User Authentication In a Voice Activated Computing System

This document describes a technique for processing voice commands received by a voice activated computing system to identify one or more tasks or actions to be performed based on the voice commands. The system determines the type of tasks or actions identified, and based on their type determines whether execution of those tasks or action requires user authentication. If user authentication is required, the system pauses the tasks or actions and executes one or more authentication actions that can authenticate the user. If the user authentication is successful, the system can continue to execute the paused tasks or actions. However, if the authentication fails, the system can terminate or disable the tasks or actions

Human user authentication based on mouse dynamics: a feasibility study

Security problems have been discussed for a long time in the past recent decades in many fields such as communication, networking and user authentication. Security and authentication methods have also been explored for a long time by many researchers, and many ecient ways have been developed and used in modern society. Password and fingerprint based user authentication methods are most common user authentication methods being used in our daily lives. With computers and smart phones population growing vastly, we need to put more attention on the security methods. However, those traditional authentication methods are not safe and ecient enough. Passwords are stolen and revealed to hackers, while fingerprint can be easily got from an authenticated person. We moved our eyes on another way of security and authentication- biometric kinesiology. The muscle in our body can remember the movement if we practiced an action a lot, and that memory is built in the body, not in our brain memory, which means that we cannot forget a practiced action in the way we forget a password. We proposed to use the action with mouse from an authenticated user as the password of a system, in which only the user perform right action can be regarded as an authenticated user. Otherwise the system will reject the user. This movement is hard to mimic unless the hacker do a lot of practice of that certain movement and do exactly the same as an authenticated user. This is very difficult because we modified the normal mouse and the mouse will not move as the hacker expect. What’s more, only the authenticated user knows how was the mouse be modified and how to act to adjust to that modification. In this way our proposed approach is much safer than the above traditional security and authentication methods. However, this is a feasibility study and more experiment will be done to prove our proposal and we will discuss it in the future work chapter

User Behavior-Based Implicit Authentication

In this work, we proposed dynamic retraining (RU), wind vane module (WVM), BubbleMap (BMap), and reinforcement authentication (RA) to improve the efficacy of implicit authentication (IA). Motivated by the great potential of implicit and seamless user authentication, we have built an implicit authentication system with adaptive sampling that automatically selects dynamic sets of activities for user behavior extraction. Various activities, such as user location, application usage, user motion, and battery usage have been popular choices to generate behaviors, the soft biometrics, for implicit authentication. Unlike password-based or hard biometric-based authentication, implicit authentication does not require explicit user action or expensive hardware. However, user behaviors can change unpredictably, which renders it more challenging to develop systems that depend on them. In addition to dynamic behavior extraction, the proposed implicit authentication system differs from the existing systems in terms of energy efficiency for battery-powered mobile devices. Since implicit authentication systems rely on machine learning, the expensive training process needs to be outsourced to the remote server. However, mobile devices may not always have reliable network connections to send real-time data to the server for training. In addition, IA systems are still at their infancy and exhibit many limitations, one of which is how to determine the best retraining frequency when updating the user behavior model. Another limitation is how to gracefully degrade user privilege when authentication fails to identify legitimate users (i.e., false negatives) for a practical IA system.To address the retraining problem, we proposed an algorithm that utilizes Jensen-Shannon (JS)-dis(tance) to determine the optimal retraining frequency, which is discussed in Chapter 2. We overcame the limitation of traditional IA by proposing a W-layer, an overlay that provides a practical and energy-efficient solution for implicit authentication on mobile devices. The W-layer is discussed in Chapter 3 and 4. In Chapter 5, a novel privilege-control mechanism, BubbleMap (BMap), is introduced to provide fine-grained privileges to users based on their behavioral scores. In the same chapter, we describe reinforcement authentication (RA) to achieve a more reliable authentication

Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach

User authentication is considered to be an important aspect of any cybersecurity program. However, one-time validation of user’s identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented. A novel method of authenticating the user on each action has been presented which decides the legitimacy of current user based on the confidence in the genuineness of each action. The 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model(R-RCM), has been designed which employs a novel perception of two thresholds i.e., alert and final threshold. Proposed methodology classifies each action based on the probability score of ensemble classifier which is afterwards used along with hyperparameters of R-RCM to compute the current confidence in the genuineness of user. System decides if user can continue using the system or not based on new confidence value and final threshold. However, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II

Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach

User authentication is considered to be an important aspect of any cybersecurity program. However, one-time validation of user’s identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented. A novel method of authenticating the user on each action has been presented which decides the legitimacy of current user based on the confidence in the genuineness of each action. The 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model(R-RCM), has been designed which employs a novel perception of two thresholds i.e., alert and final threshold. Proposed methodology classifies each action based on the probability score of ensemble classifier which is afterwards used along with hyperparameters of R-RCM to compute the current confidence in the genuineness of user. System decides if user can continue using the system or not based on new confidence value and final threshold. However, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II

Intelligent School Computer Laboratory Reservation System by Using Multiple Agents Based

One of the problems about school computer laboratory is to handle the reservation for class subject (non-computer based relation) or any events in Malaysia. The reservation process was still using manual approach. To make reservation, a user must fill the form; meet the administrator to check lab availability and lastly the admin will approve it. A computer system must be developed to manage the reservation for user request. This system was used multiple intelligent agents that assist the user and the admin to manage the lab reservation based on a few constraints such as holiday and permanent classes. The agents also try to give alternative session if the request session is not available. The agents have ability to evaluate user authentication based on inconsistent action on the system and will keep up the security level based on individual authentication. The collection of user action pattern is recorded then gives direction to the agent to reconstruct its intend action related to user behavior. FIPA-ACL as communication standard between the agents to make them able to be socializes in their environment. This system was developed by using JAVA computer programming language. JAVA programming is the best choice to create multi-agent functions and reacted base on intelligent agents approach. The system will be deployed as a stand-done system and also might share through internal local area network (LAN). MS Access was used as the database for storing the data and might it easy to integrate with Education Management Information System (EMIS) that already exist. The level of user' acceptance was determined using Technology Acceptance Model (TAM) by Davis (1989) where data is gathered through a set of designed questionnaire. In conclusion, this system has a great potential to encounter school computer lab reservation problem by using intelligent multi-agent as human behalf

On-Demand Security Token Linking

Systems and methods described herein allow for automatic execution by a virtual personal assistant of secure access tasks, such as user account authentication or acquiring/granting access authorization, associated with an online service or online action requested by a user of a client computing device. The client computing device can receive an input speech signal indicative of a request or command from a user of a client computing device. A data processing system communicatively coupled to the client computing device can identify, based on the input speech signal, a service or online action requested by the user of the client computing device. The data processing system can identify one or more secure access tasks associated with the requested online service or requested online action. The data processing system can perform the one or more identified secure access tasks associated with the online service or online action. The data processing system can then perform the requested online service or online action

SemanticLock: An authentication method for mobile devices using semantically-linked images

We introduce SemanticLock, a single factor graphical authentication solution for mobile devices. SemanticLock uses a set of graphical images as password tokens that construct a semantically memorable story representing the user`s password. A familiar and quick action of dragging or dropping the images into their respective positions either in a \textit{continous flow} or in \textit{discrete} movements on the the touchscreen is what is required to use our solution. The authentication strength of the SemanticLock is based on the large number of possible semantic constructs derived from the positioning of the image tokens and the type of images selected. Semantic Lock has a high resistance to smudge attacks and it equally exhibits a higher level of memorability due to its graphical paradigm. In a three weeks user study with 21 participants comparing SemanticLock against other authentication systems, we discovered that SemanticLock outperformed the PIN and matched the PATTERN both on speed, memorability, user acceptance and usability. Furthermore, qualitative test also show that SemanticLock was rated more superior in like-ability. SemanticLock was also evaluated while participants walked unencumbered and walked encumbered carrying "everyday" items to analyze the effects of such activities on its usage