Federated decentralized IoT systems are reshaping how data is exchanged and processed across domains such as smart cities and healthcare, yet ensuring trust in such dynamic, distributed environments remains a significant challenge. This paper introduces FedTrust, a layered framework that integrates decentralized communication, federated data services, and a self-adaptive trust-risk model to assess the trustworthiness of IoT devices in real time. By extending an established analytical trust model, we refine existing constructs and introduce new dimensions such as context awareness and behavioral monitoring to account for the operational variability of edge devices. Our proposed model computes risk and trust scores using weighted metrics, driving automated decisions and mitigation actions via a closed feedback loop. FedTrust provides a scalable and resilient approach for securing federated IoT ecosystems through continuous, data-driven trust calibration.

Abstract Network intrusion detection systems (NIDS) act as a premier defense to protect computer networks from cybersecurity threats. In Adversarial Machine Learning (AML), adversaries aim to deceive Machine Learning (ML) and Deep Learning (DL) models into producing false predictions with deliberately prepared adversarial samples. These intentionally generated adversarial samples have become a significant vulnerability of ML and DL-based systems, posing major challenges for their adoption in real-world, critical applications such as NIDS. In this study, we aim to present a novel hybrid defense model that enhances the performance of DL-based NIDS against adversarial attacks. The MinMaxScaler is used for data normalization. We employed Independent Component Analysis (ICA) for feature extraction and Recursive Feature Elimination (RFE) for feature selection to reduce complexity and overfitting. The proposed model comprises two defense strategies: Projected Gradient Descent (PGD) with a Pigeon-Inspired Optimization Algorithm (PIOA) during the training phase, aiming to enhance the model's ability to distinguish between adversarial examples. Spatial Smoothing (SS) is employed during the testing phase to decrease the potential impact of adversarial noise and sensitivity to minor feature changes. We have implemented three adversarial attack generation methods: Jacobian Saliency Map Attacks (JSMA), Fast Gradient Sign Method (FGSM), and Carlini and Wagner (C&W), and evaluated them in five distinct scenarios. The proposed model demonstrates an accuracy of 99.65%, a recall of 99.87%, an ASR of 1.29%, and a specificity of 99.05%. We further presented the computational efficiency and a hyperparameter sensitivity analysis to validate and assess the model's real-time processing feasibility. The scope of the presented study extends beyond computer security.

Abstract Context Software Fault Prediction (SFP) leverages supervised Machine Learning to detect faulty software constructs using software metrics and corresponding labels. Despite recent advances in Deep Learning (DL) for tabular data, their application to SFP remains underexplored. Objectives This study proposes a novel feature selection method, CorrBoost, which combines correlation analysis and XGBoost to address feature dimensionality. Additionally, we evaluate existing tabular DL architectures, super convergent deep neural networks (sDNN) and TabNet for SFP. Methods Using 26 public datasets from NASA, PROMISE, and AEEEM repositories, we apply the adaptive synthetic oversampling technique to manage class imbalance. We compare DL models with five state-of-the-art techniques and two gradient-boosted tree models (XGBoost and LightGBM) using AUC-ROC, AUPRC, and Accuracy. Statistical significance is validated using the Bayesian Signed Rank Test and Scott-Knott ESD. Results Gradient-boosted trees and existing state-of-the-art models outperform DL methods in AUC-ROC by 17.9% and 9.6%, respectively. CorrBoost achieves a 55% average reduction in feature dimensionality with negligible performance loss. DL methods, however, incur significantly higher processing time and perform poorly on unseen test data. Conclusion CorrBoost combined with boosted tree models offers a superior trade-off between performance and computation. While tabular DL architectures hold promise, they currently lag behind traditional methods for SFP on real-world data.

Ensuring the confidentiality of transmitting sensitive image data is paramount. Cryptography recreates a critical function in safeguarding information from potential risks and confirming the identity of authorised individuals, thereby addressing the growing demand for enhanced image security. This paper presents a novel AES-permuted Feistel Blowfish (APFB) model that aims to improve image data security cost-effectively and enhance data protection by incorporating AES and Blowfish algorithms. The proposed model's utility over existing approaches stems from its computational efficiency and speed. The proposed model's resilience and security against several attack modalities are validated through a comprehensive range of methods, including extensive experimentation, histogram analysis, PSNR, entropy, MSE, CC, computational time, and NIST statistical tests. The outcomes yielded a PSNR of 67.26, NPCR of 99.6354, and UACI of 33.412. Additionally, the applicability of the proposed model is validated by utilising a practical case analysis. The outcomes exhibit the relevance of the proposed model in real-world applications.

