基于遗传算法和灰色关联分析的击键特征识别算法

基于用户击键特征的身份认证比传统的基于口令的身份认证方法有更高的安全性，现有研究方法中基于神经网络、数据挖掘等算法计算复杂度高，而基于特征向量、贝叶斯统计模型等算法识别精度较低。为了在提高识别精度的同时有效降低计算复杂度，在研究现有算法的基础上提出了一种基于遗传算法与灰色关联分析的击键特征识别算法。该算法利用遗传算法根据用户训练样本确定表征用户击键特征的标准特征序列，通过对当前用户击键特征序列与标准特征序列进行灰色关联分析实现用户身份认证。实验结果表明，该算法识别精度达到神经网络、支持向量机等算法的较高水平，错误拒绝率与错误接受率分别为0%与1.5%。且计算复杂度低，与基于特征向量的算法相近。     

基于流形学习的用户身份认证

本文基于等距映射（ISOMAP）非线性降维算法, 提出了一种新的基于用户击键特征的用户身份认证算法, 该算法用测地距离代替传统的欧氏距离, 作为样本向量之间的距离度量,在用户击键特征向量空间中挖掘嵌入的低维黎曼流形,进行用户识别。用采集到的1500个击键模式数据进行实验测试,结果表明,该文的算法性能优于现有的同类算法,其错误拒绝率（FRR）和错误通过率（FAR）分别是1.65%和0%,低于现有的同类算法。     

基于击键特征的用户身份认证新方法

口令保护机制是绝大多数计算机及网络安全管理中的重要一环，如何有效防止因口令失窃而造成计算机及网络系统的非法进入，一直是一个倍受关注的问题，为此文章提出了一种利用用户口令输入击键特征进行用户身份认证的新方法，该方法利用人在点击键盘时所产生的按键压力和击键频率，构造出能够描述每个用户独有特征的击键特征向量，并由此提出了一种基于有限正例样本集合识别正反例的新算法，相关实验结果表明该身份认证新方法具有较高的用户识别能力。     

基于差别子空间的用户击键特征识别

根据用户的击键行为特征，提出了一种基于差别子空间的识别算法，该算法仅依据用户前几次成功登录的击键特征计算出能够代表用户击键的共性特征向量，进而利用当前用户击键特征向量与共性特征向量的欧几里德距离作为判别依据来判定用户的身份。该算法主要进行内积运算，实现简单且识别速度快，实验结果表明该算法误报率较低，鲁棒性较强。     

基于三分类的击键序列身份认证

针对基于统计学用户击键模式识别算法识别率较低的不足，提出了一种统计学三分类主机用户身份认证算法。该方法通过对当前注册用户的击键特征与由训练样本得到的标准击键特征进行比较，将当前注册用户划分为合法用户类、怀疑类与入侵类三类,对怀疑类采用二次识别机制。 采用动态判别域值，引入了与系统安全性和友好性相关的可控参量k，由系统管理员根据实际确定。并对该算法性能进行了理论分析与实验测试，结果表明该算法在保持贝叶斯统计算法需要训练样本集规模较小、算法收敛速度快优点的基础上,识别精度高于贝叶斯统计算法，错误拒绝率(FRR)和错误通过率(FAR)分别为1.6%和1.5%。     

基于击键压力和时间匹配的异常检测方法

为了有效利用用户的生物特征进行入侵者身份识别,提出了一种基于用户击键特征进行异常检测的新方法.该方法根据人们在击键时所产生的按键压力和时间间隔的惟一性,利用正态分布的特性控制模式库生成方式,构造出能够描述每个用户独有特征的击键特征向量库,然后利用模式匹配算法对新登陆用户进行检测.相关实验验证了该方法具有较高的用户识别能力.     

基于击键特征识别的网络诈骗嫌疑人追踪系统

在互联网日益发展的今天,网络诈骗频繁发生,犯罪嫌疑人的身份极难识别,给网络诈骗案件的侦破带来了困难。该项目以即时聊天工具QQ为例,对网络诈骗嫌疑人身份识别及追踪给出一个完整的解决方案。文章通过键盘击键行为特征识别嫌疑人身份,击键特征能够高精准地区分不同的人,而且键盘普及率很高,这给系统运行提供了硬件环境。通过监听用户键盘击键提取击键特征,对用户进行身份识别,通过IP地理位置信息定位犯罪嫌疑人,从而协助执法人员侦查网络诈骗案件。     

