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This paper presents a methodology for improving the security of identification and authentication processes using Keystroke Dynamics (KSD). KSD is considered a behavioral biometric operating as a second level of security along with the login process after inserting user name and password. KSD is mainly about observing the way in which the user types. Firstly, we propose four new time features; these features represent the user's behavior. Secondly, due to the unavailability of standard dataset, a new behavioral dataset is built. Thirdly, we propose employing KSD on CAPTCHA Code for the identification process. In this research, we applied three different classification techniques namely: J48, Random Forest and Multi-layer Perceptron (MLP), to accurately identify the user behavior (legitimate or illegitimate) and its authority. Random Forest showed the best result for the identification with accuracy (93.13%), however for the authorization process the highest accuracy was obtained using MLP (94.90%). [ABSTRACT FROM AUTHOR]

This paper proposes how the Mobile Agents (MA) supported by statistical filtering based on the Wiener filter algorithm can present a reasonable new scheme to assist in achieving distributed fault detection. The statistical method of the Wiener filter captures the abnormal changes in the behaviour of the MIB variables. We have effectively demonstrated that the MAs can be deployed to examine and monitor devices in virtual sub-domains using polling procedures that proved to be more efficient when the size of the domain is small. Our algorithm was implemented on data obtained from two different scenarios using four different fault case studies. Each agent responsible for a particular domain and device, proving to be more scalable and efficient, carries out the filtering process concurrently. The algorithm has detected the network faults in the network at a very early stage, hence allowing the agents to very quickly and effectively raise the alarms. The process is simple and effective, and detects anomalies in the network immediately, after polling. [ABSTRACT FROM AUTHOR]