Hospitals and medical facilities are increasingly concerned about network security and patient data privacy as the Internet of Medical Things (IoMT) infrastructures continue to develop. Researchers have studied customized network security frameworks and cyberattack detection tools driven by Artificial Intelligence (AI) to counter different types of attacks, such as spoofing, data alteration, and botnet attacks. However, carrying out routine IoMT services and tasks during an under-attack scenario is challenging. Machine Learning has been extensively suggested for detecting cyberattacks in IoMT and IoT infrastructures. However, the conventional centralized approach in ML cannot effectively detect newly emerging attacks without compromising patient data privacy and network flow data confidentiality. This study discusses a Federated Bayesian Optimization XGBoost framework that employs multimodal sensory signals from patient vital signs and network flow data to detect attack patterns and malicious network traffic in IoMT infrastructure while ensuring data privacy and detecting previously unknown attacks. The proposed model employs a Federated Bayesian Optimisation XGBoost approach, which allows us to search the parameter space quickly and find an optimal solution from each local server while aggregating the model parameters from each local server to the centralised server. The XGBoost algorithm generates a new tree by taking into account the previously estimated value for the tree's input data and then optimizing the prediction gain. This study used a dataset with 44 attributes and 16 318 instances. During the preprocessing phase, 10 features were dropped, and the remaining 34 features were used to evaluate the network flows and biometric data (patient vital signs). The performance evaluation reveals that the proposed model predicts data alteration, malware, and spoofing attacks in patients' vital signs and network flow data with a prediction accuracy of 0.96. The results obtained from the experiment demonstrate that both the centralized and federated models are synchronized, with the latter occasionally being slightly reduced. The findings indicate that the suggested model can be incorporated into the IoMT domain to detect malicious patterns while maintaining data privacy and confidentiality efficiently.

Internet of Things (IoT) devices are becoming increasingly ubiquitous, and their adoption is growing at an exponential rate. However, they are vulnerable to security breaches, and traditional security mechanisms are not enough to protect them. The massive amounts of data generated by IoT devices can be easily manipulated or stolen, posing significant privacy concerns. This paper is to provide a comprehensive overview of the integration of blockchain and IoT technologies and their potential to enhance the security and privacy of IoT systems. The paper examines various security issues and vulnerabilities in IoT and explores how blockchain-based solutions can be used to address them. It provides insights into the various security issues and vulnerabilities in IoT and explores how blockchain can be used to enhance security and privacy. The paper also discusses the potential applications of blockchain-based IoT (B-IoT) systems in various sectors, such as healthcare, transportation, and supply chain management. The paper reveals that the integration of blockchain and IoT has the potential to enhance the security, privacy, and trustworthiness of IoT systems. The multi-layered architecture of B-IoT, consisting of perception, network, data processing, and application layers, provides a comprehensive framework for the integration of blockchain and IoT technologies. The study identifies various security solutions for B-IoT, including smart contracts, decentralized control, immutable data storage, identity and access management (IAM), and consensus mechanisms. The study also discusses the challenges and future research directions in the field of B-IoT.

Context: The application of Software Fault Prediction (SFP) in the software development life cycle to predict the faulty class at the early stage has piqued the interest of various scholars. In the SFP domain, during research analysis, it got realized that there has been very little work instigated on addressing both class imbalance and feature redundancy problems jointly to enhance the performance and prediction accuracy of SFP models. It has been perceived in the literature survey the study of droughts with the comprehensive comparative analysis of different sampling and feature selection strategies together. Objective: This research builds an extensive assessment of distinct combinations of different feature selection and sampling approaches, to effectively overcome the problems of class overlap, class imbalance , and feature redundancy. The objective is to determine the best combination that will produce results with a higher degree of accuracy and an effective SFP model. Method: Considering the above erudition, the study has applied 8 different sampling techniques along with 10 feature selection algorithms against 56 open-source projects. The comparative analysis is performed against 5346 variants of input datasets by applying 8 different classifiers to predict the faulty class. In addition, the research paper presents an intensive assessment and performance of these techniques individually against all the input projects. We have considered accuracy and Area Under the ROC (receiver operating characteristic curve) Curve (AUC) performance metrics to compare the performance of different models developed using the classification algorithm . Result: For each project in the proposed work, we evaluated a total of 792 combinations that were produced using 10 feature selection methods, 1 all metrics dataset, 8 sampling methods, 1 original, unsampled dataset, and 8 classifiers. The empirical result indicates that, against 21 projects out of 54 projects, Synthetic Minority Over Sampling Technique Edited (SMOTEE) with correlation-based feature selection (FS2) combination outperformed with the highest AUC value which is 38.89 % of projects. Additionally, according to experimental results, the highest AUC values were attained by 24.07 % of projects using the SMOTEE, FS2, and RF combination. Conclusion: The results of the statical analysis test reveal that 93.42 % of the combinational pairs of different sampling and feature selection approaches demonstrated a significant variance in the performance of the distinct combinations of sampling and feature selection techniques. The empirical result indicates the performance of the SFP Model is adversely impacted by class imbalance and irrelevance. The outcome indicates for more than 75% of projects, the performance of trained models improved with an AUC value between a range of 0.805 to 0.99 post-application of sampling and feature selection strategies, in comparison without the use of feature selection and sampling techniques.

