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Robert Baykov

dblp:153/5757 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2016
—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% Systems and software security · 38%
Computer networks
1 paper
Software-defined and programmable networks · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software-defined and programmable networks
SDN security
0.212016
Towards SDN-Defined Programmable BYOD (Bring Your Own Device) Security · NDSS 2016
Network security
network access control
0.212016
Towards SDN-Defined Programmable BYOD (Bring Your Own Device) Security · NDSS 2016

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

dynamic binary analysis · 0.2active probing · 0.2
YearPublicationVenuePosition
2016 Towards SDN-Defined Programmable BYOD (Bring Your Own Device) Security
Sungmin Hong, Robert Baykov, Lei Xu 0024, Srinath Nadimpalli, Guofei Gu
NDSS2
2014 AUTOPROBE: Towards Automatic Active Malicious Server Probing Using Dynamic Binary Analysis
abstract
Malware continues to be one of the major threats to Internet security. In the battle against cybercriminals, accurately identifying the underlying malicious server infrastructure (e.g., C&C servers for botnet command and control) is of vital importance. Most existing passive monitoring approaches cannot keep up with the highly dynamic, ever-evolving malware server infrastructure. As an effective complementary technique, active probing has recently attracted attention due to its high accuracy, efficiency, and scalability (even to the Internet level). In this paper, we propose Autoprobe, a novel system to automatically generate effective and efficient fingerprints of remote malicious servers. Autoprobe addresses two fundamental limitations of existing active probing approaches: it supports pull-based C&C protocols, used by the majority of malware, and it generates fingerprints even in the common case when C&C servers are not alive during fingerprint generation. Using real-world malware samples we show that Autoprobe can successfully generate accurate C&C server fingerprints through novel applications of dynamic binary analysis techniques. By conducting Internet-scale active probing, we show that Autoprobe can successfully uncover hundreds of malicious servers on the Internet, many of them unknown to existing blacklists. We believe Autoprobe is a great complement to existing defenses, and can play a unique role in the battle against cybercriminals.
Zhaoyan Xu, Antonio Nappa, Robert Baykov, Guangliang Yang 0001, Juan Caballero, Guofei Gu
CCS3