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Kobusinska, A., Jacobsson, A. & Chang, V. (2024). Foreword. In: IoTBDS 2024 Final Program and Book of Abstracts: The 9th International Conference on Internet of Things, Big Data and Security. Paper presented at The 9th International Conference on Internet of Things, Big Data and Security, Angers, France, April 28-30 2024 (pp. 5-6). Portugal: SciTePress
Åpne denne publikasjonen i ny fane eller vindu >>Foreword
2024 (engelsk)Inngår i: IoTBDS 2024 Final Program and Book of Abstracts: The 9th International Conference on Internet of Things, Big Data and Security, Portugal: SciTePress, 2024, , s. 43s. 5-6Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

N/A.

sted, utgiver, år, opplag, sider
Portugal: SciTePress, 2024. s. 43
Serie
IoTBDS 2024 Final Program and Book of Abstracts
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-67031 (URN)
Konferanse
The 9th International Conference on Internet of Things, Big Data and Security, Angers, France, April 28-30 2024
Tilgjengelig fra: 2024-05-01 Laget: 2024-05-01 Sist oppdatert: 2024-05-02bibliografisk kontrollert
Bagheri, S., Jacobsson, A. & Davidsson, P. (2024). Smart Homes as Digital Ecosystems: Exploring Privacy in IoT Contexts. In: Gabriele Lenzini; Paolo Mori; Steven Furnell (Ed.), Proceedings of the 10th International Conference on Information Systems Security and Privacy: . Paper presented at The 10th International Conference on Information Systems Security and Privacy, February 26-28, 2024, Rome, Italy (pp. 869-877). Portugal: SciTePress
Åpne denne publikasjonen i ny fane eller vindu >>Smart Homes as Digital Ecosystems: Exploring Privacy in IoT Contexts
2024 (engelsk)Inngår i: Proceedings of the 10th International Conference on Information Systems Security and Privacy / [ed] Gabriele Lenzini; Paolo Mori; Steven Furnell, Portugal: SciTePress, 2024, s. 869-877Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Although smart homes are tasked with an increasing number of everyday activities to keep users safe, healthy, and entertained, privacy concerns arise due to the large amount of personal data in flux. Privacy is widely acknowledged to be contextually dependent, however, the interrelated stakeholders involved in developing and delivering smart home services – IoT developers, companies, users, and lawmakers, to name a few – might approach the smart home context differently. This paper considers smart homes as digital ecosystems to support a contextual analysis of smart home privacy. A conceptual model and an ecosystem ontology are proposed through design science research methodology to systematize the analyses. Four privacy-oriented scenarios of surveillance in smart homes are discussed to demonstrate the utility of the digital ecosystem approach. The concerns pertain to power dynamics among users such as main users, smart home bystanders, parent-child dynamics, and intimate partner relationships and the responsibility of both companies and public organizations to ensure privacy and the ethical use of IoT devices over time. Continuous evaluation of the approach is encouraged to support the complex challenge of ensuring user privacy in smart homes.

sted, utgiver, år, opplag, sider
Portugal: SciTePress, 2024
Serie
ICISSP, ISSN 2184-4356
Emneord
Smart Homes, Internet of Things, Privacy, Digital Ecosystems.
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-67030 (URN)10.5220/0012458700003648 (DOI)978-989-758-683-5 (ISBN)
Konferanse
The 10th International Conference on Information Systems Security and Privacy, February 26-28, 2024, Rome, Italy
Tilgjengelig fra: 2024-05-01 Laget: 2024-05-01 Sist oppdatert: 2024-05-02bibliografisk kontrollert
Bugeja, J. & Jacobsson, A. (2023). Green Intelligent Homes: A Perspective on the Future of Smart Homes and Their Implications. In: Gary, Wills; Buttyán, Levante; Kacuk, Péter; Chang, Victor (Ed.), Proceedings of the 8th International Conference on Internet of Things, Big Data and Security (IoTBDS 2023).: . Paper presented at 8th International Conference on Internet of Things, Big Data and Security (IoTBDS 2023) (pp. 186-193). Portugal
Åpne denne publikasjonen i ny fane eller vindu >>Green Intelligent Homes: A Perspective on the Future of Smart Homes and Their Implications
2023 (engelsk)Inngår i: Proceedings of the 8th International Conference on Internet of Things, Big Data and Security (IoTBDS 2023). / [ed] Gary, Wills; Buttyán, Levante; Kacuk, Péter; Chang, Victor, Portugal, 2023, s. 186-193Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The smart home technology market is witnessing rapid growth due to the advent of more advanced, intuitive, and affordable solutions. As the adoption of these technologies becomes more prevalent, there is a need for research to explore potential avenues for pervasive smart living. This study aims to review the available literature and industry studies, along with our own experiences in the field, to identify and discuss potential future research in the smart home. We observe that the future of the smart home will likely be focused on improving the user experience, with a greater emphasis on personalization, automation, and Artificial intelligence (AI)-driven technologies, leading to what we call the "Green Intelligent Home". Through this analysis, this study aims to offer insights into how the development of smart homes could shape society in the future and the potential implications of such a development. This study concludes by suggesting a framework for knowledge development in the smart home domain.

