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Adewole, K. S., Jacobsson, A. & Davidsson, P. (2024). ARAM: Assets-based Risk Assessment Model for Connected Smart Homes. In: 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud): . Paper presented at 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud), Vienna, Austria, 19-21 August 2024. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>ARAM: Assets-based Risk Assessment Model for Connected Smart Homes
2024 (English)In: 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud), Institute of Electrical and Electronics Engineers (IEEE), 2024Conference paper, Published paper (Refereed)
Abstract [en]

Connected smart homes (CSH) have benefited immensely from emerging Internet of Things (IoT) technology. CSH is intended to support everyday life in the private seclusion of the home, and typically covers the integration of smart devices such as smart meters, heating, ventilation, and air conditioning (HVAC), intelligent lightening, and voice-activated assistants among others. Nevertheless, the risks associated with CSH assets are often of high concern. For instance, energy consumption monitoring through smart meters can reveal sensitive information that may pose a privacy risk to home occupants if not properly managed. Existing risk assessment approaches for CSH tend to focus on qualitative risk assessment methodologies, such as operationally critical threat, asset, and vulnerability evaluation (OCTAVE). However, security risk assessment, particularly for IoT environments, demands both qualitative and quantitative risk assessment. This paper proposes assets-based risk assessment model that integrates both qualitative and quantitative risk assessment to determine the risk related to assets in CSH when a specific service is used. We apply fuzzy Analytic Hierarchy Process (fuzzy AHP) to address the subjective assessment of the IoT risk analysts and stakeholders. The applicability of the proposed model is illustrated through a use case that constitutes a scenario connected to service delivery in CSH. The proposed model provides a guideline to researchers and practitioners on how to quantify the risks targeting assets in CSH.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
International Conference on Future Internet of Things and Cloud, ISSN 2996-1009, E-ISSN 2996-1017
Keywords
Internet of Things, connected smart home, threat and vulnerability, risk assessment, fuzzy AHP, security and privacy
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-72735 (URN)10.1109/FiCloud62933.2024.00016 (DOI)2-s2.0-85211238528 (Scopus ID)979-8-3315-2719-8 (ISBN)979-8-3315-2720-4 (ISBN)
Conference
2024 11th International Conference on Future Internet of Things and Cloud (FiCloud), Vienna, Austria, 19-21 August 2024
Available from: 2024-12-13 Created: 2024-12-13 Last updated: 2024-12-16Bibliographically approved
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
Open this publication in new window or tab >>Foreword
2024 (English)In: IoTBDS 2024 Final Program and Book of Abstracts: The 9th International Conference on Internet of Things, Big Data and Security, Portugal: SciTePress, 2024, , p. 43p. 5-6Conference paper, Published paper (Other academic)
Abstract [en]

N/A.

Place, publisher, year, edition, pages
Portugal: SciTePress, 2024. p. 43
Series
IoTBDS, E-ISSN 2184-4976
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-67031 (URN)
Conference
The 9th International Conference on Internet of Things, Big Data and Security, Angers, France, April 28-30 2024
Available from: 2024-05-01 Created: 2024-05-01 Last updated: 2024-11-29Bibliographically approved
Adewole, K. S. & Jacobsson, A. (2024). HOMEFUS: A Privacy and Security-Aware Model for IoT Data Fusion in Smart Connected Homes. In: Proceedings of the 9th International Conference on Internet of Things, Big Data and Security IoTBDS: Volume 1. Paper presented at IoTBDS 2024 : 9th International Conference on Internet of Things, Big Data and Security, 28 - 30 April 2024, Angers, France. (pp. 133-140). SciTePress
Open this publication in new window or tab >>HOMEFUS: A Privacy and Security-Aware Model for IoT Data Fusion in Smart Connected Homes
2024 (English)In: Proceedings of the 9th International Conference on Internet of Things, Big Data and Security IoTBDS: Volume 1, SciTePress, 2024, p. 133-140Conference paper, Published paper (Refereed)
Abstract [en]

The benefit associated with the deployment of Internet of Things (IoT) technology is increasing daily. IoT has revolutionized our ways of life, especially when we consider its applications in smart connected homes. Smart devices at home enable the collection of data from multiple sensors for a range of applications and services. Nevertheless, the security and privacy issues associated with aggregating multiple sensors’ data in smart connected homes have not yet been sufficiently prioritized. Along this development, this paper proposes HOMEFUS, a privacy and security-aware model that leverages information theoretic correlation analysis and gradient boosting to fuse multiple sensors’ data at the edge nodes of smart connected homes. HOMEFUS employs federated learning, edge and cloud computing to reduce privacy leakage of sensitive data. To demonstrate its applicability, we show that the proposed model meets the requirements for efficient data fusion pipelines. The model guides practitio ners and researchers on how to setup secure smart connected homes that comply with privacy laws, regulations, and standards. 

