Sungzoon Cho

Web-Based Keystroke Dynamics Identity Verification Using Neural Network

Password typing is the most widely used identity verification method in Web based electronic commerce. Due to its simplicity, however, it is vulnerable to imposter attacks. Keystroke dynamics and password checking can be combined to result in a more secure verification system. We propose an autoassociator neural network that is trained with the timing vectors of the owners keystroke dynamics and then used to discriminate between the owner and an imposter. An imposter typing the correct password can be detected with very high accuracy using the proposed approach. This approach can be effectively implemented by a Java applet and used for the Web.

Keystroke dynamics identity verification-its problems and practical solutions

Password is the most widely used identity verification method in computer security domain. However, because of its simplicity, it is vulnerable to imposter attacks. Use of keystroke dynamics can result in a more secure verification system. Recently, Cho et al. (J Organ Comput Electron Commerce 10 (2000) 295) proposed autoassociative neural network approach, which used only the users typing patterns, yet reporting a low error rate: 1.0% false rejection rate (FRR) and 0% false acceptance rate (FAR). However, the previous research had some limitations: (1) it took too long to train the model; (2) data were preprocessed subjectively by a human; and (3) a large data set was required. In this article, we propose the corresponding solutions for these limitations with an SVM novelty detector, GA-SVM wrapper feature subset selection, and an ensemble creation based on feature selection, respectively. Experimental results show that the proposed methods are promising, and that the keystroke dynamics is a viable and practical way to add more security to identity verification.

EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems

Data imbalance occurs when the number of patterns from a class is much larger than that from the other class. It often degenerates the classification performance. In this paper, we propose an Ensemble of Under-Sampled SVMs or EUS SVMs. We applied the proposed method to two synthetic and six real data sets and we found that it outperformed other methods, especially when the number of patterns belonging to the minority class is very small.

Keystroke dynamics-based authentication for mobile devices

Recently, mobile devices are used in financial applications such as banking and stock trading. However, unlike desktops and notebook computers, a 4-digit personal identification number (PIN) is often adopted as the only security mechanism for mobile devices. Because of their limited length, PINs are vulnerable to shoulder surfing and systematic trial-and-error attacks. This paper reports the effectiveness of user authentication using keystroke dynamics-based authentication (KDA) on mobile devices. We found that a KDA system can be effective for mobile devices in terms of authentication accuracy. Use of artificial rhythms leads to even better authentication performance.

Improvement of Kittler and Illingworth's minimum error thresholding

Abstract A simple modification to Kittler and Illingworths minimum error thresholding method was made and the performance of the modified version was compared with that of the original version empirically. By correcting the biased estimates of variances of model distributions, a significant improvement in performance was found. The improvement was most outstanding among not-well-separated, but still bimodal histograms. In fact, the modification provides a more robust method. The new version is nearly computationally equivalent in complexity to the original version.

Response modeling with support vector machines

Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing, however, one has to deal with the practical difficulties: large training data, class imbalance and scoring from binary SVM output. For the first difficulty, we propose a way to alleviate or solve it through a novel informative sampling. For the latter two difficulties, we provide guidelines within SVM framework so that one can readily use the paper as a quick reference for SVM response modeling: use of different costs for different classes and use of distance to decision boundary, respectively. This paper also provides various evaluation measures for response models in terms of accuracies, lift chart analysis, and computational efficiency.

GA-SVM wrapper approach for feature subset selection in keystroke dynamics identity verification

Password is the most widely used identity verification method in computer security domain. However, due to its simplicity, it is vulnerable to imposter attacks. Keystroke dynamics adds a shield to password. Password typing patterns or timing vectors of a user are measured and used to train a novelty detector model. However, without manual pre-processing to remove noises and outliers resulting from typing inconsistencies, a poor detection accuracy results. Thus, in this paper, we propose an automatic feature subset selection process that can automatically selects a relevant subset of features and ignores the rest, thus producing a better accuracy. Genetic algorithm is employed to implement a randomized search and SVM, an excellent novelty detector with fast learning speed, is employed as a base learner. Preliminary experiments show a promising result.

Neighborhood Property--Based Pattern Selection for Support Vector Machines

The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training.

Continual retraining of keystroke dynamics based authenticator

Keystroke dynamics based authentication (KDA) verifies a user based on the typing pattern. During enroll, a few typing patterns are provided, which are then used to train a classifier. The typing style of a user is not expected to change. However, sometimes it does change, resulting in a high false reject. In order to achieve a better authentication performance, we propose to continually retrain classifiers with recent login typing patterns by updating the training data set. There are two ways to update it. The moving window uses a fixed number of most recent patterns while the growing window uses all the new patterns as well as the original enroll patterns. We applied the proposed method to the real data set involving 21 users. The experimental results show that both the moving window and the growing window approach outperform the fixed window approach, which does not retrain a classifier.

Reliable roll force prediction in cold mill using multiple neural networks

The cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. The accurate prediction of roll force is essential for product quality. Currently, a suboptimal mathematical model is used. We trained two multilayer perceptrons, one to directly predict the roll force and the other to compute a corrective coefficient to be multiplied to the prediction made by the mathematical model. Both networks were shown to improve the accuracy by 30-50%. Combining the two networks and the mathematical model results in systems with an improved reliability.