Recognition of Human Computer Operations Based on Keystroke Sensing by Smartphone Microphone

Yu, Zhiwen; Du, Hejun; Xiao, Dong; Wang, Zhu; Han, Qi; Guo, Bin

doi:10.1109/jiot.2018.2797896

Cited by 17 publications

(5 citation statements)

References 34 publications

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“…The security and privacy of data captured by sensors [ 103 , 104 ], the durability of power supplies (batteries) [ 91 , 105 ], the resilience of communications [ 105 , 106 ], and the intrusiveness of sensors [ 103 , 107 ], among other NFRs, must be considered at each stage of an IoTS development. Therefore, for example, there are research lines on different methods, protocols, and guidelines to guarantee data security and privacy [ 30 , 108 , 109 , 110 ], low energy consumption [ 110 , 111 , 112 , 113 ], or to integrate sensors with different levels of intrusiveness [ 114 , 115 ], among others.…”

Section: Methodologies Designed For the Development Of Iotssmentioning

confidence: 99%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, especially in the Scrum methodology, to determine its use and role in IoTS development. Initially, 4303 documents were retrieved, a number that was reduced to 186 after applying automatic filters and by the relevance of their titles. After analysing their contents, only 60 documents were considered. Of these, 38 documents present the development of an IoTS using some methodology, 8 present methodologies focused on the construction of IoTS software, and 14 present methodologies close to the systems life cycle (SLC). Finally, only one methodology can be considered SLC-compliant. Out of 38 papers presenting the development of some IoTS following a methodology for traditional information systems (ISs), 42.1% have used Scrum as the only methodology, while 10.5% have used Scrum combined with other methodologies, such as eXtreme Programming (XP), Kanban and Rapid Prototyping. In the analysis presented herein, the existing methodologies for developing IoTSs have been grouped according to the different approaches on which they are based, such as agile, modelling, and service oriented. This study also analyses whether the different proposals consider the standard stages of the development process or not: planning and requirements gathering, solution analysis, solution design, solution coding and unit testing (construction), integration and testing (implementation), and operation and maintenance. In addition, we include a review of the automated frameworks, platforms, and tools used in the methodologies analysed to improve the development of IoTSs and the design of their underlying architectures. To conclude, the main contribution of this work is a review for IoTS researchers and developers regarding existing methodologies, frameworks, platforms, tools, and guidelines for the development of IoTSs, with a deep analysis framed within international standards dictated for this purpose.

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Section: Methodologies Designed For the Development Of Iotssmentioning

confidence: 99%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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“…This type of data gathering and use entails surveillance risks. Information flowing into AI systems may be used to improve an organisation's aggregate performance and individual workers' performance by monitoring specific behaviours, ranging from toilet breaks to typing speeds (Gartner, 2019;Scassa, 2021;Yu et al, 2018). With the use of wearables, intended or incidental surveillance may not end at the factory gate or the office door.…”

Section: Development (Outcome Training)mentioning

confidence: 99%

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Cebulla¹,

Szpak²,

Knight³

2023

IJWHM

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PurposeArtificial Intelligence (AI) systems play an increasing role in organisation management, process and product development. This study identifies risks and hazards that AI systems may pose to the work health and safety (WHS) of those engaging with or exposed to them. A conceptual framework of organisational measures for minimising those risks is proposed.Design/methodology/approachAdopting an exploratory, inductive qualitative approach, the researchers interviewed 30 experts in data science, technology and WHS; 12 representatives of nine organisations using or preparing to use AI; and ran online workshops, including with 12 WHS inspectors. The research mapped AI ethics principles endorsed by the Australian government onto the AI Canvas, a tool for tracking AI implementation from ideation via development to operation. Fieldwork and analysis developed a matrix of WHS and organisational–managerial risks and risk minimisation strategies relating to AI use at each implementation stage.FindingsThe study identified psychosocial, work stress and workplace relational risks that organisations and employees face during AI implementation in a workplace. Privacy, business continuity and gaming risks were also noted. All may persist and reoccur during the lifetime of an AI system. Alertness to such risks may be enhanced by adopting a systematic risk assessment approach.Originality/valueA collaborative project involving sociologists, economists and computer scientists, the study relates abstract AI ethics principles to concrete WHS risks and hazards. The study translates principles typically applied at the societal level to workplaces and proposes a process for assessing AI system risks.

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“…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]. For beginners, these provide a clear understanding of how to identify the main area of KD-based research.…”

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

confidence: 99%

“…Tree based classifiers: After SVM, the most widely used classifiers are Random Forest (RF) [168], [177], [178], [184], [186], [288], [291]. However, Decision Tree (DT) [182], [204], [292], J48 [114], [293], XGBoost [25], [27], [195], [294], [295], AdaBoost [111] have been also identified in KD domain. Most of these tree-based classifiers are time-inefficient, but their performance in accuracy is impressive.…”

Section: ) Binary Classificationsmentioning

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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Recognition of Human Computer Operations Based on Keystroke Sensing by Smartphone Microphone

Cited by 17 publications

References 34 publications

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

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