Shing-hon Lau

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Demo Data from SIGIR 2026 / reviewers in the wild / expert

dblp:78/608 · DBLP profile ↗

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2ranked-venue papers

0as first author

0since 2021 · last 2009

—ORCID · none

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

Computer networks · 1Security and privacy · 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		Security and privacy of machine learning · 50% Network security · 50%
Artificial intelligence 1 paper		Trustworthy machine learning · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Security and privacy of machine learning adversarial attack	0.1	1	2009	ANTIDOTE: understanding and defending against poisoning of anomaly detectors · Internet Measurement Conference 2009
Network security › intrusion detection and prevention › intrusion detection anomaly detection	0.1	1	2009	ANTIDOTE: understanding and defending against poisoning of anomaly detectors · Internet Measurement Conference 2009
Network security › intrusion detection and prevention intrusion detection	0.1	1	2009	ANTIDOTE: understanding and defending against poisoning of anomaly detectors · Internet Measurement Conference 2009
Security and privacy of machine learning poisoning attack	0.1	1	2009	ANTIDOTE: understanding and defending against poisoning of anomaly detectors · Internet Measurement Conference 2009
Machine learning › Trustworthy machine learning robustness	0.0	1	2009	ANTIDOTE: understanding and defending against poisoning of anomaly detectors · Internet Measurement Conference 2009
Machine learning › Trustworthy machine learning › robustness › data poisoning training data manipulation	0.0	1	2009	ANTIDOTE: understanding and defending against poisoning of anomaly detectors · Internet Measurement Conference 2009

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

statistical machine learning · 0.2

Year	Publication	Venue	Position
2009	ANTIDOTE: understanding and defending against poisoning of anomaly detectors abstract Statistical machine learning techniques have recently garnered increased popularity as a means to improve network design and security. For intrusion detection, such methods build a model for normal behavior from training data and detect attacks as deviations from that model. This process invites adversaries to manipulate the training data so that the learned model fails to detect subsequent attacks. Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang 0001, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, J. D. Tygar	Internet Measurement Conference	5
2008	Evading Anomaly Detection through Variance Injection Attacks on PCA Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang 0001, Anthony D. Joseph, Shing-hon Lau, Nina Taft, J. D. Tygar	RAID	5