On the Large-Scale Traffic DDoS Threat of Space Backbone Network

Di, Ao; Shi, Ruisheng; Lan, Lina; Lu, Yueming

doi:10.1109/bigdatasecurity-hpsc-ids.2019.00045

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Attacks as DDoS is one of the most popular intrusions in STINs, which can lead to a decline in service quality or denial of service [26]. Many studies have used different machine learning techniques to establish effective NIDSs for DDoS attack detection in satellite networks or terrestrial networks [27]- [30]. For DDoS attacks in the satellite network, Di et al [27] proposed an idea of defense architectures combining with distributed multipoint detection, near-source defense, collaborative management and integrality of protection.…”

Section: Network Intrusion Detection Systemmentioning

confidence: 99%

“…Many studies have used different machine learning techniques to establish effective NIDSs for DDoS attack detection in satellite networks or terrestrial networks [27]- [30]. For DDoS attacks in the satellite network, Di et al [27] proposed an idea of defense architectures combining with distributed multipoint detection, near-source defense, collaborative management and integrality of protection. For DDoS attacks in the flying ad-hoc network (FANET), Mowla et al [28] developed a modelfree Q-learning mechanism with an adaptive explorationexploitation epsilon-greedy policy, directed by an ondevice federated jamming detection mechanism.…”

Section: Network Intrusion Detection Systemmentioning

confidence: 99%

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Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Zhou

et al. 2020

IEEE Access

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The existing satellite-terrestrial integrated networks (STINs) suffer from security and privacy concerns due to the limited resources, poor attack resistance and high privacy requirements of satellite networks. Network Intrusion Detection System (NIDS) is intended to provide a high level of protection for modern network environments, but how to implement distributed NIDS on STINs has not been widely discussed. At the same time, satellite networks have always lacked real and effective security data sets as references. To solve these problems, we propose a distributed NIDS using Federal Learning (FL) in STIN to properly allocate resources in each domain to analyze and block malicious traffic, especially distributed denial-of-service (DDoS) attacks. Specifically, we first design a typical STIN topology, on the basis of which we collect and design security data sets adapted to satellite and terrestrial networks in STIN, respectively. To address the problem of poor attack resistance of satellite networks, we propose a satellite network topology optimization algorithm to reduce the difficulty in tracing malicious packets due to frequent link switching. In order to solve the problem of limited resources and high privacy requirements of satellite networks, we propose an algorithm for FL adaptation to STIN, and build a distributed NIDS using FL in STIN. Finally, we deploy the designed distributed NIDS in a prototype system and evaluate our proposed distributed NIDS with a large number of simulations of randomly generated malicious traffic. Related results demonstrate that the performance of our approach is better than traditional deep learning and intrusion detection methods in terms of malicious traffic recognition rate, packet loss rate, and CPU utilization. INDEX TERMS Satellite-terrestrial integrated network, distributed NIDS, security data set, federated learning.

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Section: Network Intrusion Detection Systemmentioning

confidence: 99%

Section: Network Intrusion Detection Systemmentioning

confidence: 99%

Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Zhou

et al. 2020

IEEE Access

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“…At present, there are relatively few studies on satellite network security, especially the DDoS attack [ 17 , 18 , 19 , 20 ]. The latest research progress on LSCN DDoS attacks was the first volumetric DDoS attack against next-generation LSCNs: ICARUS [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack

Zhang

Wang

et al. 2022

Sensors

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Low Earth orbit satellite constellation networks (LSCNs) have attracted significant attention around the world due to their great advantages of low latency and wide coverage, but they also bring new challenges to network security. Distributed denial of service (DDoS) attacks are considered one of the most threatening attack methods in the field of Internet security. In this paper, a space-time graph model is built to identify the key nodes in LSCNs, and a DDoS attack is adopted as the main means to attack the key nodes. The scenarios of two-satellite-key-node and multi-satellite-key-node attacks are considered, and their security performance against DDoS attacks is also analyzed. The simulation results show that the transmission path of key satellite nodes will change rapidly after being attacked, and the average end-to-end delay and packet loss are linearly related to the number of key-node attacks. This work provides a comprehensive analysis of the security performance of LSCNs under a DDoS attack and theoretical support for future research on anti-DDoS attack strategies for LSCNs.

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“…Another approach is to use a filter or firewall to prevent malicious UDP packets from entering. However, firewalls take up many system resources, and there is no guarantee that they will not be broken through new attack methods [4]. Therefore, there is an urgent need for a more effective and better defensive approach, especially in Internet of Things (IoT) applications.…”

Section: Introductionmentioning

confidence: 99%

Counteracting UDP Flooding Attacks in SDN

Wei

Tung

2020

Electronics

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Software-defined networking (SDN) is a new networking architecture with a centralized control mechanism. SDN has proven to be successful in improving not only the network performance, but also security. However, centralized control in the SDN architecture is associated with new security vulnerabilities. In particular, user-datagram-protocol (UDP) flooding attacks can be easily launched and cause serious packet-transmission delays, controller-performance loss, and even network shutdown. In response to applications in the Internet of Things (IoT) field, this study considers UDP flooding attacks in SDN and proposes two lightweight countermeasures. The first method sometimes sacrifices address-resolution-protocol (ARP) requests to achieve a high level of security. In the second method, although packets must sometimes be sacrificed when undergoing an attack before starting to defend, the detection of the network state can prevent normal packets from being sacrificed. When blocking a network attack, attacks from the affected port are directly blocked without affecting normal ports. The performance and security of the proposed methods were confirmed by means of extensive experiments. Compared with the situation where no defense is implemented, or similar defense methods are implemented, after simulating a UDP flooding attack, our proposed method performed better in terms of the available bandwidth, central-processing-unit (CPU) consumption, and network delay time.

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On the Large-Scale Traffic DDoS Threat of Space Backbone Network

Cited by 6 publications

References 12 publications

Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack

Counteracting UDP Flooding Attacks in SDN

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