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2021-09-30
Ariffin, Sharifah H. S..  2020.  Securing Internet of Things System Using Software Defined Network Based Architecture. 2020 IEEE International RF and Microwave Conference (RFM). :1–5.
Majority of the daily and business activities nowadays are integrated and interconnected to the world across national, geographic and boundaries. Securing the Internet of Things (IoT) system is a challenge as these low powered devices in IoT system are very vulnerable to cyber-attacks and this will reduce the reliability of the system. Software Defined Network (SDN) intends to greatly facilitate the policy enforcement and dynamic network reconfiguration. This paper presents several architectures in the integration of IoT via SDN to improve security in the network and system.
2021-09-07
Sanjeetha, R., Srivastava, Shikhar, Kanavalli, Anita, Pattanaik, Ashutosh, Gupta, Anshul.  2020.  Mitigation of Combined DDoS Attack on SDN Controller and Primary Server in Software Defined Networks Using a Priority on Traffic Variation. 2020 International Conference for Emerging Technology (INCET). :1–5.
A Distributed Denial of Service ( DDoS ) attack is usually instigated on a primary server that provides important services in a network. However such DDoS attacks can be identified and mitigated by the controller in a Software Defined Network (SDN). If the intruder further performs an attack on the controller along with the server, the attack becomes successful.In this paper, we show how such a combined DDoS attack can be instigated on a controller as well as a primary server. The DDoS attack on the primary server is instigated by compromising few hosts to send packets with spoofed IP addresses and the attack on the controller is instigated by compromising few switches to send flow table requests repeatedly to the controller. With the help of an emulator called mininet, we show the severity of this attack on the performance of the network. We further propose a common technique that can be used to mitigate this kind of attack by observing the variation of destination IP addresses and setting different priorities to switches and handling the flow table requests accordingly by the controller.
2021-08-17
Abranches, Marcelo, Keller, Eric.  2020.  A Userspace Transport Stack Doesn't Have to Mean Losing Linux Processing. 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). :84—90.
While we cannot question the high performance capabilities of the kernel bypass approach in the network functions world, we recognize that the Linux kernel provides a rich ecosystem with an efficient resource management and an effective resource sharing ability that cannot be ignored. In this work we argue that by mixing kernel-bypass and in kernel processing can benefit applications and network function middleboxes. We leverage a high-performance user space TCP stack and recent additions to the Linux kernel to propose a hybrid approach (kernel-user space) to accelerate SDN/NFV deployments leveraging services of the reliable transport layer (i.e., stateful middleboxes, Layer 7 network functions and applications). Our results show that this approach enables highperformance, high CPU efficiency, and enhanced integration with the kernel ecosystem. We build our solution by extending mTCP which is the basis of some state-of-the-art L4-L7 NFV frameworks. By having more efficient CPU usage, NFV applications can have more CPU cycles available to run the network functions and applications logic. We show that for a CPU intense workload, mTCP/AF\_XDP can have up to 64% more throughput than the previous implementation. We also show that by receiving cooperation from the kernel, mTCP/AF\_XDP enables the creation of protection mechanisms for mTCP. We create a simulated DDoS attack and show that mTCP/AF\_XDP can maintain up to 287% more throughput than the unprotected system during the attack.
2021-07-07
Antevski, Kiril, Groshev, Milan, Baldoni, Gabriele, Bernardos, Carlos J..  2020.  DLT federation for Edge robotics. 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). :71–76.
The concept of federation in 5G and NFV networks aims to provide orchestration of services across multiple administrative domains. Edge robotics, as a field of robotics, implements the robot control on the network edge by relying on low-latency and reliable access connectivity. In this paper, we propose a solution that enables Edge robotics service to expand its service footprint or access coverage over multiple administrative domains. We propose application of Distributed ledger technologies (DLTs) for the federation procedures to enable private, secure and trusty interactions between undisclosed administrative domains. The solution is applied on a real-case Edge robotics experimental scenario. The results show that it takes around 19 seconds to deploy & federate a Edge robotics service in an external/anonymous domain without any service down-time.
2021-05-05
Chi, Po-Wen, Wang, Ming-Hung, Zheng, Yu.  2020.  SandboxNet: An Online Malicious SDN Application Detection Framework for SDN Networking. 2020 International Computer Symposium (ICS). :397—402.

Software Defined Networking (SDN) is a concept that decouples the control plane and the user plane. So the network administrator can easily control the network behavior through its own programs. However, the administrator may unconsciously apply some malicious programs on SDN controllers so that the whole network may be under the attacker’s control. In this paper, we discuss the malicious software issue on SDN networks. We use the idea of sandbox to propose a sandbox network called SanboxNet. We emulate a virtual isolated network environment to verify the SDN application functions. With continuous monitoring, we can locate the suspicious SDN applications. We also consider the sandbox-evading issue in our framework. The emulated networks and the real world networks will be indistinguishable to the SDN controller.

2021-04-09
Usman, S., Winarno, I., Sudarsono, A..  2020.  Implementation of SDN-based IDS to protect Virtualization Server against HTTP DoS attacks. 2020 International Electronics Symposium (IES). :195—198.
Virtualization and Software-defined Networking (SDN) are emerging technologies that play a major role in cloud computing. Cloud computing provides efficient utilization, high performance, and resource availability on demand. However, virtualization environments are vulnerable to various types of intrusion attacks that involve installing malicious software and denial of services (DoS) attacks. Utilizing SDN technology, makes the idea of SDN-based security applications attractive in the fight against DoS attacks. Network intrusion detection system (IDS) which is used to perform network traffic analysis as a detection system implemented on SDN networks to protect virtualization servers from HTTP DoS attacks. The experimental results show that SDN-based IDS is able to detect and mitigate HTTP DoS attacks effectively.
2021-03-29
Liao, S., Wu, J., Li, J., Bashir, A. K..  2020.  Proof-of-Balance: Game-Theoretic Consensus for Controller Load Balancing of SDN. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :231–236.
Software Defined Networking (SDN) focus on the isolation of control plane and data plane, greatly enhancing the network's support for heterogeneity and flexibility. However, although the programmable network greatly improves the performance of all aspects of the network, flexible load balancing across controllers still challenges the current SDN architecture. Complex application scenarios lead to flexible and changeable communication requirements, making it difficult to guarantee the Quality of Service (QoS) for SDN users. To address this issue, this paper proposes a paradigm that uses blockchain to incentive safe load balancing for multiple controllers. We proposed a controller consortium blockchain for secure and efficient load balancing of multi-controllers, which includes a new cryptographic currency balance coin and a novel consensus mechanism Proof-of-Balance (PoB). In addition, we have designed a novel game theory-based incentive mechanism to incentive controllers with tight communication resources to offload tasks to idle controllers. The security analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.
2021-03-16
Freitas, M. Silva, Oliveira, R., Molinos, D., Melo, J., Rosa, P. Frosi, Silva, F. de Oliveira.  2020.  ConForm: In-band Control Plane Formation Protocol to SDN-Based Networks. 2020 International Conference on Information Networking (ICOIN). :574—579.

Although OpenFlow-based SDN networks make it easier to design and test new protocols, when you think of clean slate architectures, their use is quite limited because the parameterization of its flows resides primarily in TCP/IP protocols. Besides, despite the many benefits that SDN offers, some aspects have not yet been adequately addressed, such as management plane activities, network startup, and options for connecting the data plane to the control plane. Based on these issues and limitations, this work presents a bootstrap protocol for SDN-based networks, which allows, beyond the network topology discovery, automatic configuration of an inband control plane. The protocol is designed to act only on layer two, in an autonomous, distributed and deterministic way, with low overhead and has the intent to be the basement for the implementation of other management plane related activities. A formal specification of the protocol is provided. In addition, an analytical model was created to preview the number of required messages to establish the control plane. According to this model, the proposed protocol presents less overhead than similar de-facto protocols used to topology discovery in SDN networks.

2021-03-09
Lee, T., Chang, L., Syu, C..  2020.  Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks. 2020 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.

