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Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Åpne denne publikasjonen i ny fane eller vindu >>The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning
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2023 (engelsk)Inngår i: Applied Sciences, E-ISSN 2076-3417, Vol. 13, nr 11, artikkel-id 6516Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

sted, utgiver, år, opplag, sider
MDPI, 2023
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Tilgjengelig fra: 2023-06-07 Laget: 2023-06-07 Sist oppdatert: 2023-09-05bibliografisk kontrollert
Alkhabbas, F., Alsadi, M., Alawadi, S., Awaysheh, F. M., Kebande, V. R. & Moghaddam, M. T. (2022). ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems.. Sensors, 22(18), Article ID 6842.
Åpne denne publikasjonen i ny fane eller vindu >>ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems.
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2022 (engelsk)Inngår i: Sensors, E-ISSN 1424-8220, Vol. 22, nr 18, artikkel-id 6842Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems' environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems' security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach's feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems' constituents to learn about security threats in their environments collaboratively.

sted, utgiver, år, opplag, sider
MDPI, 2022
Emneord
Internet of Things, blockchain, multi-agent systems, security, self-adaptive and goal-driven systems, software architecture
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-55176 (URN)10.3390/s22186842 (DOI)000858946100001 ()36146191 (PubMedID)2-s2.0-85138427481 (Scopus ID)
Tilgjengelig fra: 2022-10-17 Laget: 2022-10-17 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Alawadi, S., Kebande, V. R., Dong, Y., Bugeja, J., Persson, J. A. & Olsson, C. M. (2021). A Federated Interactive Learning IoT-Based Health Monitoring Platform. In: New Trends in Database and Information Systems: . Paper presented at ADBIS 2021: New Trends in Database and Information Systems. Tartu, Estonia, August 24-26, 2021. (pp. 235-246). Springer
Åpne denne publikasjonen i ny fane eller vindu >>A Federated Interactive Learning IoT-Based Health Monitoring Platform
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2021 (engelsk)Inngår i: New Trends in Database and Information Systems, Springer, 2021, s. 235-246Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Remote health monitoring is a trend for better health management which necessitates the need for secure monitoring and privacy-preservation of patient data. Moreover, accurate and continuous monitoring of personal health status may require expert validation in an active learning strategy. As a result, this paper proposes a Federated Interactive Learning IoT-based Health Monitoring Platform (FIL-IoT-HMP) which incorporates multi-expert feedback as ‘Human-in-the-loop’ in an active learning strategy in order to improve the clients’ Machine Learning (ML) models. The authors have proposed an architecture and conducted an experiment as a proof of concept. Federated learning approach has been preferred in this context given that it strengthens privacy by allowing the global model to be trained while sensitive data is retained at the local edge nodes. Also, each model’s accuracy is improved while privacy and security of data has been upheld.

sted, utgiver, år, opplag, sider
Springer, 2021
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1450
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-47470 (URN)10.1007/978-3-030-85082-1_21 (DOI)000775759800021 ()2-s2.0-85115134304 (Scopus ID)978-3-030-85081-4 (ISBN)978-3-030-85082-1 (ISBN)
Konferanse
ADBIS 2021: New Trends in Database and Information Systems. Tartu, Estonia, August 24-26, 2021.
Tilgjengelig fra: 2021-12-13 Laget: 2021-12-13 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Kebande, V. R., Alawadi, S., Awaysheh, F. & Persson, J. A. (2021). Active Machine Learning Adversarial Attack Detection in the User Feedback Process. IEEE Access, 9
Åpne denne publikasjonen i ny fane eller vindu >>Active Machine Learning Adversarial Attack Detection in the User Feedback Process
2021 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, E-ISSN 2169-3536, Vol. 9Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Modern Information and Communication Technology (ICT)-based applications utilize currenttechnological advancements for purposes of streaming data, as a way of adapting to the ever-changingtechnological landscape. Such efforts require providing accurate, meaningful, and trustworthy output fromthe streaming sensors particularly during dynamic virtual sensing. However, to ensure that the sensingecosystem is devoid of any sensor threats or active attacks, it is paramount to implement secure real-timestrategies. Fundamentally, real-time detection of adversarial attacks/instances during the User FeedbackProcess (UFP) is the key to forecasting potential attacks in active learning. Also, according to existingliterature, there lacks a comprehensive study that has a focus on adversarial detection from an activemachine learning perspective at the time of writing this paper. Therefore, the authors posit the importance ofdetecting adversarial attacks in active learning strategy. Attack in the context of this paper through a UFPThreat driven model has been presented as any action that exerts an alteration to the learning system ordata. To achieve this, the study employed ambient data collected from a smart environment human activityrecognition from (Continuous Ambient Sensors Dataset, CASA) with fully labeled connections, where weintentionally subject the Dataset to wrong labels as a targeted/manipulative attack (by a malevolent labeler)in the UFP, with an assumption that the user-labels were connected to unique identities. While the dataset’sfocus is to classify tasks and predict activities, our study gives a focus on active adversarial strategies froman information security point of view. Furthermore, the strategies for modeling threats have been presentedusing the Meta Attack Language (MAL) compiler for purposes adversarial detection. The findings fromthe experiments conducted have shown that real-time adversarial identification and profiling during the UFPcould significantly increase the accuracy during the learning process with a high degree of certainty and pavesthe way towards an automated adversarial detection and profiling approaches on the Internet of CognitiveThings (ICoT).

