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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: 2024-02-05Bibliographically 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
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
Open this publication in new window or tab >>A Privacy-Centered System Model for Smart Connected Homes
2020 (English)In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops: PerCom Workshops, IEEE, 2020Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Internet of Things, system model, privacy, privacy threats, home data, smart home, smart living
National Category
Computer Sciences
Identifiers
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)
Conference
IEEE PerCom
Available from: 2020-08-25 Created: 2020-08-25 Last updated: 2024-02-05Bibliographically approved
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.
Open this publication in new window or tab >>Is Your Home Becoming a Spy?: A Data-Centered Analysis and Classification of Smart Connected Home Systems
2020 (English)In: IoT '20: Proceedings of the 10th International Conference on the Internet of Things, New York, United States: ACM Digital Library, 2020, article id 17Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York, United States: ACM Digital Library, 2020
Keywords
IoT, smart home, home automation, privacy, unsupervised classification, survey, web mining
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-18599 (URN)10.1145/3410992.3411012 (DOI)2-s2.0-85123040173 (Scopus ID)978-1-4503-8758-3 (ISBN)
Conference
IoT '20
Available from: 2020-10-10 Created: 2020-10-10 Last updated: 2024-02-05Bibliographically approved
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
Open this publication in new window or tab >>On the Analysis of Semantic Denial-of-Service Attacks Affecting Smart Living Devices
2020 (English)In: Intelligent Computing: Proceedings of the 2020 Computing Conference / [ed] Kohei Arai, Supriya Kapoor, Rahul Bhatia, Springer, 2020, Vol. 2Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2020
Series
Advances in Intelligent Systems and Computing book series (AISC), ISSN 2194-5357, E-ISSN 2194-5365 ; 1229
Keywords
DoS, IoT, OpenVAS, Smart home, Security, vulnerabilities, risks
National Category
Computer Sciences
Identifiers
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)
Conference
Computing Conference 2020
Available from: 2020-07-08 Created: 2020-07-08 Last updated: 2024-02-05Bibliographically approved
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
Open this publication in new window or tab >>On the Design of a Privacy-Centered Data Lifecycle for Smart Living Spaces
2020 (English)In: 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, p. 126-141Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2020 Edition: 576
Series
IFIP Advances in Information and Communication Technology book series, ISSN 1868-4238, E-ISSN 1868-422X ; 576
Keywords
IoT, Data lifecycle, Data Flow Diagrams, Data privacy, Privacy threats, Smart connected home, Smart living space, Facebook Portal
National Category
Computer Sciences
Identifiers
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)
Note

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

Available from: 2020-03-31 Created: 2020-03-31 Last updated: 2024-02-05Bibliographically approved
Bugeja, J., Vogel, B., Jacobsson, A. & Varshney, R. (2019). IoTSM: An End-to-end Security Model for IoT Ecosystems (ed.). In: (Ed.), 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops): . Paper presented at 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerLS 2019 - Third International Workshop on Pervasive Smart Living Spaces), Kyoto, Japan (March 11–15, 2019). IEEE
Open this publication in new window or tab >>IoTSM: An End-to-end Security Model for IoT Ecosystems
2019 (English)In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The Internet of Things (IoT) market is growing rapidly, allowing continuous evolution of new technologies. Alongside this development, most IoT devices are easy to compromise, as security is often not a prioritized characteristic. This paper proposes a novel IoT Security Model (IoTSM) that can be used by organizations to formulate and implement a strategy for developing end-to-end IoT security. IoTSM is grounded by the Software Assurance Maturity Model (SAMM) framework, however it expands it with new security practices and empirical data gathered from IoT practitioners. Moreover, we generalize the model into a conceptual framework. This approach allows the formal analysis for security in general and evaluates an organization’s security practices. Overall, our proposed approach can help researchers, practitioners, and IoT organizations, to discourse about IoT security from an end-to-end perspective.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Pervasive Computing and Communications, ISSN 2474-249X, E-ISSN 2474-2503
Keywords
IoT, end-to-end security, security model, secure development
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-16813 (URN)10.1109/PERCOMW.2019.8730672 (DOI)000476951900049 ()2-s2.0-85067936836 (Scopus ID)28794 (Local ID)978-1-5386-9151-9 (ISBN)28794 (Archive number)28794 (OAI)
Conference
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerLS 2019 - Third International Workshop on Pervasive Smart Living Spaces), Kyoto, Japan (March 11–15, 2019)
Available from: 2020-03-30 Created: 2020-03-30 Last updated: 2024-02-05Bibliographically approved
Bugeja, J., Jacobsson, A. & Davidsson, P. (2018). An Empirical Analysis of Smart Connected Home Data (ed.). In: (Ed.), Internet of Things – ICIOT 2018: . Paper presented at International Conference on Internet of Things (ICIOT 2018), Seattle, USA (June 25 - June 30) (pp. 134-149). Springer
Open this publication in new window or tab >>An Empirical Analysis of Smart Connected Home Data
2018 (English)In: Internet of Things – ICIOT 2018, Springer, 2018, p. 134-149Conference paper, Published paper (Refereed)
Abstract [en]