The emergence of the Internet and the growing development of online platforms (like Facebook and Instagram) opened the way for disseminating information that hasn't been experienced in the history of mankind earlier. Consumers generate and share more information and a massive amount of data than ever with the growing utilization of social media sites, many of which are deceptive with little relevance to reality. A daunting task is the automated classification of a text article as misleading or misinformation. To see the latest news alerts, individuals often utilize e-newspapers, Twitter, Instagram, Youtube, and many more. Fake news created on social media can lead to uncertainty amongst individuals and psychiatric illness. We may detect that news obtained based on machine learning techniques is either true or false. This study proposes a machine learning technique to detect fake news by carrying out filtration on social media data, classifying the preprocessed data using a machine learning algorithm, evaluating the developed system, and evaluating the results.

Software requirement classification is becoming increasingly crucial for the industry to keep up with the demand of growing project sizes.Based on client feedback or demand, software requirement classification is critical in segregating user needs into functional and quality requirements.However, because there are numerous machine learning (ML) and deep-learning (DL) models that require parameter tuning, the use of ML to facilitate decision-making across the software engineering pipeline is not well understood.Five distinct word embedding techniques were applied to the functional and quality software requirements in this study.The imbalanced classes in the dataset are balanced using Synthetic Minority Oversampling technique (SMOTE).Then, to reduce duplicate and unnecessary features, feature selection and dimensionality reduction techniques are used.Dimensionality reduction is accomplished with Principal Component Analysis (PCA), while feature selection is accomplished with the Rank-Sum Test (RST).For binary categorization into functional and non-functional needs, the generated vectors are provided as inputs to eight distinct Deep Learning classifiers.The findings of the research show that using a combination of word embedding and feature selection techniques in conjunction with various classifiers can accurately classify functional and quality software requirements.

Abstract SMS, one of the most popular and fast‐growing GSM value‐added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F‐measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine‐tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state‐of‐the‐art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F‐measure.

Software defect severity level helps to indicate the impact of bugs on the execution of the software and how rapidly these bugs need to be addressed by the team.The working team is regularly analyzing the bugs report and prioritizing the defects.The manual prioritization of these defects based on the experience may be an inaccurate prediction of the severity that will delay in fixing of critical bugs.It is compulsory to automate the process of assigning an appropriate level of severity based on bug report results with an objective to fix critical bugs without any delay.This work aims to develop defect severity level prediction models that have the ability to assign severity level of defects based on bugs report.In this work, seven different word embedding techniques are applied to defect description to represent the word, not just as a number but as a vector in n-dimensional space in order to reduce the number of features.Since the predictive ability of the developed models depends on the vectors extracted from text as they are used as an input to the defect severity level prediction models.Further, three feature selection techniques have been applied to find the right set of relevant vectors.The effectiveness of these word embedding techniques and different sets of vectors are evaluated using eleven different classification techniques with Synthetic Minority Oversampling Technique (SMOTE) to overcome the class imbalance problem.The experimental results show that the word embedding, feature selection techniques and SMOTE have the ability to predict the severity level of the defect in a software.

Abstract Improvement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real‐world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low‐quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour‐based features, which are less sensitive to the deficiencies of low‐quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down‐sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower‐quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.

In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.

Abstract New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.

Abstract Software risk management is a proactive decision‐making practice with processes, methods, and tools for managing risks in a software project. Many existing techniques for software project risk management are textual documentation with varying perspectives that are nonreusable and cannot be shared. In this paper, a life‐cycle approach to ontology‐based risk management framework for software projects is presented. A dataset from literature, domain experts, and practitioners is used. The identified risks are refined by 19 software experts; risks are conceptualized, modeled, and developed using Protégé. The risks are qualitatively analyzed and prioritized, and aversion methods are provided. The framework is adopted in real‐life software projects. Precision recall and F‐measure metrics are used to validate the performance of the extraction tool while performance and perception evaluation are carried out using the performance appraisal form and technology acceptance model, respectively. Mean scores from performance and perception evaluation are compared with evaluation concept scale. Results showed that cost is reduced, high‐quality projects are delivered on time, and software developers found this framework a potent tool needed for their day‐to‐day activities in software development.

