SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning

The term software defined networking (SDN) is a network model that contributes to redefining the network characteristics by making the components of this network programmable, monitoring the network faster and larger, operating with the networks from a central location, as well as the possibility of detecting fraudulent traffic and detecting special malfunctions in a simple and effective way. In addition, it is the land of many security threats that lead to the complete suspension of this network. To mitigate this attack this paper based on the use of machine learning techniques contribute to the rapid detection of these attacks and methods were evaluated detecting DDoS attacks and choosing the optimum accuracy for classifying these types within the SDN, the results showed that the proposed system provides the better results of accuracy to detect the DDos attack in SDN network as 99.90% accuracy of Decision Tree (DT) algorithm.

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“…Accuracy: it is the correct predictions divided by the total predictions number. It calculated based on (2) [21].…”

Section: The Proposed Methodsmentioning

confidence: 99%

DDoS attack detection in software defined networking controller using machine learning techniques

2022

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“…Deep learning methods are beyond the scope of the work as our dataset size is small. Several research works by Liu et al, 14 Li et al, 15 Liang and Znati, 16 Nugraha and Murthy, 17 Yungaicela-Naula et al, 18 and Gadze et al 19 have applied deep learning algorithms for the detection of DDoS attack.…”

Section: Related Workmentioning

confidence: 99%

ONOS Flood Defender: An Intelligent Approach to Mitigate DDoS Attack in SDN

Aslam

Srivastava

Gore

2022

Trans Emerging Tel Tech

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Software-Defined Networking (SDN) has made its place in the networks as new technology. SDN's programmable behavior enables it to change behavior on the fly, provides instructions for the task's automatic performance, dynamic scaling, and service integration. These advantages have made SDN necessary in networks. However, SDN suffers from the threat of DDoS attack. We have developed an approach to mitigate these threats by creating an ONOS Flood Defender Application (OFD App). This app effectively detects DDoS attack using supervised and ensemble machine learning techniques and mitigates them by tracebacking the attack traffic to its origin. Our results show that ensemble machine learning techniques perform better than single machine learning algorithm to detect DDoS attack. Random Forest classifier (RFC), an ensemble technique, performs best with the highest accuracy of 99.3% in detecting DDoS attack, followed by XGBoost classifier with an accuracy of 99%. The proposed framework of the OFD app is implemented in a Mininet emulator and ONOS SDN controller. The application performs effectively in terms of time, accuracy, and overhead of the system. Our app efficiently mitigates the attacks, thereby preventing a tremendous amount of damage to legitimate users.

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“…e CICDDoS2019 dataset contains normal traffic and the latest common DDoS attacks. At present, many DDoS attack detections [30][31][32][33][34] are based on this dataset.…”

Section: Cicddos2019 Datasetmentioning

confidence: 99%

A SYN Flood Attack Detection Method Based on Hierarchical Multihead Self-Attention Mechanism

Guo

Gao

2022

Security and Communication Networks

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Existing SYN flood attack detection methods have obvious problems such as poor feature selectivity, weak generalization ability, easy overfitting, and low accuracy during training. In the paper, we present a SYN flood attack detection method based on the Hierarchical Multihad Self-Attention (HMHSA) mechanism. First, we use one-hot encoding and normalization to preprocess traffic data. Then the preprocessed traffic data is transmitted to the Feature-based Multihead Self-Attention (FBMHA) layer for feature selection. Finally, we use data slices to determine the features of the preprocessed traffic data under time series by passing the preprocessed traffic data into the Slice-based Multihead Self-Attention (SBMHA) layer. We tested the proposed method on different datasets. The experimental results show that compared with other works, our method presents better in feature selection and higher detection accuracy (even up to 99.97%).

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SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning

Cited by 111 publications

References 37 publications

DDoS attack detection in software defined networking controller using machine learning techniques

DDoS attack detection in software defined networking controller using machine learning techniques

ONOS Flood Defender: An Intelligent Approach to Mitigate DDoS Attack in SDN

A SYN Flood Attack Detection Method Based on Hierarchical Multihead Self-Attention Mechanism

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