Place, publisher, year, edition, pages
SciTePress, 2024
Series
IoTBDS, E-ISSN 2184-4976
Keywords
Smart Homes, Internet of Things, Data Fusion, Security, Privacy, Federated Learning, Sensors Selection
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-70581 (URN)10.5220/0012437900003705 (DOI)2-s2.0-85193985565 (Scopus ID)978-989-758-699-6 (ISBN)
Conference
IoTBDS 2024 : 9th International Conference on Internet of Things, Big Data and Security, 28 - 30 April 2024, Angers, France.
Available from: 2024-08-24 Created: 2024-08-24 Last updated: 2024-11-29Bibliographically approved
Adewole, K. S. & Jacobsson, A. (2024). LPM: A Lightweight Privacy-Aware Model for IoT Data Fusion in Smart Connected Homes. In: 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech): . Paper presented at 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech), June 20-23 2023, Bol and Split, Croatia.. IEEE
Open this publication in new window or tab >>LPM: A Lightweight Privacy-Aware Model for IoT Data Fusion in Smart Connected Homes
2024 (English)In: 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech), IEEE, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) technology has created a new dimension for data collection, transmission, processing, storage, and service delivery. With the advantages offered by IoT technologies, interest in smart home automation has increased over the years. Nevertheless, smart connected homes are characterized with the security and privacy problems that are associated with aggregating multiple sensors' data and exposing them to the Internet. In this paper, we propose LPM, a lightweight privacy-aware model that leverages information theoretic correlation analysis and gradient boosting to fuse multiple sensors' data at the edge nodes of smart connected homes. LPM employs federated learning, edge and cloud computing to reduce privacy leakages of sensitive data. To demonstrate its applicability, two services, commonly provided by smart homes, i.e., occupancy detection and people count estimation, were experimentally investigated. The results show that LPM can achieve accuracy, F1 score and AUC-ROC of 99.98%, 99.13%, and 99.98% respectively for occupancy detection as well as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 of 0.0011,0.0175, and 98.39% respectively for people count estimation. LPM offers the opportunity to each smart connected home to participate in collaborative learning that is achieved through the federated machine learning component of the proposed model.

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
Data privacy, Cloud computing, Federated learning, Image edge detection, Estimation, Smart homes, Sensor phenomena and characterization
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-70583 (URN)10.23919/SpliTech61897.2024.10612646 (DOI)001297807000106 ()2-s2.0-85202441432 (Scopus ID)979-8-3503-9079-7 (ISBN)978-953-290-135-1 (ISBN)
Conference
2024 9th International Conference on Smart and Sustainable Technologies (SpliTech), June 20-23 2023, Bol and Split, Croatia.
Available from: 2024-08-24 Created: 2024-08-24 Last updated: 2025-01-07Bibliographically approved
Kobusinska, A., Jacobsson, A. & Chang, V. (Eds.). (2024). Proceedings of the 9th International Conference on Internet of Things, Big Data and Security: April 28-30, 2024, in Angers, France. Paper presented at 9th International Conference on Internet of Things, Big Data and Security, Angers, France, April 28-30, 2024. SciTePress
Open this publication in new window or tab >>Proceedings of the 9th International Conference on Internet of Things, Big Data and Security: April 28-30, 2024, in Angers, France
2024 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

This book contains the proceedings of the 9th International Conference on the Internet of Things, Big Data and Security (IoTBDS 2024). This year, IoTBDS was held in Angers, France, from April 28 - 30, 2024. It was sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). The Internet of Things (IoT) is a platform that allows a network of devices (sensors, smart meters, etc.) to communicate, analyze data and process information collaboratively in the service of individuals or organizations'. The IoT network can generate large amounts of data in a variety of formats and use different protocols, which can be stored and processed in the cloud. The conference looks to address the issues surrounding IoT devices, their interconnectedness and the services they may offer, including efficient, effective and secure analysis of the data IoT produces using machine learning and other advanced techniques, models and tools, and issues of security, privacy and trust that will emerge as IoT technologies mature and become part of our everyday lives. Big Data (BD) has core values of volume, velocity, variety and veracity. After collecting much data from IoT, BD can be jointly used with machine learning, AI, statistical and other advanced techniques, models and methods, which can create value for people and organizations adopting it, since forecasting, deep analysis and analytics can help identify weaknesses and make improvements based on different analysis. Maintaining a high level of security and privacy for data in IoT is crucial, and we welcome recommendations, solutions, demonstrations and best practices for all forms of security and privacy for IoT and BD. IoTBDS 2024 received 51 paper submissions from 22 countries of which 20% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least 2 but usually 3 or more members of the International Program Committee, which is composed of established researchers and domain experts. The high quality of the IoTBDS 2024 program is enhanced by the keynote lecture delivered by distinguished speakers who are renowned experts in their fields: Luigi Atzori (Università degli Studi di Cagliari, Italy), Patrick Hung (Faculty of Business and IT, Ontario Tech University, Canada), Matthieu Deboeuf Rouchon (Capgemini Engineering, France) and Samuel Fosso Wamba (Toulouse Business School, France). The conference is complemented by a Workshop on Collaborative EU Research Projects, chaired by Victor Chang and Jia-Chun Lin. All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index. As recognition for the best contributions, several awards based on the combined marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as assessed by session chairs at the conference venue, are conferred at the closing session of the conference. A shortlist of papers presented at the conference will be selected for publication of extended and revised versions in the special issues of the Springer Nature Computer Science Journal, Journal of Global Information Management, Big Data Journal and Internet of Things. The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are herewith recorded. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly capable team. We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of IoTBDS, details of which are available at https://iotbds.scitevents.org.

