, design the hardware components virtually. We can dynamically change the settings of network connections. In the traditional network, it's not possible to change dynamically, because it's a fixed connection. SDN is a good approach but still is vulnerable to DDoS attacks. The DDoS attack is menacing to the internet. To prevent the DDoS attack, the machine learning algorithm can be used. The DDoS attack is the multiple collaborated systems that are used to target the particular server at the same time. In SDN control layer is in the center that link with the application and infrastructure layer, where the devices in the infrastructure layer controlled by the software. In this paper, we propose a machine learning technique namely Decision Tree and Support Vector Machine (SVM) to detect malicious traffic. Our test outcome shows that the Decision Tree and Support Vector Machine (SVM) algorithm provides better accuracy and detection rate.

Enterprise networks are increasingly moving towards Software Defined Networking, which is becoming a major trend in the networking arena. With the increased popularity of SDN, there is a greater need for security measures for protecting the enterprise networks. This paper focuses on the design and implementation of an integrated security architecture for SDN based enterprise networks. The integrated security architecture uses a policy-based approach to coordinate different security mechanisms to detect and counteract a range of security attacks in the SDN. A distinguishing characteristic of the proposed architecture is its ability to deal with dynamic changes in the security attacks as well as changes in trust associated with the network devices in the infrastructure. The adaptability of the proposed architecture to dynamic changes is achieved by having feedback between the various security components/mechanisms in the architecture and managing them using a dynamic policy framework. The paper describes the prototype implementation of the proposed architecture and presents security and performance analysis for different attack scenarios. We believe that the proposed integrated security architecture provides a significant step towards achieving a secure SDN for enterprises.

To enhance the programmability and flexibility of network and service management, the Software-Defined Networking (SDN) paradigm is gaining growing attention by academia and industry. Motivated by its success in wired networks, researchers have recently started to embrace SDN towards developing next generation wireless networks such as Software-Defined Internet of Vehicles (SD-IoV). As the SD-IoV evolves, new security threats would emerge and demand attention. And since the core of the SD-IoV would be the control plane, it is highly vulnerable to Distributed Denial of Service (DDoS) Attacks. In this work, we investigate the impact of DDoS attacks on the controllers in a SD-IoV environment. Through experimental evaluations, we highlight the drastic effects DDoS attacks could have on a SD-IoV in terms of throughput and controller load. Our results could be a starting point to motivate further research in the area of SD-IoV security and would give deeper insights into the problems of DDoS attacks on SD-IoV.

Software-defined networking (SDN) is a relatively new technology that promotes network revolution. The most distinct characteristic of SDN is the transformation of control logic from the basic packet forwarding equipment to a centralized management unit called controller. However, the centralized control of the network resources is like a double-edged sword, for it not only brings beneficial features but also introduces single point of failure if the controller is under distributed denial of service (DDoS) attacks. In this paper, we introduce a light-weight approach based on conditional entropy to improve the SDN security with an aim of defending DDoS at the early stage. The experimental results show that the proposed method has a high average detection rate of 99.372%.

Despite bringing many benefits of global network configuration and control, Software Defined Networking (SDN) also presents potential challenges for both digital forensics and cybersecurity. In fact, there are various attacks targeting a range of vulnerabilities on vital elements of this paradigm such as controller, Northbound and Southbound interfaces. In addition to solutions of security enhancement, it is important to build mechanisms for digital forensics in SDN which provide the ability to investigate and evaluate the security of the whole network system. It should provide features of identifying, collecting and analyzing log files and detailed information about network devices and their traffic. However, upon penetrating a machine or device, hackers can edit, even delete log files to remove the evidences about their presence and actions in the system. In this case, securing log files with fine-grained access control in proper storage without any modification plays a crucial role in digital forensics and cybersecurity. This work proposes a blockchain-based approach to improve the security of log management in SDN for network forensics, called SDNLog-Foren. This model is also evaluated with different experiments to prove that it can help organizations keep sensitive log data of their network system in a secure way regardless of being compromised at some different components of SDN.