sted, utgiver, år, opplag, sider
IEEE, 2021
Emneord
Adversarial detection, user-feedback-process, active machine learning, monitoring industrial feedback.
HSV kategori
Forskningsprogram
Naturvetenskapernas didaktik
Identifikatorer
urn:nbn:se:mau:diva-41020 (URN)10.1109/ACCESS.2021.3063002 (DOI)000626493900001 ()2-s2.0-85102241032 (Scopus ID)
Tilgjengelig fra: 2021-03-05 Laget: 2021-03-05 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Al-Dhaqm, A., Shukor, R., Ikuesan, R., Kebande, V. R. & Othman, S. (2021). Face Validation of Database Forensic Investigation Metamodel. Infrastructues, 6(2), 1-20, Article ID 13.
Åpne denne publikasjonen i ny fane eller vindu >>Face Validation of Database Forensic Investigation Metamodel
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2021 (engelsk)Inngår i: Infrastructues, ISSN 2412-3811, Vol. 6, nr 2, s. 1-20, artikkel-id 13Artikkel i tidsskrift, Editorial material (Annet vitenskapelig) Published
Abstract [en]

Using a face validity approach, this paper provides a validation of the Database Forensic Investigation Metamodel (DBFIM). The DBFIM was developed to solve interoperability, heterogeneity, complexity, and ambiguity in the database forensic investigation (DBFI) field, where severalmodels were identified, collected, and reviewed to develop DBFIM. However, the developedDBFIM lacked the face validity-based approach that could ensure DBFIM’s applicability in the DBFIfield. The completeness, usefulness, and logic of the developed DBFIM needed to be validated byexperts. Therefore, the objective of this paper is to perform the validation of the developed DBFIMusing the qualitative face validity approach. The face validity method is a common way of validating metamodels through subject expert inquiry on the domain application of the metamodel to assess whether the metamodel is reasonable and compatible based on the outcomes. For this purpose,six experts were nominated and selected to validate the developed DBFIM. From the expert review,the developed DBFIM was found to be complete, coherent, logical, scalable, interoperable, and useful for the DBFI field. 

