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  • 1.
    Al Khatib, Sultan M.
    et al.
    Al-balqa Applied University, Al Salt, Jordan.
    Alkharabsheh, Khalid
    Al-balqa Applied University, Al Salt, Jordan.
    Alawadi, Sadi
    Halmstad University, School of Information Technology. Uppsala University, Uppsala, Sweden.
    Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study2023In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 155, article id 107120Article in journal (Refereed)
    Abstract [en]

    Context: Design smell detection is considered an efficient activity that decreases maintainability expenses and improves software quality. Human context plays an essential role in this domain. Objective: In this paper, we propose a search-based approach to optimize the selection of human evaluators for design smell detection. Method: For this purpose, Dragonfly Algorithm (DA) is employed to identify the optimal or near-optimal human evaluator's profiles. An online survey is designed and asks the evaluators to evaluate a sample of classes for the presence of god class design smell. The Kappa-Fleiss test has been used to validate the proposed approach. Results: The results show that the dragonfly optimization algorithm can be utilized effectively to decrease the efforts (time, cost ) of design smell detection concerning the identification of the number and the optimal or near-optimal profile of human experts required for the evaluation process. Conclusions: A Search-based approach can be effectively used for improving a god-class design smell detection. Consequently, this leads to minimizing the maintenance cost. © 2022 The Author(s)

  • 2.
    Alkhabbas, Fahed
    et al.
    Malmö University, Malmö, Sweden.
    Alawadi, Sadi
    Halmstad University, School of Information Technology. Blekinge Institute of Technology, Karlskrona, Sweden.
    Ayyad, Majed
    Birzeit University, Birzeit, Palestine.
    Spalazzese, Romina
    Malmö University, Malmö, Sweden.
    Davidsson, Paul
    Malmö University, Malmö, Sweden.
    ART4FL: An Agent-based Architectural Approach for Trustworthy Federated Learning in the IoT2023In: 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, 2023, p. 270-275Conference paper (Refereed)
    Abstract [en]

    The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has opened up for the development of novel types of systems and services. Federated Learning (FL) has enabled the systems to collaboratively train their ML models while preserving the privacy of the data collected by their IoT devices and objects. Several FL frameworks have been developed, however, they do not enable FL in open, distributed, and heterogeneous IoT environments. Specifically, they do not support systems that collect similar data to dynamically discover each other, communicate, and negotiate about the training terms (e.g., accuracy, communication latency, and cost). Towards bridging this gap, we propose ART4FL, an end-to-end framework that enables FL in open IoT settings. The framework enables systems’ users to configure agents that participate in FL on their behalf. Those agents negotiate and make commitments (i.e., contractual agreements) to dynamically form federations. To perform FL, the framework deploys the needed services dynamically, monitors the training rounds, and calculates agents’ trust scores based on the established commitments. ART4FL exploits a blockchain network to maintain the trust scores, and it provides those scores to negotiating agents’ during the federations’ formation phase. © 2023 IEEE.

  • 3.
    Alkhabbas, Fahed
    et al.
    Malmö University, Malmo, Sweden; Malmö University, Malmo, Sweden.
    Alsadi, Mohammed
    Norwegian University Of Science And Technology, Trondheim, Norway.
    Alawadi, Sadi
    Halmstad University, School of Information Technology. Uppsala University, Uppsala, Sweden.
    Awaysheh, Feras M.
    University Of Tartu, Tartu, Estonia.
    Kebande, Victor R.
    Blekinge Institute Of Technology, Karlskrona, Sweden.
    Moghaddam, Mahyar T.
    University Of Southern Denmark, Odense, Denmark.
    ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 18, article id 6842Article in journal (Refereed)
    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. © 2022 by the authors.