The Software Defined Network (SDN) provides higher programmable functionality for network configuration and management dynamically. Moreover, SDN introduces a centralized management approach by dividing the network into control and data planes. In this paper, we introduce a deep learning enabled intrusion detection and prevention system (DL-IDPS) to prevent secure shell (SSH) brute-force attacks and distributed denial-of-service (DDoS) attacks in SDN. The packet length in SDN switch has been collected as a sequence for deep learning models to identify anomalous and malicious packets. Four deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Stacked Auto-encoder (SAE), are implemented and compared for the proposed DL-IDPS. The experimental results show that the proposed MLP based DL-IDPS has the highest accuracy which can achieve nearly 99% and 100% accuracy to prevent SSH Brute-force and DDoS attacks, respectively.

2021-03-04
Hashemi, M. J., Keller, E..  2020.  Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems. 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). :37—43.

The increase of cyber attacks in both the numbers and varieties in recent years demands to build a more sophisticated network intrusion detection system (NIDS). These NIDS perform better when they can monitor all the traffic traversing through the network like when being deployed on a Software-Defined Network (SDN). Because of the inability to detect zero-day attacks, signature-based NIDS which were traditionally used for detecting malicious traffic are beginning to get replaced by anomaly-based NIDS built on neural networks. However, recently it has been shown that such NIDS have their own drawback namely being vulnerable to the adversarial example attack. Moreover, they were mostly evaluated on the old datasets which don't represent the variety of attacks network systems might face these days. In this paper, we present Reconstruction from Partial Observation (RePO) as a new mechanism to build an NIDS with the help of denoising autoencoders capable of detecting different types of network attacks in a low false alert setting with an enhanced robustness against adversarial example attack. Our evaluation conducted on a dataset with a variety of network attacks shows denoising autoencoders can improve detection of malicious traffic by up to 29% in a normal setting and by up to 45% in an adversarial setting compared to other recently proposed anomaly detectors.

Hajizadeh, M., Afraz, N., Ruffini, M., Bauschert, T..  2020.  Collaborative Cyber Attack Defense in SDN Networks using Blockchain Technology. 2020 6th IEEE Conference on Network Softwarization (NetSoft). :487—492.

The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.

2021-02-23
Mendiboure, L., Chalouf, M. A., Krief, F..  2020.  A Scalable Blockchain-based Approach for Authentication and Access Control in Software Defined Vehicular Networks. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1—11.
Software Defined Vehicular Networking (SDVN) could be the future of the vehicular networks, enabling interoperability between heterogeneous networks and mobility management. Thus, the deployment of large SDVN is considered. However, SDVN is facing major security issues, in particular, authentication and access control issues. Indeed, an unauthorized SDN controller could modify the behavior of switches (packet redirection, packet drops) and an unauthorized switch could disrupt the operation of the network (reconnaissance attack, malicious feedback). Due to the SDVN features (decentralization, mobility) and the SDVN requirements (flexibility, scalability), the Blockchain technology appears to be an efficient way to solve these authentication and access control issues. Therefore, many Blockchain-based approaches have already been proposed. However, two key challenges have not been addressed: authentication and access control for SDN controllers and high scalability for the underlying Blockchain network. That is why in this paper we propose an innovative and scalable architecture, based on a set of interconnected Blockchain sub-networks. Moreover, an efficient access control mechanism and a cross-sub-networks authentication/revocation mechanism are proposed for all SDVN devices (vehicles, roadside equipment, SDN controllers). To demonstrate the benefits of our approach, its performances are compared with existing solutions in terms of throughput, latency, CPU usage and read/write access to the Blockchain ledger. In addition, we determine an optimal number of Blockchain sub-networks according to different parameters such as the number of certificates to store and the number of requests to process.
Yu, M., He, T., McDaniel, P., Burke, Q. K..  2020.  Flow Table Security in SDN: Adversarial Reconnaissance and Intelligent Attacks. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :1519—1528.

The performance-driven design of SDN architectures leaves many security vulnerabilities, a notable one being the communication bottleneck between the controller and the switches. Functioning as a cache between the controller and the switches, the flow table mitigates this bottleneck by caching flow rules received from the controller at each switch, but is very limited in size due to the high cost and power consumption of the underlying storage medium. It thus presents an easy target for attacks. Observing that many existing defenses are based on simplistic attack models, we develop a model of intelligent attacks that exploit specific cache-like behaviors of the flow table to infer its internal configuration and state, and then design attack parameters accordingly. Our evaluations show that such attacks can accurately expose the internal parameters of the target flow table and cause measurable damage with the minimum effort.

