• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39B no.6
• /
• pp.397-405
• /
• 2014

Software-Defined Networking (SDN), which separates the control plane from the data plane and manages data planes in a centralized way, is now considered as a future networking technology, and many researchers and practitioners have dived into this area to devise new network applications, such new routing methods. Likewise, network security applications could be redesigned with SDN, and some pioneers have proposed several interesting network security applications with SDN. However, most approaches have just reimplemented some well-known network security applications, although SDN provides many interesting features, They didn't effectively use them. To investigate if we can use SDN in realizing sophisticated network security applications, we have designed and implemented an advanced network security application, Reflectornet, which redirects malicious or suspicious network trials to other security monitoring points (e.g., honeypot). In addition, we have tested its performance and practicability in diverse angles. Our findings and some insights will encourage other researchers to design better or intelligent network security applications with SDN.

• International Journal of Computer Science & Network Security
• /
• v.23 no.12
• /
• pp.91-100
• /
• 2023

As SDN devices and systems hit the market, security in SDN must be raised on the agenda. SDN has become an interesting area in both academics and industry. SDN promises many benefits which attract many IT managers and Leading IT companies which motivates them to switch to SDN. Over the last three decades, network attacks becoming more sophisticated and complex to detect. The goal is to study how traffic information can be extracted from an SDN controller and open virtual switches (OVS) using SDN mechanisms. The testbed environment is created using the RYU controller and Mininet. The extracted information is further used to detect these attacks efficiently using a machine learning approach. To use the Machine learning approach, a dataset is required. Currently, a public SDN based dataset is not available. In this paper, SDN based dataset is created which include legitimate and non-legitimate traffic. Classification is divided into two categories: binary and multiclass classification. Traffic has been classified with or without dimension reduction techniques like PCA and LDA. Our approach provides 98.58% of accuracy using a random forest algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.2
• /
• pp.393-399
• /
• 2019

In a situation where an unauthorized SW brought into the organization without being authorized is emerging as a threat to the network security, the security of the network based on the SDN(Software-Defined Network) can be strengthened through the development of the security application considering the organization's characteristics. Security technology of existing SDN environment has been studied to protect internal network from external networks such as firewalls and Intrusion Detection Systems, but the research for resolving insider threat was insufficient. Therefore, We propose a system that protects the internal network from unauthorized SW, which is one of the insider threats in the SDN environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.148-151
• /
• 2018

Software-Defined Networking, called SDN is a next-generation network technology that allows a centralized SDN controller to manage multiple switches and easily apply any network requirements, unlike traditional network devices. However, as the research on SDN has progressed rapidly in recent years, the security of SDN is also considered to be important. Therefore, in this paper, we investigate the major security problems in SDN and introduce a methodology to apply blockchain technology to SDN as a solution to solve them.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.580-599
• /
• 2021

Software-Defined Networking (SDN) has three key features: separation of control and forwarding, centralized control, and network programmability. While improving network management flexibility, SDN has many security issues. This paper systemizes the security threats of SDN using spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege (STRIDE) model to understand the current security status of SDN. First, we introduce the network architecture and data flow of SDN. Second, we analyze security threats of the six types given in the STRIDE model, aiming to reveal the vulnerability mechanisms and assess the attack surface. Then, we briefly describe the corresponding defense technologies. Finally, we summarize the work of this paper and discuss the trends of SDN security research.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.200-202
• /
• 2023

SDN (Software Defined Networking) is an emerging networking system which differs from traditional network architecture. Moreover SDN has many advantages and special capabilities that traditional networks do not have. SDN and P4 are related in that they can be combined to create more advanced and intelligent networking systems. Additionally, Al has emerged as a transformative force in various fields, including SDN. By applying Al and P4 to SDN, network administrators can leverage the power of them to make impact on SDN security. We offer an overview of recent trend of SDN security integrating P4 a nd Al in this study.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.374-386
• /
• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• The Journal of the Korea Contents Association
• /
• v.21 no.6
• /
• pp.51-61
• /
• 2021

In the era of the 4th industrial revolution symbolized by the Internet of Things, big data and artificial intelligence, various embedded devices are increasing exponentially. These devices have communication functions despite their low specifications, so the possibility of personal information leakage is increasing, and security threats are also increasing. Embedded devices can have security issues at most levels, from hardware to services over the network. In addition, it is difficult to apply general security techniques because it has characteristics of resource constraints such as low specifications and low power, and the related technology has not been standardized. In this study, we present vulnerabilities and possible problems and considerations in applying SDN to embedded devices in consideration of structural characteristics and real-world discovered cases. This study presents vulnerabilities and possible problems and considerations when applying SDN to embedded devices. From a hardware perspective, we consider the problems of Wi-Fi chips and Bluetooth, the problems of open flow implementation, SDN controllers, and examples of structural properties. SDN separates the data plane and the control plane, and provides a standardized interface between the two, enabling efficient communication control. It can respond to the security limitations of existing network technologies that are difficult to respond to rapid changes.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.67-78
• /
• 2024

Software-Defined Networking (SDN) is a new emerging networking paradigm that has adopted a logically centralized architecture to increase overall network performance agility and programmability. Combining network virtualization with SDN will guarantees for combined advantages of improved flexibility and network performance. Combining SDN with hypervisors divides the network physical resources into several logical transparent and isolated virtual SDN network (vSDN), where each has its virtual controller. However, SDN hypervisors bring several advantages as well as several challenges to its network operators as for the virtual appliances, their efficient placement, assurance of network performance is mandatory, and their dynamic instantiation with their migration. In this article, we provide a brief and concise review of network virtualization along with its implementation in the SDN network. SDN hypervisors types are discussed, and taxonomy is provided to demonstrate the importance of hypervisors in SDN. A comparison of SDN hypervisors is performed to elaborate on the vital hypervisor software along with their features, and different challenges are discussed faced by the SDN network. A framework is proposed to add combined functionalities of hypervisors to create a more effective and efficient virtual system. The purpose of the framework is to increase network performance through proper configuration of resources, software, control plane isolation functions with defined rules and policies.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.376-388
• /
• 2022

The expansion of new applications and business models is being significantly fueled by the development of Fifth Generation (5G) networks, which are becoming more widely accessible. The creation of the newest intelligent vehicular networks and applications is made possible by the use of Vehicular Ad hoc Networks (VANETs) and Software Defined Networking (SDN). Researchers have been concentrating on the integration of SDN and VANET in recent years, and they have examined a variety of issues connected to the architecture, the advantages of software-defined VANET services, and the new features that can be added to them. However, the overall architecture's security and robustness are still in doubt and have received little attention. Furthermore, new security threats and vulnerabilities are brought about by the deployment and integration of novel entities and a number of architectural components. In this study, we comprehensively examine the good and negative effects of the most recent SDN-enabled vehicular network topologies, focusing on security and privacy. We examine various security flaws and attacks based on the existing SDVN architecture. Finally, a thorough discussion of the unresolved concerns and potential future study directions is provided.