sted, utgiver, år, opplag, sider
Portugal: , 2023
Serie
IoTBDS, ISSN 2184-4976
Emneord
Smart Home, Home Automation, Internet of Things, Artificial Intelligence, Security, Privacy, Sustainability
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-67028 (URN)10.5220/0011964800003482 (DOI)001078900300018 ()2-s2.0-85160704898 (Scopus ID)978-989-758-643-9 (ISBN)
Konferanse
8th International Conference on Internet of Things, Big Data and Security (IoTBDS 2023)
Tilgjengelig fra: 2024-05-01 Laget: 2024-05-01 Sist oppdatert: 2024-05-02bibliografisk kontrollert
Munir, H., Vogel, B. & Jacobsson, A. (2022). Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision. Information, 13(4), Article ID 203.
Åpne denne publikasjonen i ny fane eller vindu >>Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
2022 (engelsk)Inngår i: Information, E-ISSN 2078-2489, Vol. 13, nr 4, artikkel-id 203Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

sted, utgiver, år, opplag, sider
MDPI, 2022
Emneord
AI, ML, DL, digital education, literature review, dropouts, intelligent tutors, performance prediction
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-51752 (URN)10.3390/info13040203 (DOI)000786209900001 ()2-s2.0-85129306474 (Scopus ID)
Tilgjengelig fra: 2022-05-30 Laget: 2022-05-30 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Bugeja, J., Jacobsson, A. & Davidsson, P. (2022). The Ethical Smart Home: Perspectives and Guidelines. IEEE Security and Privacy, 20(1), 72-80
Åpne denne publikasjonen i ny fane eller vindu >>The Ethical Smart Home: Perspectives and Guidelines
2022 (engelsk)Inngår i: IEEE Security and Privacy, ISSN 1540-7993, E-ISSN 1558-4046, Vol. 20, nr 1, s. 72-80Artikkel i tidsskrift (Fagfellevurdert) Published
sted, utgiver, år, opplag, sider
IEEE, 2022
Emneord
ethics, smart homes, security, guidelines, privacy, internet of things, data privacy
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-47468 (URN)10.1109/MSEC.2021.3111668 (DOI)000732920200001 ()2-s2.0-85118646780 (Scopus ID)
Tilgjengelig fra: 2021-12-13 Laget: 2021-12-13 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Bugeja, J., Jacobsson, A. & Davidsson, P. (2021). PRASH: A Framework for Privacy Risk Analysis of Smart Homes.. Sensors, 21(19), Article ID 6399.
Åpne denne publikasjonen i ny fane eller vindu >>PRASH: A Framework for Privacy Risk Analysis of Smart Homes.
2021 (engelsk)Inngår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 19, artikkel-id 6399Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Smart homes promise to improve the quality of life of residents. However, they collect vasts amounts of personal and sensitive data, making privacy protection critically important. We propose a framework, called PRASH, for modeling and analyzing the privacy risks of smart homes. It is composed of three modules: a system model, a threat model, and a set of privacy metrics, which together are used for calculating the privacy risk exposure of a smart home system. By representing a smart home through a formal specification, PRASH allows for early identification of threats, better planning for risk management scenarios, and mitigation of potential impacts caused by attacks before they compromise the lives of residents. To demonstrate the capabilities of PRASH, an executable version of the smart home system configuration was generated using the proposed formal specification, which was then analyzed to find potential attack paths while also mitigating the impacts of those attacks. Thereby, we add important contributions to the body of knowledge on the mitigations of threat agents violating the privacy of users in their homes. Overall, the use of PRASH will help residents to preserve their right to privacy in the face of the emerging challenges affecting smart homes.

