Mobile apps utilize the features of a mobile device to offer an ever-growing range of functionalities. This vast choice of functionalities is usually available for a small fee or for free. These apps access the user’s personal data, utilizing both the sensors on the device and big data from several sources. Nowadays, Artificial Intelligence (AI) is enhancing the ability to utilize more data and gain deeper insight. This increase in the access and utilization of personal information offers benefits but also challenges to trust. Using questionnaire data from Germany, this research explores the role of trust from the consumer’s perspective when purchasing mobile apps with enhanced AI. Models of trust from e-commerce are adapted to this specific context. A model is proposed and explored with quantitative methods. Structural Equation Modeling enables the relatively complex model to be tested and supported. Propensity to trust, institution-based trust, perceived sensitivity of personal information, and trust in the mobile app are found to impact the intention to use the mobile app with enhanced AI.
{"title":"Re-Evaluating Trust and Privacy Concerns When Purchasing a Mobile App: Re-calibrating for the Increasing Role of Artificial Intelligence","authors":"Alex Zarifis, Shixuan Fu","doi":"10.3390/digital3040018","DOIUrl":"https://doi.org/10.3390/digital3040018","url":null,"abstract":"Mobile apps utilize the features of a mobile device to offer an ever-growing range of functionalities. This vast choice of functionalities is usually available for a small fee or for free. These apps access the user’s personal data, utilizing both the sensors on the device and big data from several sources. Nowadays, Artificial Intelligence (AI) is enhancing the ability to utilize more data and gain deeper insight. This increase in the access and utilization of personal information offers benefits but also challenges to trust. Using questionnaire data from Germany, this research explores the role of trust from the consumer’s perspective when purchasing mobile apps with enhanced AI. Models of trust from e-commerce are adapted to this specific context. A model is proposed and explored with quantitative methods. Structural Equation Modeling enables the relatively complex model to be tested and supported. Propensity to trust, institution-based trust, perceived sensitivity of personal information, and trust in the mobile app are found to impact the intention to use the mobile app with enhanced AI.","PeriodicalId":50578,"journal":{"name":"Digital Investigation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Alqahtani, Sumayya Azzony, Leen Alsharafi, Maha Alaseri
In this article, we introduce a web-based malware detection system that leverages a deep-learning approach. Our primary objective is the development of a robust deep-learning model designed for classifying malware in executable files. In contrast to conventional malware detection systems, our approach relies on static detection techniques to unveil the true nature of files as either malicious or benign. Our method makes use of a one-dimensional convolutional neural network 1D-CNN due to the nature of the portable executable file. Significantly, static analysis aligns perfectly with our objectives, allowing us to uncover static features within the portable executable header. This choice holds particular significance given the potential risks associated with dynamic detection, often necessitating the setup of controlled environments, such as virtual machines, to mitigate dangers. Moreover, we seamlessly integrate this effective deep-learning method into a web-based system, rendering it accessible and user-friendly via a web interface. Empirical evidence showcases the efficiency of our proposed methods, as demonstrated in extensive comparisons with state-of-the-art models across three diverse datasets. Our results undeniably affirm the superiority of our approach, delivering a practical, dependable, and rapid mechanism for identifying malware within executable files.