sted, utgiver, år, opplag, sider
Basel: MDPI, 2021
Emneord
database forensics, digital forensic, face validity, metamodel, validation
HSV kategori
Forskningsprogram
Naturvetenskapernas didaktik
Identifikatorer
urn:nbn:se:mau:diva-39593 (URN)10.3390/infrastructures6020013 (DOI)000623662600001 ()
Tilgjengelig fra: 2021-01-20 Laget: 2021-01-20 Sist oppdatert: 2024-04-10bibliografisk kontrollert
Zawali, B., Ikuesan, R. A., Kebande, V. R., Furnell, S. & A-Dhaqm, A. (2021). Realising a Push Button Modality for Video-Based Forensics. Infrastructures, 6(4), Article ID 54.
Åpne denne publikasjonen i ny fane eller vindu >>Realising a Push Button Modality for Video-Based Forensics
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2021 (engelsk)Inngår i: Infrastructures, ISSN 2412-3811, Vol. 6, nr 4, artikkel-id 54Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Complexity and sophistication among multimedia-based tools have made it easy for perpetrators to conduct digital crimes such as counterfeiting, modification, and alteration without being detected. It may not be easy to verify the integrity of video content that, for example, has been manipulated digitally. To address this perennial investigative challenge, this paper proposes the integration of a forensically sound push button forensic modality (PBFM) model for the investigation of the MP4 video file format as a step towards automated video forensic investigation. An open-source multimedia forensic tool was developed based on the proposed PBFM model. A comprehensive evaluation of the efficiency of the tool against file alteration showed that the tool was capable of identifying falsified files, which satisfied the underlying assertion of the PBFM model. Furthermore, the outcome can be used as a complementary process for enhancing the evidence admissibility of MP4 video for forensic investigation.

sted, utgiver, år, opplag, sider
MDPI, 2021
Emneord
multimedia forensics, push button forensics, file signature alteration technique
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-42373 (URN)10.3390/infrastructures6040054 (DOI)000643749300001 ()2-s2.0-85108382505 (Scopus ID)
Tilgjengelig fra: 2021-05-27 Laget: 2021-05-27 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Kebande, V. R., Karie, N. & Ikuesan, R. (2021). Real-time monitoring as a supplementary security component of vigilantism in modern network environments. International Journal of Information Technology, 13, 5-17
Åpne denne publikasjonen i ny fane eller vindu >>Real-time monitoring as a supplementary security component of vigilantism in modern network environments
2021 (engelsk)Inngår i: International Journal of Information Technology, ISSN 2511-2104, Vol. 13, s. 5-17Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The phenomenon of network vigilantism is autonomously attributed to how anomalies and obscure activities from adversaries can be tracked in real-time. Needless to say, in today’s dynamic, virtualized, and complex network environments, it has become undeniably necessary for network administrators, analysts as well as engineers to practice network vigilantism, on traffic as well as other network events in real-time. The reason is to understand the exact security posture of an organization’s network environment at any given time. This is driven by the fact that modern network environments do, not only present new opportunities to organizations but also a different set of new and complex cybersecurity challenges that need to be resolved daily. The growing size, scope, complexity, and volume of networked devices in our modern network environments also makes it hard even for the most experienced network administrators to independently provide the breadth and depth of knowledge needed to oversee or diagnose complex network problems. Besides, with the growing number of Cyber Security Threats (CSTs) in the world today, many organisations have been forced to change the way they plan, develop and implement cybersecurity strategies as a way to reinforce their ability to respond to cybersecurity incidents. This paper, therefore, examines the relevance of Real-Time Monitoring (RTM) as a supplementary security component of vigilantism in modern network environments, more especially for proper planning, preparedness, and mitigation in case of a cybersecurity incident. Additionally, this paper also investigates some of the key issues and challenges surrounding the implementation of RTM for security vigilantism in our modern network environments.

sted, utgiver, år, opplag, sider
Springer Nature, 2021
Emneord
Real-time monitoring, Implementation, Vigilantism, Cyber security, Network environments, Issues and challenges
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-37543 (URN)10.1007/s41870-020-00585-8 (DOI)
Tilgjengelig fra: 2020-12-11 Laget: 2020-12-11 Sist oppdatert: 2022-11-02bibliografisk kontrollert
Al-Dhaqm, A., Razak, S. A., Ikuesan, R. A., Kebande, V. R. & Siddique, K. (2020). A Review of Mobile Forensic Investigation Process Models. IEEE Access, 8, 173359-173375
Åpne denne publikasjonen i ny fane eller vindu >>A Review of Mobile Forensic Investigation Process Models
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2020 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 173359-173375Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.

