Enhancing security of SDN focusing on control plane and data plane

Celesova, Barbora; Val'ko, Jozef; Grezo, Rudolf; Helebrandt, Pavol

doi:10.1109/isdfs.2019.8757542

Cited by 12 publications

(10 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Celesova et al [25] suggested a method that utilizes a Deep Neural Network (DNN) to protect the data planes and control DDoS attacks in SDN networks. However, they used the UNSW-NB15 dataset, which is not specifically designed for the SDN network environment, to train, test, and evaluate their proposed system.…”

Section: Ml-based Approachmentioning

confidence: 99%

“…As shown in Table 2, several approaches in the literature have achieved low detection accuracies, such as those of Santos et al [12], Sudar et al [8], Celesova et al [25], Hsieh et al [26], and Deepa et al [11]. Additionally, some of these approaches have been evaluated using non-SDN datasets, including Celesova et al [25], Sudar et al [8], Boukria et al [28], and Alanazi et al [10]. Moreover, most of the existing approaches are implemented on SDN controllers, which can increase overhead during DDoS attacks.…”

Section: Boukria Et Al [28]mentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller

Mansoor

Anbar

Bahashwan

et al. 2023

Systems

View full text Add to dashboard Cite

The rapid growth of cloud computing has led to the development of the Software-Defined Network (SDN), which is a network strategy that offers dynamic management and improved performance. However, security threats are a growing concern, particularly with the SDN controller becoming an attractive target for malicious actors and potential Distributed Denial of Service (DDoS) attacks. Many researchers have proposed different approaches to detecting DDoS attacks. However, those approaches suffer from high false positives, leading to low accuracy, and the main reason behind this is the use of non-qualified features and non-realistic datasets. Therefore, the deep learning (DL) algorithmic technique can be utilized to detect DDoS attacks on SDN controllers. Moreover, the proposed approach involves three stages, (1) data preprocessing, (2) cross-feature selection, which aims to identify important features for DDoS detection, and (3) detection using the Recurrent Neural Networks (RNNs) model. A benchmark dataset is employed to evaluate the proposed approach via standard evaluation metrics, including false positive rate and detection accuracy. The findings indicate that the recommended approach effectively detects DDoS attacks with average detection accuracy, average precision, average FPR, and average F1-measure of 94.186 %, 92.146%, 8.114%, and 94.276%, respectively.

show abstract

Section: Ml-based Approachmentioning

confidence: 99%

Section: Boukria Et Al [28]mentioning

confidence: 99%

Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller

Mansoor

Anbar

Bahashwan

et al. 2023

Systems

View full text Add to dashboard Cite

show abstract

“…Similarly, attacks on vulnerabilities in controllers and switches can wreak havoc on the network; therefore, malicious controllers compromise the whole network. The prevention of DDoS attacks has been a primary concern for researchers and network security administrators [14][15][16][17]. DDoS attacks are highly frequent; therefore, it is necessary to develop robust solutions that are effective in detecting and mitigating DDoS attacks [18,19].…”

Section: Related Workmentioning

confidence: 99%

Security and Privacy Issues in Software-Defined Networking (SDN): A Systematic Literature Review

2023

View full text Add to dashboard Cite

Software-defined network (SDNs) have fundamentally changed network infrastructure by decoupling the data plane and the control plane. This architectural shift rejuvenates the network layer by granting the re-programmability and centralized management of networks which brings about exciting challenges. Although an SDN seems to be a secured network when compared to conventional networks, it is still vulnerable and faces rigorous deployment challenges. Moreover, the bifurcation of data and control planes also opens up new security problems. This systematic literature review (SLR) has formalized the problem by identifying the potential attack scenarios and highlighting the possible vulnerabilities. Eighty-six articles have been selected carefully to formulize the SLR. In this SLR, we have identified major security attacks on SDN planes, including the application plane, control plane, and data plane. Moreover, this research also identifies the approaches used by industry experts and researchers to develop security solutions for SDN planes. In this research, we have introduced an attack taxonomy and proposed a collaborative security model after comprehensively identifying security attacks on SDN planes. Lastly, research gaps, challenges, and future directions are discussed for the deployment of secure SDNs.

show abstract

“…e authors of [9][10][11] used deep learning algorithms such as neural networks to solve the problem of DDoS detection in SDN. is kind of method has high accuracy, which is better than the traditional machine learning method.…”

Section: Related Workmentioning

confidence: 99%

“…0 did not occur. 1 did occur (8) if a A (t t ) � � 1 (9) σ′ � Bayesian(S A |a A (t t ) � 1) //Bayes' rule is used to calculate the prior probability of the presence of attackers ( 10) else (11) σ′ � Bayesian(S A |a A (t t ) � 0) (12) end if (13) σ � σ′ //A transcendental belief correction, that is, the next round of defender's transcendental belief end ALGORITHM 1: e optimal strategy selection algorithm for both sides in t t identification, Y j � 0 represents the classification of normal traffic and Y j � 1 represents the classification of attack traffic. SVM is selected for training, which is a small sample learning method.…”

Section: Destination Ip Addresses Growth (Dg)mentioning

confidence: 99%

DDoS Defense Method in Software-Defined Space-Air-Ground Network from Dynamic Bayesian Game Perspective

Yang

Zhang

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

The centralized management of Software-Defined Network (SDN) brings convenience to Space-Air-Ground Integrated Networks (SAGIN), which also makes it vulnerable to Distributed Denial of Service (DDoS). At present, the popular detection methods are based on machine learning, but most of them are fixed detection strategies with high overhead and real-time control, so the efficiency is not high. This paper designs different defense methods for different DDoS attacks and constructs a multitype DDoS defense model based on a dynamic Bayesian game in the Software-Defined Space-Air-Ground Integrated Networks (SD-SAGIN). The proposed game model’s Nash equilibrium is solved based on the different costs and payoffs of each method. We simulated the attack and defense of DDoS in Ryu controller and Mininet. The results show that, under our model, the attacker and defender’s strategies are in a dynamic balance, and the controller can effectively reduce the defense cost while ensuring detection accuracy. Compared with the existing traditional Support Vector Machine (SVM) defense method, the performance of the proposed method is better, and it provides one of the references for DDoS defense in SD-SAGIN.

show abstract

Enhancing security of SDN focusing on control plane and data plane

Cited by 12 publications

References 1 publication

Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller

Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller

Security and Privacy Issues in Software-Defined Networking (SDN): A Systematic Literature Review

DDoS Defense Method in Software-Defined Space-Air-Ground Network from Dynamic Bayesian Game Perspective

Contact Info

Product

Resources

About