sted, utgiver, år, opplag, sider
MDPI, 2021
Emneord
IoT, attack taxonomy, privacy, privacy metrics, risk analysis, smart home, system model, threat model
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-46396 (URN)10.3390/s21196399 (DOI)000759972000012 ()34640718 (PubMedID)2-s2.0-85115805495 (Scopus ID)
Tilgjengelig fra: 2021-10-18 Laget: 2021-10-18 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Bugeja, J., Jacobsson, A. & Davidsson, P. (2020). A Privacy-Centered System Model for Smart Connected Homes. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops: PerCom Workshops. Paper presented at IEEE PerCom. IEEE
Åpne denne publikasjonen i ny fane eller vindu >>A Privacy-Centered System Model for Smart Connected Homes
2020 (engelsk)Inngår i: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops: PerCom Workshops, IEEE, 2020Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Smart connected homes are integrated with heterogeneous Internet-connected devices interacting with the physical environment and human users. While they have become an established research area, there is no common understanding of what composes such a pervasive environment making it challenging to perform a scientific analysis of the domain. This is especially evident when it comes to discourse about privacy threats. Recognizing this, we aim to describe a generic smart connected home, including the data it deals with in a novel privacy-centered system model. Such is done using concepts borrowed from the theory of Contextual Integrity. Furthermore, we represent privacy threats formally using the proposed model. To illustrate the usage of the model, we apply it to the design of an ambient-assisted living use-case and demonstrate how it can be used for identifying and analyzing the privacy threats directed to smart connected homes.

sted, utgiver, år, opplag, sider
IEEE, 2020
Emneord
Internet of Things, system model, privacy, privacy threats, home data, smart home, smart living
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-18127 (URN)10.1109/PerComWorkshops48775.2020.9156246 (DOI)000612838200136 ()2-s2.0-85091968572 (Scopus ID)978-1-7281-4716-1 (ISBN)
Konferanse
IEEE PerCom
Tilgjengelig fra: 2020-08-25 Laget: 2020-08-25 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Bugeja, J., Jacobsson, A. & Davidsson, P. (2020). Is Your Home Becoming a Spy?: A Data-Centered Analysis and Classification of Smart Connected Home Systems. In: IoT '20: Proceedings of the 10th International Conference on the Internet of Things. Paper presented at IoT '20. New York, United States: ACM Digital Library, Article ID 17.
Åpne denne publikasjonen i ny fane eller vindu >>Is Your Home Becoming a Spy?: A Data-Centered Analysis and Classification of Smart Connected Home Systems
2020 (engelsk)Inngår i: IoT '20: Proceedings of the 10th International Conference on the Internet of Things, New York, United States: ACM Digital Library, 2020, artikkel-id 17Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Smart connected home systems bring different privacy challenges to residents. The contribution of this paper is a novel privacy grounded classification of smart connected home systems that is focused on personal data exposure. This classification is built empirically through k-means cluster analysis from the technical specification of 81 commercial Internet of Things (IoT) systems as featured in PrivacyNotIncluded – an online database of consumer IoT systems. The attained classification helps us better understand the privacy implications and what is at stake with different smart connected home systems. Furthermore, we survey the entire spectrum of analyzed systems for their data collection capabilities. Systems were classified into four tiers: app-based accessors, watchers, location harvesters, and listeners, based on the sensing data the systems collect. Our findings indicate that being surveilled inside your home is a realistic threat, particularly, as the majority of the surveyed in-home IoT systems are installed with cameras, microphones, and location trackers. Finally, we identify research directions and suggest some best practices to mitigate the threat of in-house surveillance.

sted, utgiver, år, opplag, sider
New York, United States: ACM Digital Library, 2020
Emneord
IoT, smart home, home automation, privacy, unsupervised classification, survey, web mining
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-18599 (URN)10.1145/3410992.3411012 (DOI)2-s2.0-85123040173 (Scopus ID)978-1-4503-8758-3 (ISBN)
Konferanse
IoT '20
Tilgjengelig fra: 2020-10-10 Laget: 2020-10-10 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Bugeja, J., Jacobsson, A. & Spalazzese, R. (2020). On the Analysis of Semantic Denial-of-Service Attacks Affecting Smart Living Devices. In: Kohei Arai, Supriya Kapoor, Rahul Bhatia (Ed.), Intelligent Computing: Proceedings of the 2020 Computing Conference. Paper presented at Computing Conference 2020. Springer, 2
Åpne denne publikasjonen i ny fane eller vindu >>On the Analysis of Semantic Denial-of-Service Attacks Affecting Smart Living Devices
2020 (engelsk)Inngår i: Intelligent Computing: Proceedings of the 2020 Computing Conference / [ed] Kohei Arai, Supriya Kapoor, Rahul Bhatia, Springer, 2020, Vol. 2Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

