Demo Data from VLDB 2026 / reviewers in the wild / expert
Y. Guang
dblp:87/2269
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security 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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › intrusion detection and prevention
intrusion detection |
0.0 | 1 | 1999 | A High-Performance Network Intrusion Detection System · CCS 1999 |
Network security › intrusion detection and prevention › intrusion detection › anomaly detection
specification-based intrusion detection |
0.0 | 1 | 1999 | A High-Performance Network Intrusion Detection System · CCS 1999 |
Network security › attack strategy
denial-of-service attack |
0.0 | 1 | 1999 | A High-Performance Network Intrusion Detection System · CCS 1999 |
Methods — techniques the papers use, named apart from their topics
type checking · 0.0pattern matching · 0.0information aggregation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | A High-Performance Network Intrusion Detection SystemabstractIn this paper we present a new approach for network intrusion detection based on concise specifications that characterize normal and abnormal network packet sequences. Our specification language is geared for a robust network intrusion detection by enforcing a strict type discipline via a combination of static and dynamic type checking. Unlike most previous approaches in network intrusion detection, our approach can easily support new network protocols as information relating to the protocols are not hard-coded into the system. Instead, we simply add suitable type definitions in the specifications and define intrusion patterns on these types. We compile these specifications into a high-performance network intrusion detection system. Important components of our approach include efficient algorithms for pattern-matching and information aggregation on sequences of network packets. In particular, our techniques ensure that the matching time is insensitive to the number of patterns characterizing different network intrusions, and that the aggregation operations typically take constant time per packet. Our system participated in an intrusion detection evaluation organized by MIT Lincoln Labs, where our system demonstrated its effectiveness (96% detection rate on low-level network attacks) and performance (real-time detection at 500Mbps), while producing very few false positives (0.05 to 0.1 per attack). R. Sekar 0001, Y. Guang, S. Verma, T. Shanbhag |
CCS | 2 |