  • 4.
    Alkharabsheh, Khalid
    et al.
    Al-balqa Applied University, Al Salt, Jordan.
    Alawadi, Sadi
    Halmstad University, School of Information Technology. Uppsala University, Uppsala, Sweden.
    Ignaim, Karam
    Al-balqa Applied University, Al Salt, Jordan.
    Zanoon, Nabeel
    Al-balqa Applied University, Al Salt, Jordan.
    Crespo, Yania
    Universidad De Valladolid, Valladolid, Spain.
    Manso, Esperanza
    Al-balqa Applied University, Al Salt, Jordan; Universidad De Valladolid, Valladolid, Spain.
    Taboada, José A.
    University Of Santiago De Compostela, Santiago de Compostela, Spain.
    Prioritization of god class design smell: A multi-criteria based approach2022In: Journal of King Saud University - Computer and Information Sciences, ISSN 1319-1578, Vol. 34, no 10, Part B, p. 9332-9342Article in journal (Refereed)
    Abstract [en]

    Context: Design smell Prioritization is a significant activity that tunes the process of software quality enhancement and raises its life cycle. Objective: A multi-criteria merge strategy for Design Smell prioritization is described. The strategy is exemplified with the case of God Class Design Smell. Method: An empirical adjustment of the strategy is performed using a dataset of 24 open source projects. Empirical evaluation was conducted in order to check how is the top ranked God Classes obtained by the proposed technique compared against the top ranked God class according to the opinion of developers involved in each of the projects in the dataset. Results: Results of the evaluation show the strategy should be improved. Analysis of the differences between projects where respondents answer correlates with the strategy and those projects where there is no correlation should be done. © 2022 The Author(s)

  • 5.
    Awaysheh, Feras M.
    et al.
    University of Tartu, Tartu, Estonia.
    Alawadi, Sadi
    Halmstad University, School of Information Technology.
    Alzubi, Sawsan
    Independent Researcher, Tartu, Estonia.
    FLIoDT: A Federated Learning Architecture from Privacy by Design to Privacy by Default over IoT2022In: 2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC): Paris, France, 12-15 December 2022 / [ed] Saleh I., Ghedira C., Jararweh Y., Benkhelifa E., Boubchir L., Piscataway, N.J.: IEEE, 2022, p. 1-6Conference paper (Refereed)
    Abstract [en]

    The Internet of Things (IoT) realized exponential growth of smart devices with decent capabilities, promising an era of Edge Intelligence. This paradigm creates a timely need to shift many computations closer to the data source at the network's edge. Data privacy is paramount, as security breaches can severely impact such an environment with its vast attack surface. The advent of Federated learning (FL), a privacy-by-design with decentralized machine learning (ML), enables participants to collaboratively train a model without sharing their sensitive data. Nevertheless, privacy implications are a glaring concern and perrier for widening the utilization of FL approaches and their mass adoption over IoT applications. This paper introduces the notion of FL over the Internet of Disconnected Things (FLIoDT), a functionality separation of concerns following the air-gapped networks. FLIoDT provides a practical methodology to mitigate Data threats/attacks in the FL domain. FLIoDT proves a practical architectural approach to mitigate several attacks in an Edge environment. Data dredging and adversarial attacks, like data poisoning, to name some. This study investigates human activity recognition of health monitoring patient data over edge computing to validate FLIoDT. © 2022 IEEE.

  • 6.
    Galozy, Alexander
    et al.
    Halmstad University, School of Information Technology.
    Alawadi, Sadi
    Halmstad University, School of Information Technology.
    Kebande, Victor
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology.
    Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit StudyManuscript (preprint) (Other academic)
    Abstract [en]

    This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on privacy-preserving machine learning and underscores the need for tailored privacy techniques that address specific attack patterns rather than relying on one-size-fits-all solutions. We use the latent bandit setting to evaluate the trade-off between privacy and recommender performance by employing various aggregation strategies, such as averaging, nearest neighbor, and clustering combined with noise injection. More specifically, we simulate a linkage attack scenario leveraging publicly available auxiliary information acquired by the adversary. Our results on three open real-world datasets reveal that adding noise using the Laplace mechanism to an individual user's data record is a poor choice. It provides the highest regret for any noise level, relative to de-anonymization probability and the ADS metric. Instead, one should combine noise with appropriate aggregation strategies. For example, using averages from clusters of different sizes provides flexibility not achievable by varying the amount of noise alone. Generally, no single aggregation strategy can consistently achieve the optimum regret for a given desired level of privacy.

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