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Oleg M. Kolesnikov

dblp:67/5346 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

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

Security and privacy · 2

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
2 papers
Network security · 62% Malware analysis · 38%

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

TopicWeightPapersLastEvidence papers
Malware analysis › malware obfuscation
polymorphic malware
0.112006
Polymorphic Blending Attacks · USENIX Security Symposium 2006
Network security › intrusion detection and prevention › intrusion detection
anomaly detection
0.012003
Anomaly Detection Using Call Stack Information · S&P 2003
Network security › intrusion detection and prevention
intrusion detection
0.012003
Anomaly Detection Using Call Stack Information · S&P 2003
Network security › attack strategy
intrusion detection evasion
0.012006
Polymorphic Blending Attacks · USENIX Security Symposium 2006

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

polymorphism · 0.1encryption · 0.1return address extraction · 0.0abstract execution path · 0.0
YearPublicationVenuePosition
2006 Polymorphic Blending Attacks
Prahlad Fogla, Monirul Islam Sharif, Roberto Perdisci, Oleg M. Kolesnikov, Wenke Lee
USENIX Security Symposium4
2003 Anomaly Detection Using Call Stack Information
abstract
The call stack of a program execution can be a very good information source for intrusion detection. There is no prior work on dynamically extracting information from the call stack and effectively using it to detect exploits. In this paper we propose a new method to do anomaly detection using call stack information. The basic idea is to extract return addresses from the call stack, and generate an abstract execution path between two program execution points. Experiments show that our method can detect some attacks that cannot be detected by other approaches, while its convergence and false positive performance is comparable to or better than the other approaches. We compare our method with other approaches by analyzing their underlying principles and thus achieve a better characterization of their performance, in particular on what and why attacks will be missed by the various approaches.
Henry Hanping Feng, Oleg M. Kolesnikov, Prahlad Fogla, Wenke Lee, Weibo Gong
S&P2