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.

本文提出使用击键动态通过机器学习算法进行用户身份认证，可作为外部设备二步验证的替代方案，提升应用程序安全性和用户体验。基于数据集的距离度量和 ANN、CNN 多分类算法，最终实现了 95.05％的识别准确率。