Software-Defined Networking (SDN) has been changing inflexible networks in software-based programmable networks for more flexibility, scalability, and visibility into networking. At the same time, it brings many new security challenges, but there are very few educational materials for students in learning about SDN security. In this workshop, we present 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. For effective hands-on learning, the security labs are designed in CloudLab, a free open cloud platform supported by NSF. Participants receive handouts describing security problems, lab instructions, techniques to use CloudLab, and worksheets for Q&A, which can be directly used for their networking classes at their home institutions. The workshop proceeds in three sessions in which we: present the way to use CloudLab and to understand SDN; practice in simulating three networking attacks in SDN on CloudLab; and discussion and critique in small groups for new SDN security labs.
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Enhancing Security Education Through Designing SDN Security Labs in CloudLab
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.
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- PAR ID:
- 10066984
- Date Published:
- Journal Name:
- Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE'18)
- Page Range / eLocation ID:
- 185 to 190
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Software-Defined Networking (SDN) represents a major transition from traditional hardware-based networks to programmable software-based networks. While SDN brings visibility, elasticity, flexibility, and scalability, it also presents security challenges. This paper describes some of the hands-on labs we developed for teaching SDN security using the CloudLab platform. The hands-on labs have been used in a graduate level course on SDN/NFV related technologies. Our teaching experience of the hands-on labs is discussed. The hands-on labs can be adopted by other instructors to teach SDN security.more » « less
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Abstract Nowadays, real‐world learning modules become vital components in computer science and engineering in general and cybersecurity in particular. However, as student enrollments have been dramatically increasing, it becomes more challenging for a university/college to keep up with the quality of education that offers hands‐on experiment training for students thoroughly. These challenges include the difficulty of providing sufficient computing resources and keep them upgraded for the increasing number of students. In order for higher education institutions to conquer such challenges, some educators introduce an alternative solution. Namely, they develop and deploy virtual lab experiments on the clouds such as Amazon AWS and the Global Environment for Network Innovations (GENI), where students can remotely access virtual resources for lab experiments. Besides, Software‐Defined Networks (SDN) are an emerging networking technology to enhance the security and performance of networked communications with simple management. In this article, we present our efforts to develop learning modules via an efficient deployment of SDN on GENI for computer networking and security education. Specifically, we first give our design methodology of the proposed learning modules, and then detail the implementations of the learning modules by starting from user account creation on the GENI testbed to advanced experimental GENI‐enabled SDN labs. It is worth pointing out that in order to accommodate students with different backgrounds and knowledge levels, we consider the varying difficulty levels of learning modules in our design. Finally, student assessment over these pedagogical efforts is discussed to demonstrate the efficiency of the proposed learning modules.
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null (Ed.)Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain’s security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform.more » « less
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null (Ed.)Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain’s security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3159450.3159514
