Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack

Zhang, Yan; Wang, Yong; Hu, Yihua; Lin, Zhi; Zhai, Yadi; Wang, Lei; Zhao, Qingsong; Wen, Kai; Kang, Linshuang

doi:10.3390/s22197286

Cited by 12 publications

(2 citation statements)

References 25 publications

(23 reference statements)

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“…Similarly, [15] introduced a hybrid deep learning approach, AE-MLP, for DDoS detection and classification, demonstrating the use of Deep Learning (DL) techniques in detecting DDoS attacks. Furthermore, the security performance analysis of Low Earth Orbit (LEO) Satellite Constellation Networks (LSCNs) under DDoS attacks is an emerging area of study, indicating the evolving nature of DDoS attack research [16]. Additionally, the use of deep learning in OpenFlow-based SDN for DDoS attack detection and defense has been explored, highlighting the significance of advanced technologies in combating DDoS threats [17].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMM

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The utilization of machine learning has significant importance in the identification and prevention of distributed denial of service (DDoS) attacks. Through the examination of network traffic patterns, machine learning algorithms possess the capability to detect anomalous activities that serve as indicators of a Distributed Denial of Service (DDoS) assault in a timely manner. This paper presents a novel hybrid approach for the detection of distributed denialof-service (DDoS) attacks in network logs, leveraging the strengths of both Random Forest (RF) for feature extraction and Recurrent Neural Network (RNN) for classification. The proposed framework harnesses the discriminative power of RF in identifying salient features from the raw network log data, which are subsequently utilized as input for the RNN classifier. The Random Forest algorithm was employed to extract a comprehensive set of discriminative features from the network log data, enabling the model to capture intricate patterns indicative of DDoS attacks. These features were then employed as input to the RNN classifier, facilitating the utilization of sequential dependencies and temporal patterns within the log data. The hybrid model achieved exceptional performance, with an accuracy of 99.99%. Furthermore, the true positive rate was recorded at an impressive 99.99%, demonstrating the model's proficiency in correctly identifying actual instances of DDoS attacks. The false positive rate was exceptionally low, at 0.0001%, underscoring the model's robustness in minimizing misclassifications. This study represents a significant advancement in the field of DDoS attack detection, offering a powerful and accurate solution that effectively combines the strengths of Random Forest for feature extraction and RNN for classification. The hybrid model's outstanding performance metrics affirm its potential for deployment in real-world network security environments, providing a robust defense against DDoS attacks.

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Section: Introductionmentioning

confidence: 99%

Untitled

2024

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMM

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“…The DDoS attack is a type of DoS attack in which the attacker uses several distributed devices to flood the data traffic. The distributed devices are usually compromised bots [43].…”

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confidence: 99%

Random Routing Algorithm for Enhancing the Cybersecurity of LEO Satellite Networks

et al. 2023

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The recent expansion of networks of low-earth orbit (LEO) satellites such as Starlink, OneWeb, and Telesat and the evolution of communication systems toward B5G and 6G with densely interconnected devices could generate opportunities for various cyber attacks. As the satellite network offers many crucial services to the public and governmental organizations, cyberattacks pose severe risks to the communication infrastructure. In this study, we propose a random routing algorithm to prevent distributed denial-of-service (DDoS) attacks on an LEO satellite constellation network. The routing algorithm utilizes the classical algorithms, i.e., k-DG, k-DS, k-SP, and k-LO, by introducing randomness and selecting one with weighted probability distribution to increase the uncertainty in the algorithm. The study shows that the proposed random routing algorithm improves the average and median cost of the attacker against DDoS attacks while maintaining the functionality of the network. The algorithm is optimized by formulating a Bayesian optimization problem. In addition to providing an additional level of uncertainty in the routing, there is an improvement of 1.71% in the average cost and 2.05% in the median cost in a typical scenario. The algorithm causes the network to be robust to cyber attacks against LEO Satellite Networks (LSNs), however, similar to any other defensive measures, it reduces the network’s goodput.

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Research on Dos Attack Simulation and Detection in Low-Orbit Satellite Network

Xie,

Yuan

et al. 2024

Lecture Notes in Computer Science

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Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack

Cited by 12 publications

References 25 publications

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Random Routing Algorithm for Enhancing the Cybersecurity of LEO Satellite Networks

Research on Dos Attack Simulation and Detection in Low-Orbit Satellite Network

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