Place, publisher, year, edition, pages
SciTePress, 2024
Series
IoTBDS, E-ISSN 2184-4976
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-70269 (URN)10.5220/0000186600003705 (DOI)978-989-758-699-6 (ISBN)
Conference
9th International Conference on Internet of Things, Big Data and Security, Angers, France, April 28-30, 2024
Available from: 2024-08-15 Created: 2024-08-15 Last updated: 2024-08-15Bibliographically approved
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
Open this publication in new window or tab >>Smart Homes as Digital Ecosystems: Exploring Privacy in IoT Contexts
2024 (English)In: Proceedings of the 10th International Conference on Information Systems Security and Privacy / [ed] Gabriele Lenzini; Paolo Mori; Steven Furnell, Portugal: SciTePress, 2024, p. 869-877Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Portugal: SciTePress, 2024
Series
ICISSP, ISSN 2184-4356
Keywords
Smart Homes, Internet of Things, Privacy, Digital Ecosystems.
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:mau:diva-67030 (URN)10.5220/0012458700003648 (DOI)2-s2.0-85190898797 (Scopus ID)978-989-758-683-5 (ISBN)
Conference
The 10th International Conference on Information Systems Security and Privacy, February 26-28, 2024, Rome, Italy
Available from: 2024-05-01 Created: 2024-05-01 Last updated: 2024-12-09Bibliographically approved
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
Open this publication in new window or tab >>Green Intelligent Homes: A Perspective on the Future of Smart Homes and Their Implications
2023 (English)In: 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, p. 186-193Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Portugal: , 2023
Series
IoTBDS, ISSN 2184-4976
Keywords
Smart Home, Home Automation, Internet of Things, Artificial Intelligence, Security, Privacy, Sustainability
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-67028 (URN)10.5220/0011964800003482 (DOI)001078900300018 ()2-s2.0-85160704898 (Scopus ID)978-989-758-643-9 (ISBN)
Conference
8th International Conference on Internet of Things, Big Data and Security (IoTBDS 2023)
Available from: 2024-05-01 Created: 2024-05-01 Last updated: 2024-05-02Bibliographically approved
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.
Open this publication in new window or tab >>Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
2022 (English)In: Information, E-ISSN 2078-2489, Vol. 13, no 4, article id 203Article, review/survey (Refereed) 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.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
AI, ML, DL, digital education, literature review, dropouts, intelligent tutors, performance prediction
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-51752 (URN)10.3390/info13040203 (DOI)000786209900001 ()2-s2.0-85129306474 (Scopus ID)
Available from: 2022-05-30 Created: 2022-05-30 Last updated: 2024-09-18Bibliographically approved
Bugeja, J., Jacobsson, A. & Davidsson, P. (2022). The Ethical Smart Home: Perspectives and Guidelines. IEEE Security and Privacy, 20(1), 72-80
Open this publication in new window or tab >>The Ethical Smart Home: Perspectives and Guidelines
2022 (English)In: IEEE Security and Privacy, ISSN 1540-7993, E-ISSN 1558-4046, Vol. 20, no 1, p. 72-80Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2022
Keywords
ethics, smart homes, security, guidelines, privacy, internet of things, data privacy
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mau:diva-47468 (URN)10.1109/MSEC.2021.3111668 (DOI)000732920200001 ()2-s2.0-85118646780 (Scopus ID)
Available from: 2021-12-13 Created: 2021-12-13 Last updated: 2024-02-05Bibliographically approved
Bugeja, J., Jacobsson, A. & Davidsson, P. (2021). PRASH: A Framework for Privacy Risk Analysis of Smart Homes.. Sensors, 21(19), Article ID 6399.
Open this publication in new window or tab >>PRASH: A Framework for Privacy Risk Analysis of Smart Homes.
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 19, article id 6399Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
IoT, attack taxonomy, privacy, privacy metrics, risk analysis, smart home, system model, threat model
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-46396 (URN)10.3390/s21196399 (DOI)000759972000012 ()34640718 (PubMedID)2-s2.0-85115805495 (Scopus ID)
Available from: 2021-10-18 Created: 2021-10-18 Last updated: 2024-02-05Bibliographically approved
Projects
Internet of Things and People Research Profile; Malmö University; Publications
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 University, Internet of Things and People (IOTAP) (Closed down 2024-12-31)Internet of Things Master's Program; Malmö University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8512-2976

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