Alshamrani, A..  2020.  Reconnaissance Attack in SDN based Environments. 2020 27th International Conference on Telecommunications (ICT). :1—5.
Software Defined Networking (SDN) is a promising network architecture that aims at providing high flexibility through the separation between network logic (control plane) and forwarding functions (data plane). This separation provides logical centralization of controllers, global network overview, ease of programmability, and a range of new SDN-compliant services. In recent years, the adoption of SDN in enterprise networks has been constantly increasing. In the meantime, new challenges arise in different levels such as scalability, management, and security. In this paper, we elaborate on complex security issues in the current SDN architecture. Especially, reconnaissance attack where attackers generate traffic for the goal of exploring existing services, assets, and overall network topology. To eliminate reconnaissance attack in SDN environment, we propose SDN-based solution by utilizing distributed firewall application, security policy, and OpenFlow counters. Distributed firewall application is capable of tracking the flow based on pre-defined states that would monitor the connection to sensitive nodes toward malicious activity. We utilize Mininet to simulate the testing environment. We are able to detect and mitigate this type of attack at early stage and in average around 7 second.
Zheng, L., Jiang, J., Pan, W., Liu, H..  2020.  High-Performance and Range-Supported Packet Classification Algorithm for Network Security Systems in SDN. 2020 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.
Packet classification is a key function in network security systems in SDN, which detect potential threats by matching the packet header bits and a given rule set. It needs to support multi-dimensional fields, large rule sets, and high throughput. Bit Vector-based packet classification methods can support multi-field matching and achieve a very high throughput, However, the range matching is still challenging. To address issue, this paper proposes a Range Supported Bit Vector (RSBV) algorithm for processing the range fields. RSBV uses specially designed codes to store the pre-computed results in memory, and the result of range matching is derived through pipelined Boolean operations. Through a two-dimensional modular architecture, the RSBV can operate at a high clock frequency and line-rate processing can be guaranteed. Experimental results show that for a 1K and 512-bit OpenFlow rule set, the RSBV can sustain a throughput of 520 Million Packets Per Second.
2021-02-22
Rivera, S., Fei, Z., Griffioen, J..  2020.  POLANCO: Enforcing Natural Language Network Policies. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1–9.
Network policies govern the use of an institution's networks, and are usually written in a high-level human-readable natural language. Normally these policies are enforced by low-level, technically detailed network configurations. The translation from network policies into network configurations is a tedious, manual and error-prone process. To address this issue, we propose a new intermediate language called POlicy LANguage for Campus Operations (POLANCO), which is a human-readable network policy definition language intended to approximate natural language. Because POLANCO is a high-level language, the translation from natural language policies to POLANCO is straightforward. Despite being a high-level human readable language, POLANCO can be used to express network policies in a technically precise way so that policies written in POLANCO can be automatically translated into a set of software defined networking (SDN) rules and actions that enforce the policies. Moreover, POLANCO is capable of incorporating information about the current network state, reacting to changes in the network and adjusting SDN rules to ensure network policies continue to be enforced correctly. We present policy examples found on various public university websites and show how they can be written as simplified human-readable statements using POLANCO and how they can be automatically translated into SDN rules that correctly enforce these policies.
2021-02-16
Li, R., Wu, B..  2020.  Early detection of DDoS based on φ-entropy in SDN networks. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:731—735.
Software defined network (SDN) is an emerging network architecture. Its control logic and forwarding logic are separated. SDN has the characteristics of centralized management, which makes it easier for malicious attackers to use the security vulnerabilities of SDN networks to implement distributed denial Service (DDoS) attack. Information entropy is a kind of lightweight DDoS early detection method. This paper proposes a DDoS attack detection method in SDN networks based on φ-entropy. φ-entropy can adjust related parameters according to network conditions and enlarge feature differences between normal and abnormal traffic, which can make it easier to detect attacks in the early stages of DDoS traffic formation. Firstly, this article demonstrates the basic properties of φ-entropy, mathematically illustrates the feasibility of φ-entropy in DDoS detection, and then we use Mini-net to conduct simulation experiments to compare the detection effects of DDoS with Shannon entropy.
