Machine Learning for Securing SDN based 5G Network

Alamri, Hassan A.; Thayananthan, Vijey; Yazdani, Javad

doi:10.5120/ijca2021921027

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…Previous studies used ML to detect DDoS attacks effectively on 5G mobile networks [19][20][21][22]. Especially final previous work is similar to our study in that it constructed a virtual 5G environment with a Kitsune dataset, and collected a 5G dataset on its own, which was used to perform detection [22].…”

Section: G Network Ddos Detection With ML Modelmentioning

confidence: 62%

“…Alamri et al [21] analyzed an appropriate ML to protect SDN-based 5G networks from DDoS attacks. This previous work prepared an SDN-based 5G network with datasets (CICDDoS 2019, NSL-KDD) to build and test the proposed ML model.…”

Section: G Network Ddos Detection With ML Modelmentioning

confidence: 99%

“…Research to analyze attack detection performance by optimizing ML models in a wired network environment is being actively conducted [12][13][14][15][16][17][18]. In addition, other previous studies conducted attack detection studies using ML models in 5G networks [19][20][21][22]. However, studies that consider hyperparameter tuning to optimize ML models in 5G networks are currently lacking.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison Experiment of Binary Classification for Detecting the GTP Encapsulated IoT DDoS Traffics in 5G Network

Kim¹,

Kim²,

Kim³

2022

Journal of Internet Technology

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<p>5G is characterized by ultra-low latency and the deployment of large-scale IoT environments. IoT devices with weak security can cause security problems such as network failures in 5G. To solve this problem, automated intrusion detection research using ML was being conducted. In previous studies, detection research using ML in the wired network environment was active, but it was relatively insufficient in the 5G network. In addition, the vast amount of traffic in IoT devices can create latency problems for intrusion detection with ML, making it difficult to achieve ultra-low latency 5G service objectives. Therefore, this study analyzed the meaning of the performance and required time of an optimized single ML model and ensemble learning experiment to detect in real time while ensuring high detection performance of large-capacity DDoS in 5G network. When the 5G GTP encapsulated traffic was collected and binary classification was performed, the optimized single ML model performed more than 99%. Especially, compared with ensemble learning, the experimental results showed similar performance and reduced detection time by at least 34 times. As a result of the experiment, it was shown that a single ML model optimized for detecting IoT DDoS in 5G with ultra-low latency is significant.</p> <p> </p>

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Section: G Network Ddos Detection With ML Modelmentioning

confidence: 62%

Section: G Network Ddos Detection With ML Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison Experiment of Binary Classification for Detecting the GTP Encapsulated IoT DDoS Traffics in 5G Network

Kim¹,

Kim²,

Kim³

2022

Journal of Internet Technology

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“…This section outlines research on anomaly detection using ML in 5G mobile networks. Alamri et al [ 21 ] analyzed appropriate ML models to protect 5G SDN controllers, which are targeted in DDoS attacks. The datasets used were CICDDoS201 and NSL-KDD, and the algorithms were XGBoost, RF, and logistic regression.…”

Section: Related Workmentioning

confidence: 99%

Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network

Kim

2022

Sensors

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The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology.

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“…As a result, this paper performs quantitative operations on the data collected, and some of the equations are as follows: Definition 1: Eq. ( 1) is used to calculate the change rate of data traffic [22]. P i is the port number of the SDN switch, The number of sending bytes and transmission bytes of each port of SDN switch are represented by tx and rx, respectively.If the total amount of data on each port of the switch at moment t ST is:…”

Section: Indicators Of Quantitativementioning

confidence: 99%

Network Situation Assessment Method Based on Improved BP Neural Network

Yao

et al. 2022

Preprint

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The wide application of the Internet and the use of various network devices make the existing network scale increasingly complex, and the hidden dangers of various network security incidents are gradually exposed. Software Defined Network (SDN) brings potential security risks and challenges while changing the underlying network structure. In this paper, a situation assessment model based on SDN network is proposed by summarizing the important indicators and parameters of SDN. Then, the situational factors are mapped to the layers of the neural network, and the weights and thresholds of the back propagation(BP) neural network are optimized by the improved algorithm to obtain the global optimal solution. Finally, the purpose of situation assessment and comprehensive rating of the SDN network is realized. The experimental results show that the situation assessment method proposed in this paper has good effect and high accuracy, which provides a better basis for the next situation prediction.

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Machine Learning for Securing SDN based 5G Network

Cited by 16 publications

References 16 publications

A Comparison Experiment of Binary Classification for Detecting the GTP Encapsulated IoT DDoS Traffics in 5G Network

A Comparison Experiment of Binary Classification for Detecting the GTP Encapsulated IoT DDoS Traffics in 5G Network

Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network

Network Situation Assessment Method Based on Improved BP Neural Network

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