基于模糊逻辑的击键特征用户认证系统

为了增强用户身份认证机制的安全性,在传统的口令认证方式的基础上,提出了一种基于模糊逻辑的击键特征用户认证方法。该方法利用模糊逻辑对用户输入口令的键盘特征进行分析鉴别,并结合用户口令进行用户身份认证。该方法有效弥补了传统的口令机制易被攻击的缺点,有一定的实用性。     

一种面向击键动态身份认证的多模板选择算法

在击键动态身份认证系统中,样本采集和模板建立直接影响系统性能。目前单模板击键认证系统存在无法使错误接受率和错误拒绝率都降低到可接受范围内的不足。为此将多模板思想引入击键认证过程中,在提出最大认证概率算法和最小认证概率算法后,提出均衡概率多模板选择算法,将两种错误率都控制在合理范围内。通过实验同GMMS算法进行对比,并研究了模板数和模板样本数对认证结果的影响,最后与单模板认证系统进行了比较分析。     

一种面向击键动态身份认证的多模板选择算法

在击键动态身份认证系统中,样本采集和模板建立直接影响系统性能。目前单模板击键认证系统存在无法使错误接受率和错误拒绝率都降低到可接受范围内的不足。为此将多模板思想引入击键认证过程中,在提出最大认证概率算法和最小认证概率算法后,提出均衡概率多模板选择算法,将两种错误率都控制在合理范围内。通过实验同GMMS算法进行对比,并研究了模板数和模板样本数对认证结果的影响,最后与单模板认证系统进行了比较分析。     

Keystroke dynamics‐based user authentication service for cloud computing

User authentication is a crucial requirement for cloud service providers to prove that the outsourced data and services are safe from imposters. Keystroke dynamics is a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions designed for certain datasets, for example, a fixed length text typed on a traditional personal computer keyboard and their authentication performances were not acceptable for other input devices nor free length text. Moreover, they suffer from a high dimensional feature space that degrades the authentication accuracy and performance. In this paper, a keystroke dynamics based authentication system is proposed for cloud environments that is applicable to fixed and free text typed on traditional and touch screen keyboards. The proposed system utilizes different feature extraction methods, as a preprocessing step, to minimize the feature space dimensionality. Moreover, different fusion rules are evaluated to combine the different feature extraction methods so that a set of the most relevant features is chosen. Because of the huge number of users' samples, a clustering method is applied to the users' profile templates to reduce the verification time. The proposed system is applied to three different benchmark datasets using three different classifiers. Experimental results demonstrate the effectiveness and efficiency of the proposed system. Copyright © 2015 John Wiley & Sons, Ltd.     

人脸识别在退休金身份认证中的应用

针对传统方法提供的身份证明时效性差、易伪造、识别过程复杂等缺陷,利用人脸图像具有唯一性、稳定性和不需要接触等优点,提出一种基于人脸识别技术的退休金身份认证算法。通过对退休人员人脸进行实时采集,并与服务器人脸库予以匹配,实现退休人员身份真伪判断。将该算法应用于退休金身份认证系统,结果表明,该算法可提高退休金身份认证的准确率,有效阻止身份造假行为。     

A parameterized model to select discriminating features on keystroke dynamics authentication on smartphones

Nowadays, smartphones work not only as personal devices, but also as distributed IoT edge devices uploading information to a cloud. Their secure authentications become more crucial as information from them can spread wider. Keystroke dynamics is one of prominent candidates for authentications factors. Combined with PIN/pattern authentications, keystroke dynamics provide a user-friendly multi-factor authentication for smartphones and other IoT devices equipped with keypads and touch screens. There have been many studies and researches on keystroke dynamics authentication with various features and machine-learning classification methods. However, most of researches extract the same features for the entire user and the features used to learn and authenticate the user’s keystroke dynamics pattern. Since the same feature is used for all users, it may include features that express the users’ keystroke dynamics well and those that do not. The authentication performance may be deteriorated because only the discriminative feature capable of expressing the keystroke dynamics pattern of the user is not selected. In this paper, we propose a parameterized model that can select the most discriminating features for each user. The proposed technique can select feature types that better represent the user’s keystroke dynamics pattern using only the normal user’s collected samples. In addition, performance evaluation in previous studies focuses on average EER(equal error rate) for all users. EER is the value at the midpoint between the FAR(false acceptance rate) and FRR(false rejection rate), FAR is the measure of security, and FRR is the measure of usability. The lower the FAR, the higher the authentication strength of keystroke dynamics. Therefore, the performance evaluation is based on the FAR. Experimental results show that the FRR of the proposed scheme is improved by at least 10.791% from the maximum of 31.221% compared with the other schemes.     

