activity
20172020
most citedTime Series Segmentation through Automatic Feature Learning

27 citations · 57 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CR202023 cited

UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers

Yuwei Li, Shouling Ji, Yuan Chen +9

A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still cha…

cs.LG201827 cited

Time Series Segmentation through Automatic Feature Learning

Wei-Han Lee, Jorge Ortiz, Bongjun Ko +1

Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and ac…

cs.SI2018

Blind De-anonymization Attacks using Social Networks

Wei-Han Lee, Changchang Liu, Shouling Ji +2

It is important to study the risks of publishing privacy-sensitive data. Even if sensitive identities (e.g., name, social security number) were removed and advanced data perturbati…

cs.CR20171 cited

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Wei-Han Lee, Ruby B. Lee

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and service…

cs.CR20175 cited

Secure Pick Up: Implicit Authentication When You Start Using the Smartphone

Wei-Han Lee, Xiaochen Liu, Yilin Shen +2

We propose Secure Pick Up (SPU), a convenient, lightweight, in-device, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background,…

cs.SI2017

Quantification of De-anonymization Risks in Social Networks

Wei-Han Lee, Changchang Liu, Shouling Ji +2

The risks of publishing privacy-sensitive data have received considerable attention recently. Several de-anonymization attacks have been proposed to re-identify individuals even if…