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Computer systems and networks are increasingly used for many types of applications; as a result the security threats to computers
and networks have also increased significantly. Traditionally, password user authentication is widely used to authenticate
legitimate user, but this method has many loopholes such as password sharing, brute force attack, dictionary attack and more.
The aim of this paper is to improve the password authentication method using Probabilistic Neural Networks (PNNs) with three
types of distance include Euclidean Distance, Manhattan Distance and Euclidean Squared Distance and four features of
keystroke dynamics including Dwell Time (DT), Flight Time (FT), mixture of (DT) and (FT), and finally Up-Up Time (UUT). The
resul
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