Steven Gianvecchio - MITRE
Students: Spring 2024, unless noted otherwise, sessions will be virtual on Zoom.
Detecting Bots in Online Games using Human Observational Proofs
Sep 07, 2011
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Abstract
The abuse of online games by automated programs, known as bots, hasgrown significantly in recent years. The conventional methods for
distinguishing bots from humans, such as CAPTCHAs, are not effective in
a gaming context. This talk presents a non-interactive approach based on
human observational proofs for continuous game bot detection. HOPs
differentiate bots from human players by passively monitoring input
actions that are difficult for current bots to perform in a human-like
manner. The talk describes a prototype HOP-based game bot defense system
that analyzes user-input actions with a cascade-correlation neural
network to distinguish bots from humans. The experimental results show
that the HOP system is effective in capturing game bots in World of
Warcraft, raising the bar against game exploits and forcing attackers to
build more complicated bots for detection evasion in the future.
About the Speaker
Steven Gianvecchio received his Ph.D. in Computer Science from the
College of William and Mary in 2010. He is a Senior Scientist at the
MITRE Corporation, McLean, VA. His research interests include networks,
distributed systems, network monitoring, intrusion detection, traffic modeling, and covert channels.
College of William and Mary in 2010. He is a Senior Scientist at the
MITRE Corporation, McLean, VA. His research interests include networks,
distributed systems, network monitoring, intrusion detection, traffic modeling, and covert channels.
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