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 共查询到18条相似文献,搜索用时 140 毫秒
1.
王晅  陈伟伟  马建峰 《计算机应用》2007,27(5):1054-1057
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。  相似文献   

2.
以用户击键特征为依据,提出了一种基于谱系聚类法的识别算法。该算法通过谱系聚类法对用户击键特征向量进行聚类分析,并形成各向量之间的谱系关系,从而由谱系关系来对击键特征向量进行识别。该算法的主要特点是使用欧氏距离进行分类,算法实现简单并且识别速度快。由于采用的聚类算法的简单性,其识别精度尚有待提高,因此该算法适用于击键识别的简单应用。  相似文献   

3.
梁娟  王晅  陈伟伟  傅博  王益艳 《计算机工程》2007,33(11):204-205,221
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。  相似文献   

4.
击键特征是一种能反映用户行为的动态特征,可作为用户身份识别的信息源。传统的认证方法通常仅采用击键特征向量中所包含的每个特征值的大小来进行身份识别,而没有利用任意两个相邻特征值之间的变化率,在一些情况下,可能导致识别准确度不理想。针对上述问题,定义了一种新颖的击键特征曲线差异度的概念,并由此提出基于击键特征曲线差异度的认证算法。该认证算法不仅利用了常规的击键特征信息,还首次引入了任意两个相邻特征值之间的变化率信息,使算法性能得到显著提高。实验结果表明,相比于曼哈顿距离算法、统计学算法、神经网络算法和机器学习算法,新算法的错误拒绝率、错误接受率和相等错误率更低,识别准确度更高,效果更好。  相似文献   

5.
由于计算机用户对键盘的熟悉程度、击键习惯等不尽相同,每个用户都具有自己独特的击键生物特征,对于某个用户来说,其击键特征为正常类,其他所有用户为异常类,这可以利用模式识别中的单类分类器来解决,本文设计基于支持向量数据描述(SVDD)的击键生物特征身份认证系统模型,将该方法与BP、RBF和SOM方法进行对比,证实SVDD具有较好的识别效果,它可将非法用户误接受率从28.9%降低到0.28%,最后给出一个嵌入Windows用户登录中的口令+击键特征身份认证的实现技术.  相似文献   

6.
基于等距映射(ISOMAP)非性降维算法,提出了一种新的基于用户击键特征的用户身份认证算法.谊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O%,低于现有的同类算法。  相似文献   

7.
用户击键行为作为一种生物特征,具有采集成本低、安全性高的特点。然而,现有的研究方法和实验环境都是基于实验室数据,并不适用于极度不平衡的真实数据。比如,在实验室数据上效果出色的分类算法在真实数据上却无法应用。针对此问题,提出了基于真实击键行为数据的用户识别算法。该方法将聚类算法和距离算法结合起来,通过比较新来的击键行为和历史击键行为相似度以实现用户识别。实验结果表明,该算法在100名用户的3015条真实击键记录组成的数据集上准确率达到88.22%,在投入实际应用后,随着样本集的增大算法的准确率还可以进一步提升。  相似文献   

8.
访问控制在安全领域起着越来越重要的作用,个人的一些生物特征具有唯一性而且难以改变,利用这些特征识别用户身份,从而进行系统访问控制具有很强的优势。论文首先通过对人体生物击键特性的分析,阐述了基于生物击键特性的访问控制过程;然后对已有的击键序列识别算法进行分析,提出了一种新的自适应击键特性识别算法。该算法能够适应用户敲击键盘熟练程度的变化,并可防止输入过程中的个别“奇点”影响整体的识别效果。算法改进的讨论中,通过改变特征数据的存储结构使得用户修改密码后,算法仍然能够从历史数据中挖掘击键特性,提高学习效率。最后的测试结果和理论分析都表明该方法对于因密钥丢失导致的系统失控具有很好的防护作用。  相似文献   

9.
刘磊  陈兴蜀  尹学渊  段意  吕昭 《计算机应用》2011,31(12):3268-3270
基于网络用户的访问记录,提出了采用特征加权的朴素贝叶斯分类算法对用户进行识别。首先利用基于WinPcap框架的数据采集系统对用户访问记录进行采集,通过分析记录从5个方面对用户特征进行统计,并经过筛选后对特征进行选取,最后采用特征加权的朴素贝叶斯分类算法对3300个测试样本进行识别,识别率达到了85.73%。实验结果表明该算法能够有效实现对网络用户身份的识别。  相似文献   

10.
击键特征是一种能反映用户行为的动态特征,可作为识别用户的信息源。传统方法不仅要求收集大量击键样本来建立识别模型,并且同时需要正例样本与反例样本。但在实际应用中,需要用户提供大量的训练样本是不现实的,并且反例样本收集比正例样本收集困难。为此,提出一种新的以击键序列为信息源的主机入侵检测模型。在小样本和仅有正例的情况下,通过One-Class支持向量机(OCSVM)来训练检测模型,通过对用户的击键行为是否偏离正常模型来检测入侵。仿真实验结果表明该模型具有较好的检测效果。  相似文献   

