Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques
Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques
复制标题
使用软计算技术的基于击键动力学的个人认证
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
M. Akila
中科院分区：
文献类型：
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作者：
M. Karnan;M. Akila
保护敏感数据和计算机系统免受入侵者攻击，同时允许认证用户轻松访问是计算机安全的主要问题之一。传统上，密码一直是控制访问计算机系统的常用方法，但这种方法有许多固有的缺陷。击键动力学是一种很有前途的生物特征识别技术，可以根据对个人打字模式的分析来识别他/她。在实验中，我们测量了延迟、持续时间、有向图及其组合等特征的均值、标准差和中值，并比较了它们的性能。粒子群优化（PSO），遗传算法（GA）和提出的蚁群优化（ACO）用于特征子集的选择。反向传播神经网络（BPNN）用于分类。蚁群算法在特征约简率和分类精度方面优于粒子群算法和遗传算法。使用有向图作为特征进行特征子集选择是一种新颖的方法，具有良好的分类性能。
The need to secure sensitive data and computer systems from intruders, while allowing ease of access for authenticate user is one of the main problems in computer security. Traditionally, passwords have been the usual method for controlling access to computer systems but this approach has many inherent flaws. Keystroke dynamics is a promising biometric technique to recognize an individual based on an analysis of his/her typing patterns. In the experiment, we measure mean, standard deviation and median values of keystroke features such as latency, duration, digraph and their combinations and compare their performance. Particle swarm optimization (PSO), genetic algorithm (GA) and the proposed ant colony optimization (ACO) are used for feature subset selection. Back propagation neural network (BPNN) is used for classification. ACO gives better performance than PSO and GA with regard to feature reduction rate and classification accuracy. Using digraph as the feature for feature subset selection is novel and show good classification performance.