Cyber attacks has always been of a great concern. Websites and services with poor security layers are the most vulnerable to such cyber attacks. The attackers can easily access sensitive data like credit card details and social security number from such vulnerable services. Currently to stop cyber attacks, various different methods are opted from using two-step verification methods like One-Time Password and push notification services to using high-end bio-metric devices like finger print reader and iris scanner are used as security layers. These current security measures carry a lot of cons and the worst is that user always need to carry the authentication device on them to access their data. To overcome this, we are proposing a technique of using keystroke dynamics (typing pattern) of a user to authenticate the genuine user. In the method, we are taking a data set of 51 users typing a password in 8 sessions done on alternate days to record mood fluctuations of the user. Developed and implemented anomaly-detection algorithm based on distance metrics and machine learning algorithms like Artificial Neural networks (ANN) and convolutional neural network (CNN) to classify the users. In ANN, we implemented multi-class classification using 1-D convolution as the data was correlated and multi-class classification with negative class which was used to classify anomaly based on all users put together. We were able to achieve an accuracy of 95.05% using ANN with Negative Class. From the results achieved, we can say that the model works perfectly and can be bought into the market as a security layer and a good alternative to two-step verification using external devices. This technique will enable users to have two-step security layer without worrying about carry an authentication device.

翻译：网络攻击一直是一个重大关切点。安全层级较弱的网站和服务最容易受到此类网络攻击。攻击者可以从这些脆弱服务中轻松获取信用卡详情和社会安全号码等敏感数据。目前，为了阻止网络攻击，人们采用了多种方法，从使用两步验证（如一次性密码和推送通知服务）到采用高端生物识别设备（如指纹读取器和虹膜扫描仪）作为安全层。这些现有的安全措施存在许多缺点，最严重的是用户必须始终随身携带身份验证设备才能访问其数据。为了克服这一点，我们提出了一种利用用户击键动力学（打字模式）来验证真实用户的技术。在该方法中，我们采用了一个数据集，该数据集包含51名用户在8个会话中（每隔一天进行）输入密码的记录，以捕捉用户情绪波动。我们开发并实现了基于距离度量的异常检测算法以及人工神经网络和卷积神经网络等机器学习算法，以对用户进行分类。在人工神经网络中，我们利用一维卷积实现了多类分类（因为数据存在相关性），以及带有负类的多类分类（用于基于所有用户共同分类异常）。使用带有负类的人工神经网络，我们达到了95.05%的准确率。从所获得的结果来看，我们可以说该模型运行完美，能够作为安全层推向市场，并且是使用外部设备进行两步验证的良好替代方案。该技术将使用户能够拥有两步安全层，而无需担心携带身份验证设备。