ROAchain: Securing Route Origin Authorization With Blockchain for Inter-Domain Routing

He, Guobiao; Su, Wei; Gao, Shuai; Yue, Jiarui; Das, Sajal K.

doi:10.1109/tnsm.2020.3015557

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…2) Hijacking: There have been numerous contributions on dealing with the issues of hijacking [41]- [43]. Yi et al [41] studied the vulnerability of intelligent early warning technology in the context of IoT networks.…”

Section: Related Contributionsmentioning

confidence: 99%

“…Vieira et al [42] conceived an architecture for automated intrusion detection based on Big Data, with a special emphasis on the classification, understanding, and prediction of behavioral irregularities in distributed computing environments. He et al [43] proposed a novel blockchain-based technique, which eliminated the security risks of a public key infrastructure. Hence it was robust against prefix hijacking attacks.…”

Section: Related Contributionsmentioning

confidence: 99%

“…Digital currency design for debris removal [61] Multi-sensor space debris tracking [62] Reinforcement learning for debris removal [63] Multibistatic radar [64] Space Debris Reconfiguration through remote uplink [56] Periodic reconfigura -tion [57] SEU TMR [58] Fault-tolerant Viterbi decoder [59] Fault-tolerant Turbo decoder [60] Robust constrained inverse BF [52] Jamming Anti-jamming based on IRS [54] DRL and stackelberg game [53] Satellite diversity [55] Coverageexpanding [48] Inter-system interference REM based spectrum sensing [50] Heuristics based RRM [49] SIC [51] Shortest path routing with risk control [44] Node compromise Stochastic blockchain [45] Artificial neural networks based intrusion detection [46] Physical unclonable password [47] Vulnerability intelligent early warning [41] Hijacking Big Data-aided intrusion detection [42] ROAchain based on blockchain [43] Robust BF [38] Wiretap Threshold-based Scheduling [39] IRS [40] Fig. 3: Development timeline of related works for solving security vulnerabilities.…”

Section: 2021mentioning

confidence: 99%

See 2 more Smart Citations

On the Security of LEO Satellite Communication Systems: Vulnerabilities, Countermeasures, and Future Trends

Yue¹,

An²,

Zhang³

et al. 2022

Preprint

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Section: Related Contributionsmentioning

confidence: 99%

Section: Related Contributionsmentioning

confidence: 99%

Section: 2021mentioning

confidence: 99%

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On the Security of LEO Satellite Communication Systems: Vulnerabilities, Countermeasures, and Future Trends

Yue¹,

An²,

Zhang³

et al. 2022

Preprint

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“…There are studies [7], [14], [15] based on blockchain to prevent route leaks. In the blockchain, ASes share their relationships and store them in blocks.…”

Section: Route Leaks Detectionmentioning

confidence: 99%

“…However, their inference techniques still suffer errors on partial critical links [12]. Studies like [7], [14], [15], [16] do not consider the privacy of AS relationship well and require deployers directly revealing AS relationship information, which makes the deployment hard to proceed.…”

Section: Introductionmentioning

confidence: 99%

Federated Route Leak Detection in Inter-domain Routing with Privacy Guarantee

Zeng¹,

Li²,

Zhang³

et al. 2021

Preprint

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In the inter-domain network, a route leak occurs when a routing announcement is propagated outside of its intended scope, which is a violation of the agreed routing policy. The route leaks can disrupt the internet traffic and cause large outages. The accurately detection of route leaks requires the share of AS business relationship information of ASes. However, the business relationship information between ASes is confidential due to economic issues. Thus, ASes are usually unwilling to revealing this information to the other ASes, especially their competitors. Recent advancements in federated learning make it possible to share data while maintaining privacy. Motivated by this, in this paper we study the route leak problem by considering the privacy of business relationships between ASes, and propose a method for route leak detection with privacy guarantee by using blockchain-based federated learning framework, in which ASes can train a global detection model without revealing their business relationships directly. Moreover, the proposed method provides a self-validation scheme by labeling AS triples with local routing policies, which mitigates route leaks' lack of ground truth. We evaluate the proposed method under a variety of datasets including unbalanced and balanced datasets. The different deployment strategies of the proposed method under different topologies are also examined. The results show that the proposed method has a better performance in detecting route leaks than a single AS detection regardless of whether using balanced or unbalanced datasets. In the analysis of the deployment, the results show that ASes with more peers have more possible route leaks and can contribute more on the detection of route leaks with the proposed method.

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