On the (in)Security of the Control Plane of SDN Architecture: A Survey

Bhuiyan, Zaheed Ahmed; Islam, Salekul; Islam, Md. Motaharul; Ullah, A. B. M. Ahasan; Naz, Farha; Rahman, Mohammad Shahriar

doi:10.1109/access.2023.3307467

Cited by 17 publications

(4 citation statements)

References 116 publications

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“…The study in [13] presents the DARFESS (Attack-Resilient Framework for Energy Security System), which uses software-defined networking to monitor and control the cyber infrastructure of power systems. Authors provide insights into the security landscape of SDN in [14], aiming to enhance the security posture of SDN deployments and address the evolving security challenges posed by the programmability and centralization of network control. Another study in [15] presents an optimized AI model that effectively detects and mitigates DDoS attacks in SDN environments, showcasing its superiority over traditional machine learning models.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, research in SDN has been focusing on controller placement strategies, determining the number of controllers, the location for controller placement, and scaling SDN networks. However, failover techniques using attack-aware link assignment methods aiming to mitigate DDoS-based large volumetric attacks have not been investigated thoroughly in SDN, e.g., in [8][9][10][11][12][13][14][15][16][17][18][19]. This work focuses on tackling a single point of failure and studying the hierarchy of controllers in SDN and presents a novel mathematical model for logical link assignment from switches to the cluster (backup) controllers under DDoS attacks.…”

Section: Related Workmentioning

confidence: 99%

“…This will restore or resume the disrupted service again to the requested users, ensuring service availability. The past literature has focused on the security of controller placement, the security of message transformation, bandwidth optimization, and network scalability [8][9][10][11][12][13][14][15][16][17][18][19]. However, the past literature has not focused on link assignment strategies considering bandwidth and cost optimization under single points of failure in SDN networks.…”

Section: Introductionmentioning

confidence: 99%

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AALLA: Attack-Aware Logical Link Assignment Cost-Minimization Model for Protecting Software-Defined Networks against DDoS Attacks

Ali,

Tan,

Lee

et al. 2023

Sensors

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Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its “network brain” located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AALLA: Attack-Aware Logical Link Assignment Cost-Minimization Model for Protecting Software-Defined Networks against DDoS Attacks

Ali,

Tan,

Lee

et al. 2023

Sensors

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“…It guides the management of network traffic, data flow, and the allocation of network resources. Additionally, the control plane establishes rules and policies that impact network performance in terms of data transmission and traffic [1,2]. The central system for data transmission in the network is the controller, which makes high-level decisions, manages the configuration of network devices (like switches), and coordinates their functions.…”

Section: Introductionmentioning

confidence: 99%

Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning

Al-Sarray,

Rahebi,

Demirhan

2024

Preprint

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The Software-defined Networking (SDN) system plays a crucial role in efficiently overseeing the Internet network by segregating the control and data planes. In the SDN, the controller manages and determines the policy in sending data and setting the SDN switches. Despite the significant advantages, the SDN network has security challenges. DDoS attacks are the main challenge in SDN networks. DDoS attacks primarily target the SDN controller to disrupt network performance. Intrusion detection systems in SDN networks need confidential methods for message exchange and coordination of controllers so that they can exchange the blacklist of attacking addresses with each other. In this manuscript, we introduce an approach utilizing 1D CNN and LSTM networks for detecting attacks in the SDN network, incorporating blacklist information hidden in images. In the first stage, game theory and deep learning based on GAN are used to increase attack detection accuracy in each SDN controller to balance the data set. In the second stage, each controller uses 1D CNN to extract the primary features, and the Siberian tiger optimization (STO) algorithm is applied to enhance the efficiency of this deep learning network. In the third step, the STO algorithm selects the optimal features. Finally, the LSTM network classifies traffic by receiving the selected features. SDN controllers use image encryption to increase privacy and security for exchanging and sharing blacklists. The tests performed in Python and on datasets UNSW-NB15, CIC-IDS2017, and NSL-KDD are 99.49%, 99.86%, and 99.91%. The proposed method GAN-CL-STO demonstrates higher accuracy in detecting network attacks compared to the CNN-LSTM, HODNN+CRF, 1D CNN, PSO-1D CNN, and 1D CNN+BiLSTM methods. The suggested approach for identifying attacks in SDN is more accurate than WOA, HHO, and COA feature selection methods.

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A Comprehensive State-of-the-Art Review for Digital Twin: Cybersecurity Perspectives and Open Challenges

Jaber,

Koufos,

Christopoulou

2024

Lecture Notes on Data Engineering and Communications Technologies

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On the (in)Security of the Control Plane of SDN Architecture: A Survey

Cited by 17 publications

References 116 publications

AALLA: Attack-Aware Logical Link Assignment Cost-Minimization Model for Protecting Software-Defined Networks against DDoS Attacks

AALLA: Attack-Aware Logical Link Assignment Cost-Minimization Model for Protecting Software-Defined Networks against DDoS Attacks

Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning

A Comprehensive State-of-the-Art Review for Digital Twin: Cybersecurity Perspectives and Open Challenges

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