An optimization neural network for smartphone data protection
Wen‐Chen Hu, Yanjun Zuo, Naima Kaabouch, Lei Chen · 2010
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.