John V. Monaco

An investigation of keystroke and stylometry traits for authenticating online test takers

The 2008 federal Higher Education Opportunity Act requires institutions of higher learning to make greater access control efforts for the purposes of assuring that students of record are those actually accessing the systems and taking exams in online courses by adopting identification technologies as they become more ubiquitous. To meet these needs, keystroke and stylometry biometrics were investigated towards developing a robust system to authenticate (verify) online test takers. Performance statistics on keystroke, stylometry, and combined keystroke-stylometry systems were obtained on data from 40 test-taking students enrolled in a university course. The best equal-error-rate performance on the keystroke system was 0.5% which is an improvement over earlier reported results on this system. The performance of the stylometry system, however, was rather poor and did not boost the performance of the keystroke system, indicating that stylometry is not suitable for text lengths of short-answer tests unless the features can be substantially improved, at least for the method employed.

Behavioral biometric verification of student identity in online course assessment and authentication of authors in literary works

Keystroke and stylometry behavioral biometrics were investigated with the objective of developing a robust system to authenticate students taking online examinations. This work responds to the 2008 U.S. Higher Education Opportunity Act that requires institutions of higher learning undertake greater access control efforts, by adopting identification technologies as they become available, to assure that students of record are those actually accessing the systems and taking the exams in online courses. Performance statistics on keystroke, stylometry, and combined keystroke-stylometry systems were obtained on data from 30 students taking examinations in a university course. The performance of the keystroke system was 99.96% and 100.00%, while that of the stylometry system was considerably weaker at 74% and 78%, on test input of 500 and 1000 words, respectively. To further investigate the stylometry system, a separate study on 30 book authors achieved performance of 88.2% and 91.5% on samples of 5000 and 10000 words, respectively, and the varied performance over the population of authors was analyzed.

Developing a Keystroke Biometric System for Continual Authentication of Computer Users

Data windows of keyboard input are analyzed to continually authenticate computer users and verify that they are the authorized ones. Because the focus is on fast intruder detection, the authentication process operates on short bursts of roughly a minute of keystroke input, while the training process can be extensive and use hours of input. The biometric system consists of components for data capture, feature extraction, authentication classification, and receiver-operating-characteristic curve generation. Using keystroke data from 120 users, system performance was obtained as a function of two independent variables: the user population size and the number of keystrokes per sample. For each population size, the performance increased (and the equal error rate decreased) roughly logarithmically as the number of keystrokes per sample was increased. The best closed-system performance results of 99 percent on 14 participants and 96 percent on 30 participants indicate the potential of this approach.

Recent Advances in the Development of a Long-Text-Input Keystroke Biometric Authentication System for Arbitrary Text Input

This study focuses on the development and evaluation of a new classification algorithm that halves the previously reported best error rate. Using keystroke data from 119 users, closed system performance was obtained as a function of the number of keystrokes per sample. The applications of interest are authenticating online student test takers and computer users in security sensitive environments. The authentication process operates on keystroke data windows as short as 1/2 minute. Performance was obtained on 119 test users compared to the previous maximum of 30. For each population size, the performance increases, and the equal error rate decreases, as the number of keystrokes per sample increases. Performance on 14, 30, and 119 users was 99.6%, 98.3%, and 96.3%, respectively, on 755-keystroke samples, indicating the potential of this approach. Because the mean population performance does not give the complete picture, the varied performance over the population of users was analyzed.

Keystroke Biometric Studies on Password and Numeric Keypad Input

The keystroke biometric classification system described in this study was evaluated on two types of short input - passwords and numeric keypad input. On the password input, the system outperforms 14 other systems evaluated in a previous study using the same raw input data. The three top performing systems in that study had equal error rates between 9.6% and 10.2%. With the classification system developed in this study, equal error rates of 8.7% were achieved on both the features from the previous study and on a new set of features. On the numeric keypad input, the system achieved an equal error rate of 10.5% on the features from the previous study and 6.1% on a new set of features.

