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EDBT 2026 Demo / reviewers in the wild / expert

Junchi Lei

dblp:377/2239 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0007-1563-6465ORCID · reported

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

Security and privacy · 1 · 1 since 2021

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
Malware analysis · 56% Network security · 44%
Artificial intelligence
1 paper
Trustworthy machine learning · 50% Time series and sequential data · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Time series and sequential data › non-stationary environments
concept drift		 0.912025
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification · CCS 2025
Machine learning › Trustworthy machine learning
robustness		 0.912025
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification · CCS 2025
Malware analysis › android malware
android malware classification		 0.912025
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification · CCS 2025
Network security › intrusion detection and prevention › intrusion detection
concept drift adaptation		 0.912025
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification · CCS 2025
Malware analysis › mobile malware detection
android malware detection		 0.312025
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification · CCS 2025

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

model retraining · 1.7explanatory detection · 1.7
YearPublicationVenuePosition
2025 Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification
abstract
Machine learning-based Android malware classifiers struggle with concept drift: the rapid evolution of malware, especially with new families, can depress classification accuracy to near-random levels. Previous research has largely centered on detecting drift samples, with expert-led label revisions on these samples to guide model retraining. However, these methods often lack a comprehensive understanding of malware concepts and provide limited guidance for effective drift adaptation, leading to high human labeling costs.
Yiling He, Junchi Lei, Zhan Qin, Kui Ren 0001, Chun Chen 0001
 CCS2