Demo Data from EDBT 2026 / reviewers in the wild / expert
Gi Jun Moon
dblp:271/9731
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1Security and privacy · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Network security · 67% Blockchain and cryptocurrency security · 33% | |
| Computer networks
2 papers |
Network management and operations · 87% Routing and switching · 13% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network management and operations
network monitoring |
0.4 | 1 | 2020 | Network Monitoring for SDN Virtual Networks · INFOCOM 2020 |
Blockchain and cryptocurrency security › blockchain network security
bitcoin network attacks |
0.4 | 1 | 2020 | A Stealthier Partitioning Attack against Bitcoin Peer-to-Peer Network · SP 2020 |
Network security
network partitioning |
0.4 | 1 | 2020 | A Stealthier Partitioning Attack against Bitcoin Peer-to-Peer Network · SP 2020 |
Network security › routing security
routing attacks |
0.4 | 1 | 2020 | A Stealthier Partitioning Attack against Bitcoin Peer-to-Peer Network · SP 2020 |
Routing and switching › inter-domain routing
BGP |
0.1 | 1 | 2020 | A Stealthier Partitioning Attack against Bitcoin Peer-to-Peer Network · SP 2020 |
Cloud and datacenter computing › multi-tenancy
multi-tenant cloud |
0.1 | 1 | 2020 | Network Monitoring for SDN Virtual Networks · INFOCOM 2020 |
Methods — techniques the papers use, named apart from their topics
transmission disaggregation · 0.9peering decision manipulation · 0.9pcollector aggregation · 0.9IP address spoofing · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Network Monitoring for SDN Virtual NetworksabstractThis paper proposes V-Sight, a network monitoring framework for software-defined networking (SDN)-based virtual networks. Network virtualization with SDN (SDN-NV) makes it possible to realize programmable virtual networks; so, the technology can be beneficial to cloud services for tenants. However, to the best of our knowledge, although network monitoring is a vital prerequisite for managing and optimizing virtual networks, it has not been investigated in the context of SDN-NV. Thus, virtual networks suffer from non-isolated statistics between virtual networks, high monitoring delays, and excessive control channel consumption for gathering statistics, which critically hinders the benefits of SDN-NV. To solve these problems, V-Sight presents three key mechanisms: 1) statistics virtualization for isolated statistics, 2) transmission disaggregation for reduced transmission delay, and 3) pCollector aggregation for efficient control channel consumption. V-Sight is implemented on top of OpenVirteX, and the evaluation results demonstrate that V-Sight successfully reduces monitoring delay and control channel consumption up to 454 times. Gyeongsik Yang, Heesang Jin, Minkoo Kang 0001, Gi Jun Moon, Chuck Yoo |
INFOCOM | 4 |
| 2020 | A Stealthier Partitioning Attack against Bitcoin Peer-to-Peer NetworkabstractNetwork adversaries, such as malicious transit autonomous systems (ASes), have been shown to be capable of partitioning the Bitcoin's peer-to-peer network via routing-level attacks; e.g., a network adversary exploits a BGP vulnerability and performs a prefix hijacking attack (viz. Apostolaki et al. [3]). Due to the nature of BGP operation, such a hijacking is globally observable and thus enables immediate detection of the attack and the identification of the perpetrator. In this paper, we present a stealthier attack, which we call the EREBUS attack, that partitions the Bitcoin network without any routing manipulations, which makes the attack undetectable to control-plane and even to data-plane detectors. The novel aspect of EREBUS is that it makes the adversary AS a natural man-in-the-middle network of all the peer connections of one or more targeted Bitcoin nodes by patiently influencing the targeted nodes' peering decision. We show that affecting the peering decision of a Bitcoin node, which is believed to be infeasible after a series of bug patches against the earlier Eclipse attack [29], is possible for the network adversary that can use abundant network address resources (e.g., spoofing millions of IP addresses in many other ASes) reliably for an extended period of time at a negligible cost. The EREBUS attack is readily available for large ASes, such as Tier-1 and large Tier-2 ASes, against the vast majority of 10K public Bitcoin nodes with only about 520 bit/s of attack traffic rate per targeted Bitcoin node and a modest (e.g., 5-6 weeks) attack execution period. The EREBUS attack can be mounted by nation-state adversaries who would be willing to execute sophisticated attack strategies patiently to compromise cryptocurrencies (e.g., control the consensus, take down a cryptocurrency, censor transactions). As the attack exploits the topological advantage of being a network adversary but not the specific vulnerabilities of Bitcoin core, no quick patches seem to be available. We discuss that some naive solutions (e.g., whitelisting, rate-limiting) are ineffective and third-party proxy solutions may worsen the Bitcoin's centralization problem. We provide some suggested modifications to the Bitcoin core and show that they effectively make the EREBUS attack significantly harder; yet, their non-trivial changes to the Bitcoin's network operation (e.g., peering dynamics, propagation delays) should be examined thoroughly before their wide deployment. Muoi Tran, Inho Choi, Gi Jun Moon, Anh V. Vu, Min Suk Kang |
SP | 3 |