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Lumin Shi

dblp:238/9759 · DBLP profile ↗
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3ranked-venue papers
2as first author
2since 2021 · last 2023
0000-0002-4031-3912ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 2 first-author · 2 since 2021Computer networks · 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.

Computer networks
1 paper
Network measurement and analytics · 87% Internet architecture and protocols · 13%
Network and information security
1 paper
Network security · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network measurement and analytics › network inference
path inference
0.612022
On Capturing DDoS Traffic Footprints on the Internet · IEEE Trans. Dependable Secur. Comput. 2022
Network measurement and analytics › traffic measurement
traffic monitoring
0.612022
On Capturing DDoS Traffic Footprints on the Internet · IEEE Trans. Dependable Secur. Comput. 2022
Network security › attack resilience › attack mitigation › denial-of-service defense
DDoS defense
0.612022
On Capturing DDoS Traffic Footprints on the Internet · IEEE Trans. Dependable Secur. Comput. 2022
Network security › attack strategy
denial-of-service attack
0.612022
On Capturing DDoS Traffic Footprints on the Internet · IEEE Trans. Dependable Secur. Comput. 2022

Methods — techniques the papers use, named apart from their topics

zooming · 1.1streaming · 1.1PFTrie data structure · 1.1
YearPublicationVenuePosition
2023 DDoS Mitigation Dilemma Exposed: A Two-Wave Attack with Collateral Damage of Millions
Lumin Shi, Jun Li 0001, Devkishen Sisodia, Mingwei Zhang 0004, Alberto Dainotti, Peter L. Reiher
SecureComm (2)1
2022 On Capturing DDoS Traffic Footprints on the Internet
abstract
While distributed denial-of-service (DDoS) attacks are easy to launch and are becoming more damaging, the defense against DDoS attacks often suffers from the lack of relevant knowledge of the DDoS traffic, including the paths the DDoS traffic has used, the source addresses (spoofed or not) that appear along each path, and the amount of traffic per path or per source. Though IP traceback and path inference approaches could be considered, they are either expensive and hard to deploy or inaccurate. We propose PathFinder, a service that a DDoS defense system can use to obtain the footprints of the DDoS traffic to the victim. PathFinder employs an architecture that is easy to implement and deploy on today's Internet, a PFTrie data structure that introduces multiple design features to log traffic at line rate, and streaming and zooming mechanisms that facilitates the storage and transmission of DDoS footprints more efficiently. Our evaluation shows that PathFinder can significantly improve the efficacy of a DDoS defense system, its PFTrie data structure is fast and has a manageable overhead, and its streaming and zooming mechanisms significantly reduce the delay and overhead in transmitting DDoS footprints.
Lumin Shi, Jun Li 0001, Mingwei Zhang 0004, Peter L. Reiher
IEEE Trans. Dependable Secur. Comput.1
2019 On Multi-Point, In-Network Filtering of Distributed Denial-of-Service Traffic
Mingwei Zhang 0004, Lumin Shi, Devkishen Sisodia, Jun Li 0001, Peter L. Reiher
IM2