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Shaleen Shah

dblp:07/113 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Network security › intrusion detection and prevention
intrusion detection
0.112007
DoWitcher: Effective Worm Detection and Containment in the Internet Core · INFOCOM 2007
Malware analysis › malware defense
worm containment
0.112007
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
0.112007
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
YearPublicationVenuePosition
2007 DoWitcher: Effective Worm Detection and Containment in the Internet Core
abstract
Enterprise 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
INFOCOM2