With the interconnectedness of heterogeneous IoT devices being deployed in smart living spaces, it is imperative to assure that connected devices are resilient against Denial-of-Service (DoS) attacks. DoS attacks may cause economic damage but may also jeopardize the life of individuals, e.g., in a smart home healthcare environment since there might be situations (e.g., heart attacks), when urgent and timely actions are crucial. To achieve a better understanding of the DoS attack scenario in the ever so private home environment, we conduct a vulnerability assessment of five commercial-off-the-shelf IoT devices: a gaming console, media player, lighting system, connected TV, and IP camera, that are typically found in a smart living space. This study was conducted using an automated vulnerability scanner – Open Vulnerability Assessment System (OpenVAS) – and focuses on semantic DoS attacks. The results of the conducted experiment indicate that the majority of the tested devices are prone to DoS attacks, in particular those caused by a failure to manage exceptional conditions, leading to a total compromise of their availability. To understand the root causes for successful attacks, we analyze the payload code, identify the weaknesses exploited, and propose some mitigations that can be adopted by smart living developers and consumers.

sted, utgiver, år, opplag, sider
Springer, 2020
Serie
Advances in Intelligent Systems and Computing book series (AISC), ISSN 2194-5357, E-ISSN 2194-5365 ; 1229
Emneord
DoS, IoT, OpenVAS, Smart home, Security, vulnerabilities, risks
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-17692 (URN)10.1007/978-3-030-52246-9_32 (DOI)2-s2.0-85088500921 (Scopus ID)978-3-030-52246-9 (ISBN)978-3-030-52245-2 (ISBN)
Konferanse
Computing Conference 2020
Tilgjengelig fra: 2020-07-08 Laget: 2020-07-08 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Bugeja, J. & Jacobsson, A. (2020). On the Design of a Privacy-Centered Data Lifecycle for Smart Living Spaces (576ed.). In: Michael Friedewald, Melek Önen, Eva Lievens, Stephan Krenn, and Samuel Fricker (Ed.), Privacy and Identity Management. Data for Better Living: AI and Privacy: 14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School, Windisch, Switzerland, August 19--23, 2019, Revised Selected Papers (pp. 126-141). Springer
Åpne denne publikasjonen i ny fane eller vindu >>On the Design of a Privacy-Centered Data Lifecycle for Smart Living Spaces
2020 (engelsk)Inngår i: Privacy and Identity Management. Data for Better Living: AI and Privacy: 14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School, Windisch, Switzerland, August 19--23, 2019, Revised Selected Papers / [ed] Michael Friedewald, Melek Önen, Eva Lievens, Stephan Krenn, and Samuel Fricker, Springer, 2020, 576, s. 126-141Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

Many living spaces, such as homes, are becoming smarter and connected by using Internet of Things (IoT) technologies. Such systems should ideally be privacy-centered by design given the sensitive and personal data they commonly deal with. Nonetheless, few systematic methodologies exist that deal with privacy threats affecting IoT-based systems. In this paper, we capture the generic function of an IoT system to model privacy so that threats affecting such contexts can be identified and categorized at system design stage. In effect, we integrate an extension to so called Data Flow Diagrams (DFD) in the model, which provides the means to handle the privacy-specific threats in IoT systems. To demonstrate the usefulness of the model, we apply it to the design of a realistic use-case involving Facebook Portal. We use that as a means to elicit the privacy threats and mitigations that can be adopted therein. Overall, we believe that the proposed extension and categorization of privacy threats provide a useful addition to IoT practitioners and researchers in support for the adoption of sound privacy-centered principles in the early stages of the smart living design process.

sted, utgiver, år, opplag, sider
Springer, 2020 Opplag: 576
Serie
IFIP Advances in Information and Communication Technology book series, ISSN 1868-4238, E-ISSN 1868-422X ; 576
Emneord
IoT, Data lifecycle, Data Flow Diagrams, Data privacy, Privacy threats, Smart connected home, Smart living space, Facebook Portal
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-16962 (URN)10.1007/978-3-030-42504-3_9 (DOI)2-s2.0-85082383912 (Scopus ID)978-3-030-42503-6 (ISBN)978-3-030-42504-3 (ISBN)
Merknad

14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School, Windisch, Switzerland, August 19--23, 2019, Revised Selected Papers

Tilgjengelig fra: 2020-03-31 Laget: 2020-03-31 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Prosjekter
Forskningsprofilen Internet of Things and People; Malmö universitet; Publikasjoner
Banda, L., Mjumo, M. & Mekuria, F. (2022). Business Models for 5G and Future Mobile Network Operators. In: 2022 IEEE Future Networks World Forum (FNWF): . Paper presented at IEEE Future Networks World Forum FNWF 2022, Montreal, QC, Canada, 10-14 October 2022. IEEE, Article ID M17754.
Securing IOT Devices in a Dynamic Environment: The Case of Drones; Malmö universitet, Internet of Things and People (IOTAP)AVANS projekt: "Internet of Things Master's Program"; Malmö universitet
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-8512-2976