The increasing presence of heterogeneous Internet of Things devices inside the home brings with it added convenience and value to the householders. At the same time, these devices tend to be Internet-connected and continuously monitor and collect data about the residents and their daily lifestyle activities. Such data can be of a sensitive nature, given that the house is the place where privacy is naturally expected. To gain insight into this state of affairs, we empirically investigate the privacy policies of 87 different categories of commercial smart home devices in terms of data being collected. This is done using a combination of manual and data mining techniques. The overall contribution of this work is a model that identifies and categorizes smart connected home data in terms of its collection mode, collection method, and collection phase. Our findings bring up several implications for smart connected home privacy, which include the need for better security controls to safeguard the privacy of the householders.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10972
Keywords
Smart home, IoT, Data model, Privacy policies
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-12509 (URN)10.1007/978-3-319-94370-1_10 (DOI)2-s2.0-85049026562 (Scopus ID)26281 (Local ID)26281 (Archive number)26281 (OAI)
Conference
International Conference on Internet of Things (ICIOT 2018), Seattle, USA (June 25 - June 30)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2024-02-05Bibliographically approved
Bugeja, J., Jönsson, D. & Jacobsson, A. (2018). An Investigation of Vulnerabilities in Smart Connected Cameras (ed.). In: (Ed.), 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops): . Paper presented at IEEE PerCom 2018 - Second International Workshop on Pervasive Smart Living Spaces (PerLS 2018), Athens, Greece (19 March - 23 March) (pp. 656-661). IEEE
Open this publication in new window or tab >>An Investigation of Vulnerabilities in Smart Connected Cameras
2018 (English)In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2018, p. 656-661Conference paper, Published paper (Refereed)
Abstract [en]

The Internet of Things is enabling innovative ser-vices promising added convenience and value in various domains such as the smart home. Increasingly, households, office envi-ronments and cities, are being fitted with smart camera systems aimed to enhance the security of citizens. At the same time, sev-eral systems being deployed suffer from weak security implemen-tations. Recognizing this, and to understand the extent of this situation, in this study we perform a global vulnerability assess-ment using the Shodan search engine and the Common Vulnera-bilities and Exposures database. This is done to detect smart con-nected cameras exposed on the Internet alongside their sensitive, potentially private, data being broadcasted. Furthermore, we discuss whether the discovered data can be used to compromise the safety and privacy of individuals, and identify some mitiga-tions that can be adopted. The results indicate that a significant number of smart cameras are indeed prone to diverse security and privacy vulnerabilities.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
IoT, IoT security, Shodan, smart connected cameras, smart connected homes, vulnerabilities
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-12708 (URN)10.1109/PERCOMW.2018.8480184 (DOI)000541062400110 ()2-s2.0-85056473592 (Scopus ID)26328 (Local ID)978-1-5386-3227-7 (ISBN)978-1-5386-3228-4 (ISBN)26328 (Archive number)26328 (OAI)
Conference
IEEE PerCom 2018 - Second International Workshop on Pervasive Smart Living Spaces (PerLS 2018), Athens, Greece (19 March - 23 March)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2024-04-05Bibliographically 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)Internet of Things Master's Program; Malmö University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8512-2976

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