An Optimization Neural Network for Smartphone Data Protection

Wen Chen Hu; Yanjun Zuo; Naima Kaabouch; Lei Chen

doi:10.1109/EIT.2010.5612088

An Optimization Neural Network for Smartphone Data Protection

Wen Chen Hu, Yanjun Zuo, Naima Kaabouch, Lei Chen

Information Technology

Research output: Contribution to book or proceeding › Chapter

2 Scopus citations

Abstract

Since the launch of iPhones in 2007, smartphones become very popular these days. Because of their small sizes and high mobility, smartphones are easily lost or stolen. When people lost their smartphones, they are worried the private data stored in the phones may be revealed to strangers. This research proposes a novel approach for mobile data protection. Mobile usage data is first collected and usage patterns are then discovered and saved. An optimization Hopfield neural network is proposed to match the usage data with the stored usage patterns. When an unusual usage pattern such as an unlawful user trying to access the mobile data is detected, the device will automatically lock itself down until a further action is taken. Experimental results show this method is effective and convenient for mobile data protection.

Original language	American English
Title of host publication	Proceedings of the IEEE International Conference on Electro/Information Technology
DOIs	https://doi.org/10.1109/EIT.2010.5612088
State	Published - May 20 2010

Disciplines

Databases and Information Systems

Keywords

Data protection
Neural network
Optimization
Smartphone

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10.1109/EIT.2010.5612088License: Unspecified

Cite this

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title = "An Optimization Neural Network for Smartphone Data Protection",

abstract = " Since the launch of iPhones in 2007, smartphones become very popular these days. Because of their small sizes and high mobility, smartphones are easily lost or stolen. When people lost their smartphones, they are worried the private data stored in the phones may be revealed to strangers. This research proposes a novel approach for mobile data protection. Mobile usage data is first collected and usage patterns are then discovered and saved. An optimization Hopfield neural network is proposed to match the usage data with the stored usage patterns. When an unusual usage pattern such as an unlawful user trying to access the mobile data is detected, the device will automatically lock itself down until a further action is taken. Experimental results show this method is effective and convenient for mobile data protection.",

keywords = "Data protection, Neural network, Optimization, Smartphone",

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AU - Hu, Wen Chen

AU - Zuo, Yanjun

AU - Kaabouch, Naima

AU - Chen, Lei

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AB - Since the launch of iPhones in 2007, smartphones become very popular these days. Because of their small sizes and high mobility, smartphones are easily lost or stolen. When people lost their smartphones, they are worried the private data stored in the phones may be revealed to strangers. This research proposes a novel approach for mobile data protection. Mobile usage data is first collected and usage patterns are then discovered and saved. An optimization Hopfield neural network is proposed to match the usage data with the stored usage patterns. When an unusual usage pattern such as an unlawful user trying to access the mobile data is detected, the device will automatically lock itself down until a further action is taken. Experimental results show this method is effective and convenient for mobile data protection.

KW - Data protection

KW - Neural network

KW - Optimization

KW - Smartphone

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M3 - Chapter

BT - Proceedings of the IEEE International Conference on Electro/Information Technology

ER -

An Optimization Neural Network for Smartphone Data Protection

Abstract

Disciplines

Keywords

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