Machine Learning Algorithm on Keystroke dynamics Fused pattern in biometrics

Baynath, Purvashi; Soyjaudah, K. M. S.; Khan, Maleika Heenaye-Mamode

doi:10.1109/nextcomp.2019.8883621

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…In the emotion recognition domain, 28 articles are encountered; from them, 21 were in recognition [272]- [292], 2 were in analysis [293], [294], and prediction [295], [296], and 1 was in detection [297], assessment [298], and estimating [299]. In total, 17 papers were found in the security domain, where 6 were in detection [300]- [305], 4 were in identification [306]- [309], 2 were in authentication [310], [311] and recognition [312], [313], and 1 was in determination [314], verification [315] and filtering [316]. Of the 13 papers in the biometric domain, 6 were in recognition [317]- [322], 2 were in detection [323], [324], 3 were in authentication [325]- [327] and 1 was in transforming [328] and classification [329].…”

Section: Inclusion Criteriamentioning

confidence: 99%

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

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Section: Inclusion Criteriamentioning

confidence: 99%

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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“…Similarly, a smaller proportion of research (25%) investigated combination characteristics. The following are the research areas where researchers are interested -(a) Improving accuracy through techniques such as feature fusion [99], [100], score fusion [101], [102], feature selection [103], [104], anomaly detection [105], [106], and others. (b) Domain adaptation for cross-device validation [107], [108], (c) Real-world dataset collected using IoTenabled device with typing patterns [109], some times data are being collected in different positions [110] through a variety of applications like arithmetic games [111], e-wallet [112], video clips for emotional changing [113], (d) Usability control specifically in active authentication where data are being captured continuously [114], to balance the device and application levels security, (e) Computation and energy consumption specifically in the area of a smartphone where battery power is limited [110], (f) Design some useful intelligent applications including auto-profiling user [40], disease prediction [32], age-restricted security control, genderspecific advertisement, password recovery mechanism [115].…”

Section: G Increasing Research Trend (Contribution To Ob2)mentioning

confidence: 99%

“…A few studies [155], [179], [211] included 250 subjects where a study [212] considered 300 subjects. A study [213] included 283 subjects, while some of the few studies [102], [214]- [217] considered more than 300 subjects. A recent study [218] used GPower for estimating sample size.…”

Section: F Subject Size and Subject Selectionmentioning

confidence: 99%

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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The purpose of this study is to conduct a comprehensive evaluation and analysis of the most recent studies on the implications of keystroke dynamics (KD) patterns in user authentication, identification, and the determination of useful information. Another aim is to provide an extensive and up-to-date survey of the recent literature and potential research directions to understand the present state-of-the-art methodologies in this particular domain that are expected to be beneficial for the KD research community. From January 1st, 2017 to March 13th, 2022, the popular six electronic databases have been searched using a search criterion ("keystroke dynamics" OR "typing pattern") AND ("authentication" OR "verification" OR "identification"). With this criterion, a total of nine thousand three hundred forty-eight results, including duplicates, were produced. However, one thousand five hundred forty-seven articles have been chosen after removing duplicates and preliminary screening. Due to insufficient information, only one hundred twentyseven high-quality quantitative research articles have been included in the article selection process. We compared and summarised several factors with multiple tables to comprehend the various methodologies, experimental settings, and findings. In this study, we have identified six unique KD-based designs and presented the status of findings toward an effective solution in authentication, identification, and prediction. We have also discovered considerable heterogeneity across studies in each KD-based design for desktops and smartphones separately. Finally, this paper found a few open research challenges and provided some indications for a deeper understanding of the issues and further study.

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“…The Carnegie-Mellon University (CMU) fixed-text dataset is used for all experiments considered in this paper. The CMU dataset commonly serves to benchmark techniques in keystroke dynamics research [3,6,11,13,21,23,31]. This dataset includes 51 users' keystroke dynamics information, where each user typed the password ".tie5Roanl" a total of 400 times, consisting of 50 repetitions over each of 8 sessions.…”

Section: Datasetmentioning

confidence: 99%

Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

Chang¹,

Li²,

Wu³

et al. 2021

Preprint

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Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous work has demonstrated the feasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP) perform well in our experiments. Our best models outperform previous comparable research.

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Machine Learning Algorithm on Keystroke dynamics Fused pattern in biometrics

Cited by 5 publications

References 16 publications

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

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