Lotfalizadeh, H., Kim, D. S..  2020.  Investigating Real-Time Entropy Features of DDoS Attack Based on Categorized Partial-Flows. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM). :1—6.
With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.
Wang, L., Liu, Y..  2020.  A DDoS Attack Detection Method Based on Information Entropy and Deep Learning in SDN. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:1084—1088.
Software Defined Networking (SDN) decouples the control plane and the data plane and solves the difficulty of new services deployment. However, the threat of a single point of failure is also introduced at the same time. The attacker can launch DDoS attacks towards the controller through switches. In this paper, a DDoS attack detection method based on information entropy and deep learning is proposed. Firstly, suspicious traffic can be inspected through information entropy detection by the controller. Then, fine-grained packet-based detection is executed by the convolutional neural network (CNN) model to distinguish between normal traffic and attack traffic. Finally, the controller performs the defense strategy to intercept the attack. The experiments indicate that the accuracy of this method reaches 98.98%, which has the potential to detect DDoS attack traffic effectively in the SDN environment.
Sumantra, I., Gandhi, S. Indira.  2020.  DDoS attack Detection and Mitigation in Software Defined Networks. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). :1—5.
This work aims to formulate an effective scheme which can detect and mitigate of Distributed Denial of Service (DDoS) attack in Software Defined Networks. Distributed Denial of Service attacks are one of the most destructive attacks in the internet. Whenever you heard of a website being hacked, it would have probably been a victim of a DDoS attack. A DDoS attack is aimed at disrupting the normal operation of a system by making service and resources unavailable to legitimate users by overloading the system with excessive superfluous traffic from distributed source. These distributed set of compromised hosts that performs the attack are referred as Botnet. Software Defined Networking being an emerging technology, offers a solution to reduce network management complexity. It separates the Control plane and the data plane. This decoupling provides centralized control of the network with programmability and flexibility. This work harness this programming ability and centralized control of SDN to obtain the randomness of the network flow data. This statistical approach utilizes the source IP in the network and various attributes of TCP flags and calculates entropy from them. The proposed technique can detect volume based and application based DDoS attacks like TCP SYN flood, Ping flood and Slow HTTP attacks. The methodology is evaluated through emulation using Mininet and Detection and mitigation strategies are implemented in POX controller. The experimental results show the proposed method have improved performance evaluation parameters including the Attack detection time, Delay to serve a legitimate request in the presence of attacker and overall CPU utilization.
Abdulkarem, H. S., Dawod, A..  2020.  DDoS Attack Detection and Mitigation at SDN Data Plane Layer. 2020 2nd Global Power, Energy and Communication Conference (GPECOM). :322—326.
In the coming future, Software-defined networking (SDN) will become a technology more responsive, fully automated, and highly secure. SDN is a way to manage networks by separate the control plane from the forwarding plane, by using software to manage network functions through a centralized control point. A distributed denial-of-service (DDoS) attack is the most popular malicious attempt to disrupt normal traffic of a targeted server, service, or network. The problem of the paper is the DDoS attack inside the SDN environment and how could use SDN specifications through the advantage of Open vSwitch programmability feature to stop the attack. This paper presents DDoS attack detection and mitigation in the SDN data-plane by applying a written SDN application in python language, based on the malicious traffic abnormal behavior to reduce the interference with normal traffic. The evaluation results reveal detection and mitigation time between 100 to 150 sec. The work also sheds light on the programming relevance with the open daylight controller over an abstracted view of the network infrastructure.
Wei, D., Wei, N., Yang, L., Kong, Z..  2020.  SDN-based multi-controller optimization deployment strategy for satellite network. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :467—473.
Due to the network topology high dynamic changes, the number of ground users and the impact of uneven traffic, the load difference between SDN-based satellite network controllers varies widely, which will cause network performance such as network delay and throughput to drop dramatically. Aiming at the above problems, a multi-controller optimized deployment strategy of satellite network based on SDN was proposed. First, the controller's load state is divided into four types: overload state, high load state, normal state, and idle state; second, when a controller in the network is idle, the switch under its jurisdiction is migrated to the adjacent low load controller and turn off the controller to reduce waste of resources. When the controller is in a high-load state and an overload state, consider both the controller and the switch, and migrate the high-load switch to the adjacent low-load controller. Balance the load between controllers, improve network performance, and improve network performance and network security. Simulation results show that the method has an average throughput improvement of 2.7% and a delay reduction of 3.1% compared with MCDALB and SDCLB methods.
