Biblio

In this paper, development of cyber communication package in the application of grid connected solar system has been presented. Here, implemented communication methodology supports communication process with reduced latency, high security arrangement with various degrees of freedom. Faithful transferring of various electrical data for the purpose of measurement, monitoring and controlling actions depend on the bidirectional communication strategy. Thus, real-time communication of data through cyber network has been emphasized in this paper. The C\# language based coding is done to develop the communication program. The notable features of proposed communication process are reduction of latency during data exchange by usage of advanced encryption standard (AES) algorithm, tightening of cyber security arrangement by implementing secured socket layer (SSL) and Rivest, Shamir and Adleman (RSA) algorithms. Various real-time experiments using internet connected computers have been done to verify the usability of the proposed communication concept along with its notable features in the application.

It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.

Continuous Authentication by analysing the user's behaviour profile on the computer input devices is challenging due to limited information, variability of data and the sparse nature of the information. As a result, most of the previous research was done as a periodic authentication, where the analysis was made based on a fixed number of actions or fixed time period. Also, the experimental data was obtained for most of the previous research in a very controlled condition, where the task and environment were fixed. In this paper, we will focus on actual continuous authentication that reacts on every single action performed by the user. The experimental data was collected in a complete uncontrolled condition from 52 users by using our data collection software. In our analysis, we have considered both keystroke and mouse usages behaviour pattern to avoid a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The result we have obtained from this research is satisfactory enough for further investigation on this domain.

In this research, we focus on context independent continuous authentication that reacts on every separate action performed by a user. The experimental data was collected in a complete uncontrolled condition from 53 users by using our data collection software. In our analysis, we considered both keystroke and mouse usage behaviour patterns to prevent a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The best result obtained from this research is that for 47 bio-metric subjects we have on average 275 actions required to detect an imposter where these biometric subjects are never locked out from the system.