Multi-Layer Extreme Learning Machine-Based Keystroke Dynamics Identification for Intelligent Keyboard

作     者：Zhao, Guangquan Wu, Zhiyi Gao, Yongcheng Niu, Guangxing Wang, Zhong Lin Zhang, Bin 

作者机构：Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Georgia Inst Technol Sch Mat Sci & Engn Atlanta GA 30332 USA Univ South Carolina Dept Elect Engn Columbia SC 29208 USA 

出 版 物：《IEEE SENSORS JOURNAL》 (IEEE传感器杂志)

年 卷 期：2021年第21卷第2期

页      面：2324-2333页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0702[理学-物理学] 

主　　题：Feature extraction Keyboards Training Electrodes Fingers Sensors Password Multi-layer extreme learning machine classification auto-encoder intelligent keyboard keystroke dynamics 

摘      要：Information security plays critical roles in modern society. However, traditional security measures like passwords, tokens and personal ID numbers only provide limited protection. Inspired by the fast training speed of extreme learning machine (ELM) and the promising feature extraction capability of extreme learning machine auto-encoder (ELM-AE), this paper proposes a keystroke dynamics identification method for intelligent keyboard (IKB) based on multi-layer extreme learning machine (ML-ELM). The IKB, as first demonstrated by Wang s group, is a self-powered, non-mechanical perforated keyboard that converts mechanical stimuli applied to the keyboard into electrical signals without the needing an external power source. ML-ELM is a multilayer neural network stacking on top of ELM-AE. Our major contribution is to develop an accurate and efficient keystroke dynamics identification method based on ML-ELM. One significant advantage of the proposed method is that it does not rely on manual feature extraction and selection. In other words, the raw current signals obtained by IKB are directly the input to the network. The network structure is determined by grid search, which minimizes the human involvement. The other one is that the whole training process of the model does not require fine tuning, which makes the training process significantly faster than that of existing deep networks. Experimental results demonstrate that the proposed method has excellent timeliness whereas obtaining high identification accuracy. The proposed method has great potential in applications to computer or network access control, online payment, and cyber security.