Active Authentication Systems Research Articles

Fragile watermarking for image authentication using dyadic walsh ordering

A digital image is subjected to the most manipulation. This is driven by the easy manipulating process through image editing software which is growing rapidly. These problems can be solved through the watermarking model as an active authentication system for the image. One of the most popular methods is Singular Value Decomposition (SVD) which has good imperceptibility and detection capabilities. Nevertheless, SVD has high complexity and can only utilize one singular matrix S, and ignore two orthogonal matrices. This paper proposes the use of the Walsh matrix with dyadic ordering to generate a new S matrix without the orthogonal matrices. The experimental results showed that the proposed method was able to reduce computational time by 22% and 13% compared to the SVD-based method and similar methods based on the Hadamard matrix respectively. This research can be used as a reference to speed up the computing time of the watermarking methods without compromising the level of imperceptibility and authentication.

Continuous Authentications Using Frequent English Terms

ABSTRACTMost of the current computer systems authenticate a user’s identity only at the point of entry to the system (i.e., login). However, an effective authentication system includes continuous or frequent monitoring of the identity of a user already logged into a system to ensure the validity of the identity of the user throughout a session. Such a system is called a “continuous or active authentication system.” An authentication system equipped with such a security mechanism protects the system against certain attacks including session hijacking that can be performed later by a malicious user. The aim of this research is to advance the state-of-the-art of the user-active authentication research using keystroke dynamics. Through this research, we assess the performance and influence of various keystroke features on keystroke dynamics authentication systems. In particular, we investigate the performance of keystroke features on a subset of most frequently used English words. The performance of four features including key duration, flight time latency, diagraph time latency, and word total time duration are analyzed. A series of experiments is performed to measure the performance of each feature individually as well as the results from the combinations of these features. More specifically, four machine learning techniques are adapted for the purpose of assessing keystroke authentication schemes. The selected classification methods are Support Vector Machine (SVM), Linear Discriminate Classifier (LDC), K-Nearest Neighbors (K-NN), and Naive Bayesian (NB). Moreover, this research proposes a novel approach based on sequential change-point methods for early detection of an imposter in computer authentication without the needs for any modeling of users in advance, that is, no need for a-priori information regarding changes. The proposed approach based on sequential change-point methods provides the ability to detect the impostor in early stages of attacks. The study is performed and evaluated based on data collected for 28 users. The experimental results indicate that the word total time feature offers the best performance result among all four keystroke features, followed by diagraph time latency. Furthermore, the results of the experiments also show that the combination of features enhances the performance accuracy. In addition, the nearest neighbor method performs the best among the four machine learning techniques.

A survey on behavioral biometric authentication on smartphones

Recent research has shown the possibility of using smartphones’ sensors and accessories to extract some behavioral attributes such as touch dynamics, keystroke dynamics and gait recognition. These attributes are known as behavioral biometrics and could be used to verify or identify users implicitly and continuously on smartphones. The authentication systems that have been built based on these behavioral biometric traits are known as active or continuous authentication systems.This paper provides a review of the active authentication systems. We present the components and the operating process of the active authentication systems in general, followed by an overview of the state-of-the-art behavioral biometric traits that used to develop an active authentication systems and their evaluation on smartphones. We discuss the issues, strengths and limitations that associated with each behavioral biometric trait. Also, we introduce a comparative summary between them. Finally, challenges and open research problems are presented in this research field.

Reconstruction Attacks Against Mobile-Based Continuous Authentication Systems in the Cloud

Continuous authentication for mobile devices using behavioral biometrics is being suggested to complement initial authentication for securing mobile devices, and the cloud services accessed through them. This area has been studied over the past few years, and low error rates were achieved; however, it was based on training and testing using support vector machine (SVM) and other non-privacy-preserving machine learning algorithms. To stress the importance of carefully designed privacy-preserving systems, we investigate the possibility of reconstructing gestures raw data from users’ authentication profiles or synthesized samples’ testing results. We propose two types of reconstruction attacks based on whether actual user samples are available to the adversary (as in SVM profiles) or not. We also propose two algorithms to reconstruct raw data: a numerical-based algorithm that is specific to one compromised system, and a randomization-based algorithm that can work against almost any compromised system. For our experiments, we selected one compromised and four attacked gesture-based continuous authentication systems from the recent literature. The experiments, performed using a public data set, showed that the attacks were feasible, with a median ranging from 80% to 100% against one attacked system using all types of attacks and algorithms, and a median ranging from 73% to 100% against all attacked systems using the randomization-based algorithm and the negative support vector attack. Finally, we analyze the results, and provide recommendations for building active authentication systems that could resist reconstruction attacks.

Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System.

In this paper, a fast kernel ridge regression (KRR) learning algorithm is adopted with ( ) training cost for large-scale active authentication system. A truncated Gaussian radial basis function (TRBF) kernel is also implemented to provide better cost-performance tradeoff. The fast-KRR algorithm along with the TRBF kernel offers computational advantages over the traditional support vector machine (SVM) with Gaussian-RBF kernel while preserving the error rate performance. Experimental results validate the cost-effectiveness of the developed authentication system. In numbers, the fast-KRR learning model achieves an equal error rate (EER) of 1.39% with ( ) training time, while SVM with the RBF kernel shows an EER of 1.41% with ( ) training time.

Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges

Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in the use of mobile devices, users must constantly worry about security and privacy, as the loss of a mobile device could compromise personal information. To deal with this problem, continuous authentication systems (also known as active authentication systems) have been proposed, in which users are continuously monitored after initial access to the mobile device. In this article, we provide an overview of different continuous authentication methods on mobile devices. We discuss the merits and drawbacks of the available approaches and identify promising avenues of research in this rapidly evolving field.