Kriaa, S., Papillon, S., Jagadeesan, L., Mendiratta, V..  2020.  Better Safe than Sorry: Modeling Reliability and Security in Replicated SDN Controllers. 2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020. :1—6.
Software-defined networks (SDN), through their programmability, significantly increase network resilience by enabling dynamic reconfiguration of network topologies in response to faults and potentially malicious attacks detected in real-time. Another key trend in network softwarization is cloud-native software, which, together with SDN, will be an integral part of the core of future 5G networks. In SDN, the control plane forms the "brain" of the software-defined network and is typically implemented as a set of distributed controller replicas to avoid a single point of failure. Distributed consensus algorithms are used to ensure agreement among the replicas on key data even in the presence of faults. Security is also a critical concern in ensuring that attackers cannot compromise the SDN control plane; byzantine fault tolerance algorithms can provide protection against compromised controller replicas. However, while reliability/availability and security form key attributes of resilience, they are typically modeled separately in SDN, without consideration of the potential impacts of their interaction. In this paper we present an initial framework for a model that unifies reliability, availability, and security considerations in distributed consensus. We examine – via simulation of our model – some impacts of the interaction between accidental faults and malicious attacks on SDN and suggest potential mitigations unique to cloud-native software.
Zhai, P., Song, Y., Zhu, X., Cao, L., Zhang, J., Yang, C..  2020.  Distributed Denial of Service Defense in Software Defined Network Using OpenFlow. 2020 IEEE/CIC International Conference on Communications in China (ICCC). :1274—1279.
Software Defined Network (SDN) is a new type of network architecture solution, and its innovation lies in decoupling traditional network system into a control plane, a data plane, and an application plane. It logically implements centralized control and management of the network, and SDN is considered to represent the development trend of the network in the future. However, SDN still faces many security challenges. Currently, the number of insecure devices is huge. Distributed Denial of Service (DDoS) attacks are one of the major network security threats.This paper focuses on the detection and mitigation of DDoS attacks in SDN. Firstly, we explore a solution to detect DDoS using Renyi entropy, and we use exponentially weighted moving average algorithm to set a dynamic threshold to adapt to changes of the network. Second, to mitigate this threat, we analyze the historical behavior of each source IP address and score it to determine the malicious source IP address, and use OpenFlow protocol to block attack source.The experimental results show that the scheme studied in this paper can effectively detect and mitigate DDoS attacks.
Mujib, M., Sari, R. F..  2020.  Performance Evaluation of Data Center Network with Network Micro-segmentation. 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE). :27—32.

Research on the design of data center infrastructure is increasing, both from academia and industry, due to the rapid development of cloud-based applications such as search engines, social networks, and large-scale computing. On a large scale, data centers can consist of hundreds to thousands of servers that require systems with high-performance requirements and low downtime. To meet the network's needs in a dynamic data center, infrastructure of applications and services are growing. It takes a process of designing a network topology so that it can guarantee availability and security. One way to surmount this is by implementing the zero trust security model based on micro-segmentation. Zero trust is a security idea based on the principle of "never trust, always verify" in which no concepts of trust and untrust in network traffic. The zero trust security model implemented network traffic in the form of untrust. Micro-segmentation is a way to achieve zero trust by dividing a network into smaller logical segments to restrict the traffic. In this research, data center network performance based on software-defined networking with zero trust security model using micro-segmentation has been evaluated using a testbed simulation of Cisco Application Centric Infrastructure by measuring the round trip time, jitter, and packet loss during experiments. Performance evaluation results show that micro-segmentation adds an average round trip time of 4 μs and jitter of 11 μs without packet loss so that the security can be improved without significantly affecting network performance on the data center.