User authentication via keystroke dynamics based on difference subspace and slope correlation degree

User authentication via keystroke dynamics remains a challenging problem due to the fact that keystroke dynamics pattern cannot be maintained stable over time. This paper describes a novel keystroke dynamics-based user authentication approach. The proposed approach consists of two stages, a training stage and an authentication stage. In the training stage, a set of orthogonal bases and a common feature vector are periodically generated from keystroke features of a legitimate user?s several recent successful authentications. In the authentication stage, the current keystroke feature vector is projected onto the set of orthogonal bases, and the distortion of the feature vector between its projection is obtained. User authentication is implemented by comparing the slope correlation degree of the distortion between the common feature vector with a threshold determined periodically using the recent impostor patterns. Theoretical and experimental results show that the proposed method presents high tolerance to instability of user keystroke patterns and yields better performance in terms of false acceptance rate (FAR) and false rejection rate (FRR) compared with some recent methods.     

Continuous Authentications Using Frequent English Terms

Most of the current computer systems authenticate a user’s identity only at the point of entry to the system (i.e., login). However, an effective authentication system includes continuous or frequent monitoring of the identity of a user already logged into a system to ensure the validity of the identity of the user throughout a session. Such a system is called a “continuous or active authentication system.” An authentication system equipped with such a security mechanism protects the system against certain attacks including session hijacking that can be performed later by a malicious user. The aim of this research is to advance the state-of-the-art of the user-active authentication research using keystroke dynamics. Through this research, we assess the performance and influence of various keystroke features on keystroke dynamics authentication systems. In particular, we investigate the performance of keystroke features on a subset of most frequently used English words. The performance of four features including key duration, flight time latency, diagraph time latency, and word total time duration are analyzed. A series of experiments is performed to measure the performance of each feature individually as well as the results from the combinations of these features. More specifically, four machine learning techniques are adapted for the purpose of assessing keystroke authentication schemes. The selected classification methods are Support Vector Machine (SVM), Linear Discriminate Classifier (LDC), K-Nearest Neighbors (K-NN), and Naive Bayesian (NB). Moreover, this research proposes a novel approach based on sequential change-point methods for early detection of an imposter in computer authentication without the needs for any modeling of users in advance, that is, no need for a-priori information regarding changes. The proposed approach based on sequential change-point methods provides the ability to detect the impostor in early stages of attacks. The study is performed and evaluated based on data collected for 28 users. The experimental results indicate that the word total time feature offers the best performance result among all four keystroke features, followed by diagraph time latency. Furthermore, the results of the experiments also show that the combination of features enhances the performance accuracy. In addition, the nearest neighbor method performs the best among the four machine learning techniques.     

新的基于鼠标行为的持续身份认证方法

With the rapid development of Internet technologies, security issues have always been the hot topics. Continuous identity authentication based on mouse behavior plays a crucial role in protecting computer systems, but there are still some problems to be solved. Aiming at the problems of low authentication accuracy and long authentication latency in mouse behavior authentication method, a new continuous identity authentication method based on mouse behavior was proposed. The method divided the user’s mouse event sequence into corresponding mouse behaviors according to different types, and mined mouse behavior characteristics from various aspects based on mouse behaviors. Thereby, the differences in mouse behavior of different users can be better represented, and the authentication accuracy can be improved. Besides, the importance of mouse behavior features was obtained by the ReliefF algorithm, and on this basis, the irrelevant or redundant features of mouse behavior were removed by combining the neighborhood rough set to reduce model complexity and modeling time. Moreover binary classification was adopted. The algorithm performed the training of the authentication model. During identity authentication, the authentication model was used to obtain a classification score based on the mouse behavior collected each time, and then the user’s trust value was updated in combination with the trust model. When the user’s trust value fell below the threshold of the trust model, it might be judged as illegal user. The authentication effect of the proposed method was simulated on the Balabit and DFL datasets. The results show that, compared with the methods in other literatures, this method not only improves the authentication accuracy and reduces the authentication latency, but also has a certain robustness to the illegal intrusion of external users. © 2022, Beijing Xintong Media Co., Ltd.. All rights reserved.