SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Visual Identification by Signature Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Proceedings of the VLDB Endowment
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Game Bot Detection Based on Avatar Trajectory
ICEC '08 Proceedings of the 7th International Conference on Entertainment Computing
Game bot identification based on manifold learning
Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Trajectory analysis for user verification and recognition
Knowledge-Based Systems
Survey and research direction on online game security
Proceedings of the Workshop at SIGGRAPH Asia
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Many of our activities on computer need a verification step for authorized access. The goal of verification is to tell apart the true account owner from intruders. We propose a general approach for user verification based on user trajectory inputs. The approach is labor-free for users and is likely to avoid the possible copy or simulation from other non-authorized users or even automatic programs like bots. Our study focuses on finding the hidden patterns embedded in the trajectories produced by account users.We employ a Markov chain model with Gaussian distribution in its transitions to describe the behavior in the trajectory. To distinguish between two trajectories, we propose a novel dissimilarity measure combined with a manifold learnt tuning for catching the pairwise relationship. Based on the pairwise relationship, we plug-in any effective classification or clustering methods for the detection of unauthorized access. The method can also be applied for the task of recognition, predicting the trajectory type without pre-defined identity. Given a trajectory input, the results show that the proposed method can accurately verify the user identity, or suggest whom owns the trajectory if the input identity is not provided.