A hybrid GA-PSO fuzzy system for user identification on smart phones

Authors:
Muhammad Shahzad;Saira Zahid;Muddassar Farooq
Affiliations:
National University of Computer and Emerging Science (FAST-NUCES), Islamabad, Pakistan;National University of Computer and Emerging Science (FAST-NUCES), Islamabad, Pakistan;National University of Computer and Emerging Science (FAST-NUCES), Islamabad, Pakistan
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 6
Cited 2

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh

Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
A Fuzzy Classifier System Using the Pittsburgh Approach

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Seeing-Is-Believing: Using Camera Phones for Human-Verifiable Authentication

SP '05 Proceedings of the 2005 IEEE Symposium on Security and Privacy
GA SVM wrapper ensemble for keystroke dynamics authentication

ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
User authentication based on JPEG2000 images

VLBV'05 Proceedings of the 9th international conference on Visual Content Processing and Representation

Keystroke-Based User Identification on Smart Phones

RAID '09 Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection
Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it

Proceedings of the 19th annual international conference on Mobile computing & networking

Quantified Score

Hi-index	0.00

Visualization

Abstract

The major contribution of this paper is a hybrid GA-PSO fuzzy user identification system, UGuard, for smart phones. Our system gets 3 phone usage features as input to identify a user or an imposter. We show that these phone usage features for different users are diffused; therefore, we justify the need of a front end fuzzy classifier for them. We further show that the fuzzy classifier must be optimized using a back end online dynamic optimizer. The dynamic optimizer is a hybrid of Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA). We have collected phone usage data of 10 real users having Symbian smart phones for 8 days. We evaluate our UGuard system on this dataset. The results of our experiments show that UGuard provides on the average an error rate of 2% or less. We also compared our system with four classical classifiers -- Na¨1ve Bayes, Back Propagation Neural Networks, J48 Decision Tree, and Fuzzy System -- and three evolutionary schemes -- fuzzy system optimized by ACO, PSO, and GA. To the best of our knowledge, the current work is the first system that has achieved such a small error rate. Moreover, the system is simple and efficient; therefore, it can be deployed on real world smart phones.