Biblio
With the advent of technology and owing to mankind’s reliance on technology, it is of utmost importance to safeguard people’s data and their identity. Biometrics have for long played an important role in providing that layer of security ranging from small scale uses such as house locks to enterprises using them for confidentiality purposes. In this paper we will provide an insight into behavioral biometrics that rely on identifying and measuring human characteristics or behavior. We review different types of behavioral parameters such as keystroke dynamics, gait, footstep pressure signals and more.
With the development of modern High-Speed Railway (HSR) and mobile communication systems, network operators have a strong demand to provide high-quality on-board Internet services for HSR passengers. Multi-path TCP (MPTCP) provides a potential solution to aggregate available network bandwidth, greatly overcoming throughout degradation and severe jitter using single transmission path during the high-speed train moving. However, the choose of MPTCP algorithms, i.e., Coupled or Uncoupled, has a great impact on the performance. In this paper, we investigate this interesting issue in the practical datasets along multiple HSR lines. Particularly, we collect the first-hand network datasets and analyze the characteristics and category of traffic flows. Based on this statistics, we measure and analyze the transmission performance for both mice flows and elephant ones with different MPTCP congestion control algorithms in HSR scenarios. The simulation results show that, by comparing with the coupled MPTCP algorithms, i.e., Fully Coupled and LIA, the uncoupled EWTCP algorithm provides more stable throughput and balances congestion window distribution, more suitable for the HSR scenario for elephant flows. This work provides significant reference for the development of on-board devices in HSR network systems.
One of the basic behavioural biometric methods is keystroke element. Being less expensive and not requiring any extra bit of equipment is the main advantage of keystroke element. The primary concentration of this paper is to give an inevitable review of behavioural biometrics strategies, measurements and different methodologies and difficulties and future bearings specially of keystroke analysis and mouse dynamics. Keystrokes elements frameworks utilize insights, e.g. time between keystrokes, word decisions, word mixes, general speed of writing and so on. Mouse Dynamics is termed as the course of actions captured from the moving mouse by an individual when interacting with a GUI. These are representative factors which may be called mouse dynamics signature of an individual, and may be used for verification of identity of an individual. In this paper, we compare the authentication system based on keystroke dynamics and mouse dynamics.
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.
This paper proposes and describes an active authentication model based on user profiles built from user-issued commands when interacting with GUI-based application. Previous behavioral models derived from user issued commands were limited to analyzing the user's interaction with the *Nix (Linux or Unix) command shell program. Human-computer interaction (HCI) research has explored the idea of building users profiles based on their behavioral patterns when interacting with such graphical interfaces. It did so by analyzing the user's keystroke and/or mouse dynamics. However, none had explored the idea of creating profiles by capturing users' usage characteristics when interacting with a specific application beyond how a user strikes the keyboard or moves the mouse across the screen. We obtain and utilize a dataset of user command streams collected from working with Microsoft (MS) Word to serve as a test bed. User profiles are first built using MS Word commands and identification takes place using machine learning algorithms. Best performance in terms of both accuracy and Area under the Curve (AUC) for Receiver Operating Characteristic (ROC) curve is reported using Random Forests (RF) and AdaBoost with random forests.