Interest in the solar radiation and associated meteorological variables have been growing over the years due to their effect on energy generation, agriculture and food security, Ozone layer and other industrial applications. Hence, forecasting these variables in long-term and short-terms using various fusions of measured weather parameters has become a major research focus in many regions. However, developing and selecting an accurate model for the prediction of solar radiation based on several weather parameters is still a challenging task. In this study, a time series model for predicting and forecasting Solar Radiation and other weather parameters was developed using Long Short-Term Memory (LSTM). A publicly available dataset of Mowo, Osun State, Nigeria containing Longitude and Latitude, Elevation, Maximum Temperature, Minimum Temperature, Precipitation, Wind speed, Relative Humidity, Solar Radiation and Tropospheric Ozone was collected. The LSTM model was trained with diverse compositions like the number of layers, the number of neurons in each layer, training epochs, and optimization algorithms. Results showed the model had 97%-99% correlation coefficient between actual and predicted values and 99.3%-99.9% prediction accuracy on the test datasets. The LSTM model also forecasted each of the trained variables for a ten-year period between 2015 to 2025 accurately.

Today, natural resources are more scarce than ever, so we must make good use of them. To achieve this goal, we can use metaheuristic optimization tools as an alternative to achieve good results in a reasonable amount of time. The present work focuses on the use of adaptive techniques to facilitate the use of this type of tool to obtain good functional parameters. We use a constructive metaheuristic algorithm called Intelligent Water Drops to solve the set covering problem. To demonstrate the efficiency of the proposed method, the obtained results were compared with the standard version using the same initial configuration for both algorithms. Additionally, the Kolmogorov-Smirnov-Lilliefors, Wilcoxon signed-rank and Violin chart tests were applied to statistically validate the results, which showed that metaheuristics with autonomous search have a better behavior than do standard algorithms.

Summary A plethora of approaches exists for the evaluation and selection of open‐source software (OSS) in the literature. However, these approaches are hardly ever used in practice for the following reasons: first, the lack of a situational‐based procedure to define the evaluation criteria for OSS given its varied and dynamic nature; second, the inability of existing evaluation techniques, such as the analytic hierarchy process, to cope well with uncertainty factors, thus producing misleading results that affect the quality of decisions made; and third, a significant number of existing approaches require the prototyping of alternatives being considered in order to facilitate evaluation and decision‐making. This study addresses the aforementioned challenges by evolving a process framework for evaluating and selecting OSS. The proposed framework is validated by applying it to a case study. In addition, expert opinion was elicited via questionnaires from 10 experts, and overall feedback suggests that 80% of them are willing to adopt the approach.

Web Services have been steadily gaining maturity as their adoption in the software industry grew. Accordingly, metric suites for assessing different quality attributes of Web Service artifacts have been proposed recently. Some researchers have particularly focused on assessing service interface descriptions in WSDL, which like any other software artifact have several inherent attributes (e.g., size or complexity) that can be measured. We study the statistical relationships between some recent service interface complexity metrics (Basci & Misra's metrics suite) and the well-known Chidamber & Kemerer's metric suite applied to service implementations, on a data-set of 154 real-world services. First, to prove the ability of Basci & Misra's suite of measuring the complexity attribute in WSDL documents, a theoretical validation of these metrics using Weyuker's properties is presented. Then, after finding high correlation between both metric suites, we show that refactoring service codes prior to generating the WSDL documents might reduce service interface complexity.

ABSTRACT Global software development in software development industry is a new aspect for many software project managers. In this scenario, the allocation of work packages among project participants is not a simple task. This allocation was traditionally determined by availability and competence but this new trend introduces complexity in an already complex process. Given the need to define new models to guide managers in these operations, this paper presents a framework to allocate work packages among project participants. Apart from the introduction of the framework itself, the results of its implementation are presented. These results show a notable output of the implementation in terms of accuracy of task execution to planning, effect introduction and overall satisfaction. Copyright © 2013 John Wiley & Sons, Ltd.

Software inspection is considered a cost-effective quality assurance technique in software process improvement. Although inspections detect the majority of defects in the early stages of the development process, this technique is not a common practice in the software industry, especially in small and medium enterprises. In this paper, we propose a model for the inspection process intended to be applicable and acceptable to both small and medium enterprises and large software organisations. The model was implemented in two organisations: one in a medium-scale company and the other one in a department of a big company where its feasibility and benefits were confirmed. A comparison with recent alternative inspection models has also been performed showing the practicality of the proposal and ease of adoption and cost-effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.

Scrum is the most popular agile methodology in software industry. By using scrum practices, several companies have improved their quality and productivity. This paper presents a practical view inside the Scrum practices, specifically, the team size, team structure and description of roles in Scrum teams are explained. The paper is based on our experiences in multiple projects executed in Scrum Agile methodology. Scrum is most suitable for products with team size of 3-9 members. For larger products, Scrum provides a mechanism called Scrum of Scrums. Scrum of Scrums distribute the large work/project into several teams and to control the quality and speed of each team, regular meetings are organized amongst the representatives of each team. We also present the guidelines for work distribution for the Scrum of Scrum (SoS) teams.