Feature Selection for Android Keystroke Dynamics

Zein, Dima El; Kalakech, Ali

doi:10.1109/acit.2018.8672706

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Keystroke dynamics data can be collected by typing standard or non-standard passwords. Features extracted from the raw data that represent the typing patterns are used to create a unique profile for each user and to authorize those users later to resources [8][9][10]. It can also be used to recognize the emotions of a person [11].…”

Section: Behavioral Biometrics Models In Literaturementioning

confidence: 99%

Behavioral Biometrics: Past, Present and Future

Sharma¹,

Elmiligi²

2022

Recent Advances in Biometrics

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Behavioral biometrics are changing the way users are authenticated to access resources by adding an extra layer of security seamlessly. Behavioral biometric authentication identifies users based on a set of unique behaviors that can be observed when users perform daily activities or interact with smart devices. There are different types of behavioral biometrics that can be used to create unique profiles of users. For example, skill-based behavioral biometrics are common biometrics that is based on the instinctive, unique and stable muscle actions taken by the user. Other types include style-based behavioral biometrics, knowledge-based behavioral biometrics, strategy-based behavioral biometrics, etc. Behavioral biometrics can also be classified based on their use model. Behavioral biometrics can be used for one-time authentication or continuous authentication. One-time authentication occurs only once when a user requests access to a resource. Continuous authentication is a method of confirming the user’s identity in real-time while they are using the service. This chapter discusses the different types of behavioral biometrics and explores the various classifications of behavioral biometrics-based on their use models. The chapter highlights the most trending research directions in behavioral biometrics authentication and presents examples of current commercial solutions that are based on behavioral biometrics.

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Section: Behavioral Biometrics Models In Literaturementioning

confidence: 99%

Behavioral Biometrics: Past, Present and Future

Sharma¹,

Elmiligi²

2022

Recent Advances in Biometrics

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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 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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“…It calculates the GR in connection with the class. Whereas the IG selects the feature with a huge number of value, this method's objective is to maximize the feature IG while decreasing the value numbers [11].…”

Section: Grmentioning

confidence: 99%

“…The attribute values are evaluated by the IG method with the calculation of IG concerning the class which calculated the difference in information between cases where the feature's value is known and cases unidentified. Each feature will get an assigned score, indicating how much more information about the class is fetched when that feature is used [11].…”

Section: Igmentioning

confidence: 99%

Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Abdulhadi¹,

Talabani²

2021

UHD J SCI TECH

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Thoracic surgery refers to the information gathered for the patients who have to suffer from lung cancer. Various machine learning techniques were employed in post-operative life expectancy to predict lung cancer patients. In this study, we have used the most famous and influential supervised machine learning algorithms, which are J48, Naïve Bayes, Multilayer Perceptron, and Random Forest (RF). Then, two ranker feature selections, information gain and gain ratio, were used on the thoracic surgery dataset to examine and explore the effect of used ranker feature selections on the machine learning classifiers. The dataset was collected from the Wroclaw University in UCI repository website. We have done two experiments to show the performances of the supervised classifiers on the dataset with and without employing the ranker feature selection. The obtained results with the ranker feature selections showed that J48, NB, and MLP’s accuracy improved, whereas RF accuracy decreased and support vector machine remained stable.

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Feature Selection for Android Keystroke Dynamics

Cited by 5 publications

References 4 publications

Behavioral Biometrics: Past, Present and Future

Behavioral Biometrics: Past, Present and Future

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

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