{"title":"Web-Based Malware Detection System Using Convolutional Neural Network","authors":"Ali Alqahtani, Sumayya Azzony, Leen Alsharafi, Maha Alaseri","doi":"10.3390/digital3030017","DOIUrl":"https://doi.org/10.3390/digital3030017","url":null,"abstract":"In this article, we introduce a web-based malware detection system that leverages a deep-learning approach. Our primary objective is the development of a robust deep-learning model designed for classifying malware in executable files. In contrast to conventional malware detection systems, our approach relies on static detection techniques to unveil the true nature of files as either malicious or benign. Our method makes use of a one-dimensional convolutional neural network 1D-CNN due to the nature of the portable executable file. Significantly, static analysis aligns perfectly with our objectives, allowing us to uncover static features within the portable executable header. This choice holds particular significance given the potential risks associated with dynamic detection, often necessitating the setup of controlled environments, such as virtual machines, to mitigate dangers. Moreover, we seamlessly integrate this effective deep-learning method into a web-based system, rendering it accessible and user-friendly via a web interface. Empirical evidence showcases the efficiency of our proposed methods, as demonstrated in extensive comparisons with state-of-the-art models across three diverse datasets. Our results undeniably affirm the superiority of our approach, delivering a practical, dependable, and rapid mechanism for identifying malware within executable files.","PeriodicalId":50578,"journal":{"name":"Digital Investigation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135886399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This small-scale study comprised an evaluation of a teacher professional learning experience that involved the collaborative creation of resources using immersive virtual reality (VR) as a retrieval practice tool, specifically focusing on the open access aspects of the SchooVR platform. SchooVR offers teachers and students tools to enhance teaching and learning by providing a range of virtual field trips and the ability to create customised virtual tours aligned with curriculum requirements. By leveraging the immersive 360° learning environment, learners can interact with content in meaningful ways, fostering engagement and deepening understanding. This study draws on the experiences of a group of postgraduate teacher education students and co-operating teachers in Ireland and Northern Ireland who collaborated on the creation of a number of immersive learning experiences across a range of subjects during a professional learning event. The research showcases how immersive realities, such as VR, can be integrated effectively into blended learning spaces to create resources that facilitate retrieval practice and self-paced study, thereby supporting the learning process. By embedding VR experiences into the curriculum, students are given opportunities for independent practice, review, and personalised learning tasks, all of which contribute to the consolidation of knowledge and the development of metacognitive skills. The findings suggest that SchooVR and similar immersive technologies have the potential to enhance educational experiences and promote effective learning outcomes across a variety of subject areas.
{"title":"Using Virtual Reality to Support Retrieval Practice in Blended Learning: An Interdisciplinary Professional Development Collaboration between Novice and Expert Teachers","authors":"Pamela Cowan, Rachel Farrell","doi":"10.3390/digital3030016","DOIUrl":"https://doi.org/10.3390/digital3030016","url":null,"abstract":"This small-scale study comprised an evaluation of a teacher professional learning experience that involved the collaborative creation of resources using immersive virtual reality (VR) as a retrieval practice tool, specifically focusing on the open access aspects of the SchooVR platform. SchooVR offers teachers and students tools to enhance teaching and learning by providing a range of virtual field trips and the ability to create customised virtual tours aligned with curriculum requirements. By leveraging the immersive 360° learning environment, learners can interact with content in meaningful ways, fostering engagement and deepening understanding. This study draws on the experiences of a group of postgraduate teacher education students and co-operating teachers in Ireland and Northern Ireland who collaborated on the creation of a number of immersive learning experiences across a range of subjects during a professional learning event. The research showcases how immersive realities, such as VR, can be integrated effectively into blended learning spaces to create resources that facilitate retrieval practice and self-paced study, thereby supporting the learning process. By embedding VR experiences into the curriculum, students are given opportunities for independent practice, review, and personalised learning tasks, all of which contribute to the consolidation of knowledge and the development of metacognitive skills. The findings suggest that SchooVR and similar immersive technologies have the potential to enhance educational experiences and promote effective learning outcomes across a variety of subject areas.","PeriodicalId":50578,"journal":{"name":"Digital Investigation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135827033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1007/978-3-662-58631-0_3
Jürgen Beetz
{"title":"Die Rückwirkung auf die Realität","authors":"Jürgen Beetz","doi":"10.1007/978-3-662-58631-0_3","DOIUrl":"https://doi.org/10.1007/978-3-662-58631-0_3","url":null,"abstract":"","PeriodicalId":50578,"journal":{"name":"Digital Investigation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74225163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1016/J.DIIN.2017.08.002
E. Casey, Sean Barnum, Ryan Griffith, Jonathan Snyder, H. V. Beek, Alex Nelson
{"title":"Advancing coordinated cyber-investigations and tool interoperability using a community developed specification language","authors":"E. Casey, Sean Barnum, Ryan Griffith, Jonathan Snyder, H. V. Beek, Alex Nelson","doi":"10.1016/J.DIIN.2017.08.002","DOIUrl":"https://doi.org/10.1016/J.DIIN.2017.08.002","url":null,"abstract":"","PeriodicalId":50578,"journal":{"name":"Digital Investigation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78174530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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