Social media platforms present significant threats against underage users targeted for predatory intents. Many early research works have applied the footprints left by online predators to investigate online grooming. While digital forensics tools provide security to online users, it also encounters some critical challenges, such as privacy issues and the lack of data for research in this field. Our literature review investigates all research papers on grooming detection in online conversations by looking at the psychological definitions and aspects of grooming. We study the psychological theories behind the grooming characteristics used by machine learning models that have led to predatory stage detection. Our survey broadly considers the authorship profiling research works used for grooming detection in online conversations, along with predatory conversation detection and predatory identification approaches. Various approaches for online grooming detection have been evaluated based on the metrics used in the grooming detection problem. We have also categorized the available datasets and used feature vectors to give readers a deep knowledge of the problem considering their constraints and open research gaps. Finally, this survey details the constraints that challenge grooming detection, unaddressed problems, and possible future solutions to improve the state-of-the-art and make the algorithms more reliable.

Gait authentication using a mobile phone's acceleration sensor offers a convenient, user-friendly and subtle procedure of authenticating individuals to their mobile phone. This study analyses the possibility of cross-pocket gait recognition, which means creating the reference and the probe with the accelerometer sensor in different trouser pockets (left and right). The results of our analysis show that there is a significant performance degradation when comparing same-pocket gait recognition with cross-pocket gait recognition. In our analysis we have used a new distance metric that shows to give good (same-pocket) performance results compared to known analysis methods. We have also shown that a multi-reference template can give excellent performance without any performance degradation for cross-pocket gait recognition.

Mobile phones have become an essential part of our daily life. We are heavily relying on various applications (App) installed on our mobile phones to socialize and even do business. The majority of mobile applications require some forms of authentication measurement often combining usernames and passwords, however, memorizing various passwords for different applications is a nightmare for the users. In order to relieve people from memorizing these passwords, an unobtrusive mobile application authentication approach is designed in this paper by analyzing the data collected from four resources: WiFi, Bluetooth, accelerometer sensor and gyroscope sensor. We first develop the authentication model based on single resource respectively. Furthermore, a score-level fusion authentication approach is proposed by considering the scores generated from four models. The proposed approach was evaluated on a dataset collected from a real-life scenario with the authentication response time setting at 3 seconds. The lowest EER (Equal Error Rate) achieved from the experiments is 9.67%, which indicates the feasibility of deploying the proposed approach on mobile phone to enhance the security while still maintaining good user-friendliness.

The overall performance of iris recognition systems is affected by the quality of acquired iris sample images. Due to the development of imaging technologies, visible wavelength iris recognition gained a lot of attention in the past few years. However, iris sample quality of unconstrained imaging conditions is a more challenging issue compared to the traditional near infrared iris biometrics. Therefore, measuring the quality of such iris images is essential in order to have good quality samples for iris recognition. In this paper, we investigate whether general purpose no-reference image quality metrics can assess visible wavelength iris sample quality.

In this paper we investigate the capacity of sound & timing information during typing of a password for the user identification and authentication task. The novelty of this paper lies in the comparison of performance between improved timing-based and audio-based keystroke dynamics analysis and the fusion for the keystroke authentication. We collected data of 50 people typing the same given password 100 times, divided into 4 sessions of 25 typings and tested how well the system could recognize the correct typist. Using fusion of timing (9.73%) and audio calibration scores (8.99%) described in the paper we achieved 4.65% EER (Equal Error Rate) for the authentication task. The results show the potential of using Audio Keystroke Dynamics information as a way to authenticate or identify users during log-on.

Due to the increasing vulnerabilities in cyberspace, security alone is not enough to prevent a breach, but cyber forensics or cyber intelligence is also required to prevent future attacks or to identify the potential attacker. The unobtrusive and covert nature of biometric data collection of keystroke dynamics has a high potential for use in cyber forensics or cyber intelligence. In this paper, we investigate the usefulness of keystroke dynamics to establish the person identity. We propose three schemes for identifying a person when typing on a keyboard. We use various machine learning algorithms in combination with the proposed pairwise user coupling technique and show the performance of each separate technique as well as the performance when combining two or more together. In particular, we show that pairwise user coupling in a bottom-up tree structure scheme gives the best performance, both concerning accuracy and time complexity. The proposed techniques are validated by using keystroke data. However, these techniques could equally well be applied to other pattern identification problems. We have also investigated the optimized feature set for person identification by using keystroke dynamics. Finally, we also examined the performance of the identification system when a user, unlike his normal behaviour, types with only one hand, and we show that performance then is not optimal, as was to be expected.