11.
Biometric authentication systems represent a valid alternative to the conventional username–password based approach for user authentication. However, authentication systems composed of a biometric reader, a smartcard reader, and a networked workstation which perform user authentication via software algorithms have been found to be vulnerable in two areas: firstly in their communication channels between readers and workstation (communication attacks) and secondly through their processing algorithms and/or matching results overriding (replay attacks, confidentiality and integrity threats related to the stored information of the networked workstation). In this paper, a full hardware access point for HPC environments is proposed. The access point is composed of a fingerprint scanner, a smartcard reader, and a hardware core for fingerprint processing and matching. The hardware processing core can be described as a Handel-C algorithmic-like hardware programming language and prototyped via a Field Programmable Gate Array (FPGA) based board. The known indexes False Acceptance Rate (FAR) and False Rejection Rate (FRR) have been used to test the prototype authentication accuracy. Experimental trials conducted on several fingerprint DBs show that the hardware prototype achieves a working point with FAR=1.07% and FRR=8.33% on a proprietary DB which was acquired via a capacitive scanner, a working point with FAR=0.66% and FRR=6.13% on a proprietary DB which was acquired via an optical scanner, and a working point with FAR=1.52% and FRR=9.64% on the official FVC2002_DB2B database. In the best case scenario (depending on fingerprint image size), the execution time of the proposed recognizer is 183.32 ms.  相似文献   

12.
庞永春  孙子文  王尧 《计算机应用》2015,35(6):1780-1784
针对智能手机所面临的信息安全威胁问题,提出一种基于手机触摸屏传感器的多点触摸身份认证方法。首先由触摸屏传感器采集手指滑动原始数据序列,通过平滑去噪、位置及长度归一化预处理;然后提取手势运动一阶、二阶归一化导数序列及运动方向为身份验证特征序列;最后采用模板匹配方法,使用动态时间规整算法匹配比较注册模板特征序列与测试特征序列,判断用户身份真实性。仿真结果表明,所提算法对不同用户身份认证的平均错误拒绝率和错误接受率分别为3.83%和2.07%,与使用径向基函数为核函数的支持向量分布估计(SVDE)算法相比,平均错误拒绝率和错误接受率分别降低1.81%和2.35%。经性能分析,所提算法能明显提高身份认证的准确性。  相似文献   

13.
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.  相似文献   

14.
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.  相似文献   

15.
Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify him. The features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction approach. In this work, we investigate the ability of Deep Learning (DL) to automatically discover useful features of touch gesture and use them to authenticate the user. Four different models are investigated Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) combined with LSTM (CNN-LSTM), and CNN combined with GRU(CNN-GRU). In addition, different regularization techniques are investigated such as Activity Regularizer, Batch Normalization (BN), Dropout, and LeakyReLU. These deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch authentication. The result reported in terms of authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR). The best result we have been obtained was 96.73%, 96.07% and 96.08% for training, validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model, while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was 0.0530. For BioIdent dataset the best results have been obtained was 84.87%, 78.28% and 78.35% for Training, validation and testing accuracy respectively with CNN-LSTM model. The use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.  相似文献   

16.
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。  相似文献   

17.
This paper presents the study to develop and evaluate techniques to authenticate valid users, using the keystroke dynamics of a user's PIN number entry on a numerical keypad, with force sensing resistors. Added with two conventional parameter lists of elements, i.e. digraph latency times and key hold times, keying force was chosen as a third element. Two experiments were conducted. The first experiment was to evaluate whether the three types of elements derived from keystrokes have a significant effect for subjects and to examine how trials and session effects generated the variation of the three elements. The second experiment was to demonstrate the system performance by calculating the False Rejection Rate (FRR) and the False Acceptance Rate (FAR) of the system. In the second experiment, a total of 20 keystrokes were recorded from each subject one week after the memorizing session, in order to evaluate the FRR of the system. To evaluate the FAR of the system, the subjects pretended to be impostors, and therefore they repeatedly watched videotaped pass trials made by a valid user as many times as they desired, and tried to imitate the keystroke dynamics of the valid users. The subject's keystrokes were then evaluated on whether they could fool the system. The first experiment, ANOVA revealed that a significant effect of subject was found on each of all three elements. Trial was not significantly affected to digraph latency times and peak force; however, it was significantly affected to key hold times. There was a trend that keystroke dynamics characterized by each element showed reformation of their patterns and reached a steady state over the 10 weeks of experimental sessions. The results of the second experiment showed the average equal error rate to be 2.4%. The results of system performance were compared with those of other studies and concluded that it was difficult to obtain enough information to behave as a perfect impostor by monitoring the videotaped keystrokes.  相似文献   

18.
This paper presents the study to develop and evaluate techniques to authenticate valid users, using the keystroke dynamics of a user's PIN number entry on a numerical keypad, with force sensing resistors. Added with two conventional parameter lists of elements, i.e. digraph latency times and key hold times, keying force was chosen as a third element. Two experiments were conducted. The first experiment was to evaluate whether the three types of elements derived from keystrokes have a significant effect for subjects and to examine how trials and session effects generated the variation of the three elements. The second experiment was to demonstrate the system performance by calculating the False Rejection Rate (FRR) and the False Acceptance Rate (FAR) of the system. In the second experiment, a total of 20 keystrokes were recorded from each subject one week after the memorizing session, in order to evaluate the FRR of the system. To evaluate the FAR of the system, the subjects pretended to be impostors, and therefore they repeatedly watched videotaped pass trials made by a valid user as many times as they desired, and tried to imitate the keystroke dynamics of the valid users. The subject's keystrokes were then evaluated on whether they could fool the system. The first experiment, ANOVA revealed that a significant effect of subject was found on each of all three elements. Trial was not significantly affected to digraph latency times and peak force; however, it was significantly affected to key hold times. There was a trend that keystroke dynamics characterized by each element showed reformation of their patterns and reached a steady state over the 10 weeks of experimental sessions. The results of the second experiment showed the average equal error rate to be 2.4%. The results of system performance were compared with those of other studies and concluded that it was difficult to obtain enough information to behave as a perfect impostor by monitoring the videotaped keystrokes.  相似文献   

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