Demo Data from VLDB 2026 / reviewers in the wild / expert
Shaleen Shah
dblp:07/113
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer 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.
| Network and information security
1 paper |
Network security · 67% Malware analysis · 33% |
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 |
0.1 | 1 | 2007 | DoWitcher: Effective Worm Detection and Containment in the Internet Core · INFOCOM 2007 |
Malware analysis › malware defense
worm containment |
0.1 | 1 | 2007 | DoWitcher: Effective Worm Detection and Containment in the Internet Core · INFOCOM 2007 |
Network security › intrusion detection and prevention › intrusion detection › malicious traffic detection
worm detection |
0.1 | 1 | 2007 | DoWitcher: Effective Worm Detection and Containment in the Internet Core · INFOCOM 2007 |
Methods — techniques the papers use, named apart from their topics
traffic feature analysis · 0.1longest common subsequence · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | DoWitcher: Effective Worm Detection and Containment in the Internet CoreabstractEnterprise networks are increasingly offloading the responsibility for worm detection and containment to the carrier networks. However, current approaches to the zero-day worm detection problem such as those based on content similarity of packet payloads are not scalable to the carrier link speeds (OC-48 and up-wards). In this paper, we introduce a new system, namely DoWitcher, which in contrast to previous approaches is scalable as well as able to detect the stealthiest worms that employ low-propagation rates or polymorphisms to evade detection. DoWitcher uses an incremental approach toward worm detection: First, it examines the layer-4 traffic features to discern the presence of a worm anomaly; Next, it determines a flow-filter mask that can be applied to isolate the suspect worm flows and; Finally, it enables full-packet capture of only those flows that match the mask, which are then processed by a longest common subsequence algorithm to extract the worm content signature. Via a proof-of-concept implementation on a commercially available network analyzer processing raw packets from an OC-48 link, we demonstrate the capability of DoWitcher to detect low-rate worms and extract signatures for even the polymorphic worms. Supranamaya Ranjan, Shaleen Shah, Antonio Nucci, Maurizio M. Munafò, Rene L. Cruz, S. Muthukrishnan 0001 |
INFOCOM | 2 |