In this paper, we investigate the performance of a continuous authentication system using fuzzy logic techniques. The system monitors mouse and keystroke dynamics behaviour of a user to determine the genuineness. The objective was to design a system that is capable of detecting an impostor user as fast as possible, while not disturbing the genuine user. For our research we build a new dataset consisting of mouse and keystroke dynamics behavioural data of 52 persons, collected in a real life environment (no control over environment or performed tasks) over a period of 5-7 days.

This paper discusses the complexity measurement of a password in relation to the performance of a keystroke dynamics system. The performance of any biometric system depends on the stability of the biometric data provided by the user. We first present a new way to calculate the complexity related to the typing of a password. This complexity metric is then validated with the keystroke dynamics data collected in an experiment, as well as the user's experience during the experiment. Next, we show that the performance of the keystroke dynamics biometric system will depend on the complexity of the password and in particular that the performance of the system decreases with an increasing complexity. This leads then to the conclusion that random passwords might, although harder to guess by an attacker, might not be the most suitable choice in case of keystroke dynamics.

A renewable fingerprint minutiae template generation scheme is proposed to utilize random projection for template diversification in a security enhanced way. The scheme first achieves absolute pre-alignment over local minutiae quadruplets in the original template and results in a fix-length feature vector; and then encrypts the feature vector by projecting it to multiple random matrices and quantizing the projected result; and finally post-process the resultant binary vector in a size and security tunable way to obtain the final protected minutia vicinity. Experiments on the fingerprint database FVC2002DB2_A demonstrate the desirable biometric performance of the proposed scheme.

Often in the development of a biometric product an evaluator of the system is the same entity who developed the algorithm. Moreover, usually the test data employed in such evaluation is also collected by the same developer/evaluator. In most cases such database will not be made public and consequently test results cannot be verified by independent institutions. This paper presents an independent report on fingerprint performance evaluation that has been conducted in the context of the TURBINE project. In this study the algorithm developer and system evaluator are represented by separate and independent entities. In addition, the algorithm developer does not have access to the primary test database. All these provide pre-conditions to unbiased and trustworthy performance reports. Furthermore, this paper introduces biometric performance testing on a level of biometric references, which is complementary to image- or minutiae-based references. Biometric references in the TURBINE project are pseudonymous identifiers that have been generated by the template protection algorithms. The results of the performance evaluation in this paper are generated by applying the algorithm developers (binary) algorithms at the minutiae (traditional) and pseudonymous identifier levels. The test data set consists of almost 72000 fingerprint images from 100 subjects acquired by several fingerprint scanners to which the algorithm developers did not have access.

Research in biometric gait recognition has increased. Earlier gait recognition works reported promising results, usually with a small sample size. Recent studies with a larger sample size confirm gait potential as a biometric from which individuals can be identified. Despite much research being carried out in gait recognition, the topic of vulnerability of gait to attacks has not received enough attention. In this paper, an analysis of minimal-effort impersonation attack and the closest person attack on gait biometrics are presented. Unlike most previous gait recognition approaches, where gait is captured using a (video) camera from a distance, in our approach, gait is collected by an accelerometer sensor attached to the hip of subjects. Hip acceleration in three orthogonal directions (up-down, forward-backward, and sideways) is utilized for recognition. We have collected 760 gait sequences from 100 subjects. The experiments consisted of two parts. In the first part, subjects walked in their normal walking style, and using the averaged cycle method, an EER of about 13% was obtained. In the second part, subjects were trying to walk as someone else. Analysis based on FAR errors indicates that a minimal-effort impersonation attack on gait biometric does not necessarily improve the chances of an impostor being accepted. However, attackers with knowledge of their closest person in the database can be a serious threat to the authentication system.