sted, utgiver, år, opplag, sider
IEEE, 2020
Emneord
Smart phones, Analytical models, Unified modeling language, Tools, Digital forensics, Mobile forensics, investigation process model
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-36642 (URN)10.1109/ACCESS.2020.3014615 (DOI)000575905200001 ()
Tilgjengelig fra: 2020-11-06 Laget: 2020-11-06 Sist oppdatert: 2022-11-02bibliografisk kontrollert
Al-Dhaqm, A., Abd Razak, S., Dampier, D. A., Choo, K.-K. R., Siddique, K., Ikuesan, R. A., . . . Kebande, V. R. (2020). Categorization and Organization of Database Forensic Investigation Processes. IEEE Access, 8, 112846-112858
Åpne denne publikasjonen i ny fane eller vindu >>Categorization and Organization of Database Forensic Investigation Processes
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2020 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 112846-112858Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Database forensic investigation (DBFI) is an important area of research within digital forensics. It & x2019;s importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.

sted, utgiver, år, opplag, sider
IEEE, 2020
Emneord
Databases, Data models, Servers, Digital forensics, Terminology, Adaptation models, Database forensics, database forensic investigation, investigation process model
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-17949 (URN)10.1109/ACCESS.2020.3000747 (DOI)000546414500046 ()
Tilgjengelig fra: 2020-08-14 Laget: 2020-08-14 Sist oppdatert: 2022-11-02bibliografisk kontrollert
Khorashadizadeh, S., Ikuesan, A. R. & Kebande, V. R. (2020). Generic 5G Infrastructure for IoT Ecosystem. In: Saeed, F Mohammed, F Gazem, N (Ed.), Emerging Trends in Intelligent Computing and Informatics: Data Science, Intelligent Information Systems and Smart Computing. Paper presented at 4th International Conference of Reliable Information and Communication Technology (IRICT), SEP 22-23, 2019, Johor, MALAYSIA (pp. 451-462). Springer
Åpne denne publikasjonen i ny fane eller vindu >>Generic 5G Infrastructure for IoT Ecosystem
2020 (engelsk)Inngår i: Emerging Trends in Intelligent Computing and Informatics: Data Science, Intelligent Information Systems and Smart Computing / [ed] Saeed, F Mohammed, F Gazem, N, Springer, 2020, s. 451-462Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

While the Internet of Things (IoT) is still gaining rapid adoption in an upward trajectory means across many smart areas in recent years, still, there is a need to develop a scalable ecosystem that is able to support future IoT implementations, given the heterogeneity and increased information flow among IoT devices. The lack of effective interoperability, availability, reliability, and performance in IoT are a few challenges that hinder the effectiveness of IoT deployment and communication, and this has acted as a stumbling block for the optimisation of IoT platforms. That notwithstanding, the advent of the Fifth Generation (5G) networks, has seen a significant shift on how the IoT-paradigm operates. This article introduces a discussion on the generic 5G infrastructure for IoT environment that can support future deployments. The article begins by conducting a technical review of the evolution of the First generation (1G) through 5G cellular networks. Thereafter, an illustration of the Fourth Generation (4G) architecture, stating its features as a precedent that shows to what extent 5G network may thrive or support IoT ecosystems, is given. Furthermore, the paper explores both the architectural requirements and futuristic vision of 5G infrastructure in the perspective of IoT applications. Most importantly, the paper has also explored the IoT market share and forecast on growth and how it affects different industrial sectors. The authors believe that the conclusions that have been made in this paper will act as a pacesetter and give a direction worth exploring once the 5G infrastructure for IoT ecosystems is implemented.

sted, utgiver, år, opplag, sider
Springer, 2020
Serie
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 1073
Emneord
IoT, Ecosystem, Industrial Internet of Things, Fourth Generation (4G), Fifth Generation (5G), Smart city, Healthcare, Telehealth, Telemedicine
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-37694 (URN)10.1007/978-3-030-33582-3_43 (DOI)000583758100043 ()978-3-030-33582-3 (ISBN)978-3-030-33581-6 (ISBN)
Konferanse
4th International Conference of Reliable Information and Communication Technology (IRICT), SEP 22-23, 2019, Johor, MALAYSIA
Tilgjengelig fra: 2020-12-22 Laget: 2020-12-22 Sist oppdatert: 2022-11-02bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0003-4071-4596