Keystroke Biometric Systems for User Authentication

Keystroke biometrics (KB) authentication systems are a less popular form of access control, although they are gaining popularity. In recent years, keystroke biometric authentication has been an active area of research due to its low cost and ease of integration with existing security systems. Various researchers have used different methods and algorithms for data collection, feature representation, classification, and performance evaluation to measure the accuracy of the system, and therefore achieved different accuracy rates. Although recently, the support vector machine is most widely used by researchers, it seems that ensemble methods and artificial neural networks yield higher accuracy. Moreover, the overall accuracy of KB is still lower than other biometric authentication systems, such as iris. The objective of this paper is to present a detailed survey of the most recent researches on keystroke dynamic authentication, the methods and algorithms used, the accuracy rate, and the shortcomings of those researches. Finally, the paper identifies some issues that need to be addressed in designing keystroke dynamic biometric systems, makes suggestions to improve the accuracy rate of KB systems, and proposes some possible future research directions.

One-handed Keystroke Biometric Identification Competition

This work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.

Authentication and Identification Methods Used in Keystroke Biometric Systems

Keystroke dynamics authentication is not as widely used compared to other biometric systems. In recent years, keystroke dynamic authentication systems have gained interest because of low cost and integration with existing security systems. Many different methods have been proposed for data collection, feature representation, classification, and performance evaluation. The work presents a detailed survey of the most recent research in keystroke dynamic authentication. Research is evaluated by the conditions under which data was collected, classification algorithms used, and system performance. This work also identifies some shortcomings of the current research issues that need to be addressed for keystroke dynamics to mature. Some recommendations for future research are made, with the goal of improving keystroke dynamics system performance and robustness.

A comparison of classifiers and features for authorship authentication of social networking messages

This paper develops algorithms and investigates various classifiers to determine the authenticity of short social network postings, an average of 20.6 words, from Facebook. This paper presents and discusses several experiments using a variety of classifiers. The goal of this research is to determine the degree to which such postings can be authenticated as coming from the purported user and not from an intruder. Various sets of stylometry and ad hoc social networking features were developed to categorize 9259 posts from 30 Facebook authors as authentic or non‐authentic. An algorithm to utilize machine‐learning classifiers for investigating this problem is described, and an additional voting algorithm that combines three classifiers is investigated. This research is one of the first works that focused on authorship authentication in short messages, such as postings on social network sites. The challenges of applying traditional stylometry techniques on short messages are discussed. Experimental results demonstrate an average accuracy rate of 79.6% among 30 users. Further empirical analyses evaluate the effect of sample size, feature selection, user writing style, and classification method on authorship authentication, indicating varying degrees of success compared with previous studies. Copyright

Identifying Bitcoin users by transaction behavior

Digital currencies, such as Bitcoin, offer convenience and security to criminals operating in the black marketplace. Some Bitcoin marketplaces, such as Silk Road, even claim anonymity. This claim contradicts the findings in this work, where long term transactional behavior is used to identify and verify account holders. Transaction timestamps and network properties observed over time contribute to this finding. The timestamp of each transaction is the result of many factors: the desire purchase an item, daily schedule and activities, as well as hardware and network latency. Dynamic network properties of the transaction, such as coin flow and the number of edge outputs and inputs, contribute further to reveal account identity. In this paper, we propose a novel methodology for identifying and verifying Bitcoin users based on the observation of Bitcoin transactions over time. The behavior we attempt to quantify roughly occurs in the social band of Newells time scale. A subset of the Blockchain 230686 is taken, selecting users that initiated between 100 and 1000 unique transactions per month for at least 6 different months. This dataset shows evidence of being nonrandom and nonlinear, thus a dynamical systems approach is taken. Classification and authentication accuracies are obtained under various representations of the monthly Bitcoin samples: outgoing transactions, as well as both outgoing and incoming transactions are considered, along with the timing and dynamic network properties of transaction sequences. The most appropriate representations of monthly Bitcoin samples are proposed. Results show an inherent lack of anonymity by exploiting patterns in long-term transactional behavior.