Mitigation strategies for distributed denial of service (DDoS) in SDN: A survey and taxonomy

Karnani, Suruchi; Shakya, Harish Kumar

doi:10.1080/19393555.2022.2111004

Cited by 4 publications

(3 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…𝑟 𝑖,𝑗 denotes random numbers within the range [0,1] and 𝐼 𝑖,𝑗 signifies random numbers selected from the set {1,2}. If the newly calculated position results in an enhancement in the objective function value, it is considered valid according to Equation (10).…”

Section: Feature Selectionmentioning

confidence: 99%

“…During this stage, the Siberian tiger shifts its position while attacking its prey, improving the algorithm's capability for local search and exploitation to achieve superior solutions. Initially, a new position for the Siberian tiger near the attack site is calculated using Equation (10) This creature engages in fights with brown and black bears, driven by differences in prey preferences and survival instincts. Consequently, in the second stage, STO members undergo updates to simulate the strategic behavior of Siberian tigers when confronting bears.…”

Section: Feature Selectionmentioning

confidence: 99%

“…These attacks involve sending large volumes of malicious traffic against SDN switches or controllers, disrupting overall SDN performance. A practical approach to addressing these threats involves the use of intrusion detection systems (IDS) [9,10,11]. A system implemented within SDN can identify attacks and forward them to the firewall for blocking [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Al-Sarray,

Rahebi,

Demirhan

2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Feature Selectionmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation