Cybercriminals increasingly exploit psychological vulnerabilities and software design weaknesses to deceive users across trusted digital platforms. This study explores patterns among self-reported stress, impulsivity, online behaviors, and phishing-identification performance. Young adults frequently experience emotionally demanding circumstances, including job searches, financial deadlines, and academic stress, that may increase susceptibility to deceptive communications. The study also compares phishing-identification accuracy and confidence among participants who received no warning, a generic warning, or an inoculation-style warning. Grounded in the Heuristic-Systematic Model (HSM) and Cyber-Routine Activity Theory (Cyber-RAT), the study examines how emotional states and routine online behaviors may relate to decision-making during simulated phishing encounters. Participants completed a pre-survey assessing emotional state and online behavior prior to phishing exposure. They were then randomly assigned to receive either no warning, a generic security tip, or a targeted inoculation message designed to reduce susceptibility to phishing. Participants then completed a simulated phishing task using emotionally salient emails (e.g., fake job offers or tuition warnings) to assess trust judgments and response patterns. Participants in the inoculation-warning group showed the highest average accuracy and confidence, while findings involving stress, impulsivity, and online behaviors remained exploratory. By integrating emotional psychology, digital behavior, and preventive intervention strategies, this study provides insight into the psychological factors underlying susceptibility to cyber-enabled fraud. The findings may inform the development of targeted prevention strategies and real-time awareness tools to reduce vulnerability to online deception among young adults.

Job scheduling is a key challenge in cloud computing due to the large number of resources and services involved. The choice of scheduling strategy strongly affects system performance, and poor decisions can even cause failures. Since testing directly on real cloud systems is impractical, researchers rely on simulations to develop and evaluate scheduling algorithms. However, generating simulation input data manually is time-consuming and can lead to inconsistencies, making fair comparison between algorithms difficult. This study makes three key contributions. First, we introduce a tool that automatically generates simulation data for CloudSim Plus. Second, we use this tool to generate a dataset that is employed to evaluate our proposed algorithm; this dataset is also published publicly to enable other researchers to use it as a benchmark for testing and comparing their algorithms with ours. Finally, we propose a job scheduling algorithm based on a hybrid approach that combines a Genetic Algorithm with Particle Swarm Optimization (PSO). The proposed algorithm was evaluated on both synthetic and real datasets where it demonstrated competitive performance compared to existing approaches in the field.

The introduction of blockchain technology is thought to be among the finest methods for ensuring the security of distributed systems. However, conventional consensus mechanisms like Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT) entail heavy energy, computational, and communication costs. Therefore, the implementation of such consensus mechanisms in resource-constrained environments including the Internet of Things (IoT) and edge computing environments poses certain challenges. In this regard, this paper proposes a novel consensus framework known as Adaptive Trust Aware Consensus Framework (ATCF). It dynamically selects validators based on trustworthiness as opposed to considering only the computational power or stake ownership. It evaluates the participating nodes using different trust metrics, which include success rate of transactions, resource availability, and reputation score. After computation of trust levels, a light validator selection method is employed to choose the nodes with high levels of trustworthiness. In order to prove its efficiency, the framework was evaluated using a Python-based blockchain simulation. From the experimental results, it is noted that ATCF performs significantly better compared to PoW, PoS, and PBFT mechanisms. Particularly, it increased blockchain performance with no security risk involved since it achieved a throughput of 3372.83 transactions per second compared to others. The framework reduced consensus delay to 509.69 ms. Additionally, energy consumption dropped by approximately 86.3%. Most importantly, the framework managed to achieve a malicious node detection rate of nearly 90%. This indicates high resilience to any adversarial activities. Therefore, the proposed trust aware consensus mechanism can easily be integrated in any blockchain technology without compromising performance and security.

Accurate software effort estimation (SEE) remains a challenging task due to missing values, structural inconsistencies, redundant attributes, and the heterogeneous nature of software engineering datasets. Although numerous machine learning models have been developed for effort estimation, comparatively less attention has been given to improving data quality and feature quality before model construction. As a result, estimation models are often trained on datasets containing incomplete, noisy, or redundant information, which can adversely affect prediction accuracy and model generalization. This study proposes an integrated software effort estimation framework that combines a Data Quality Enhancement Module (DQEM) with an Adaptive Multi-Criteria Redundancy-Aware Feature Optimization (AMRFO) framework. DQEM enhances dataset reliability through automated inspection, structural correction, adaptive missing-value reconstruction, uncertainty assessment, and diagnostic validation. The enhanced datasets are subsequently processed using AMRFO, which identifies informative, stable, and non-redundant feature subsets by jointly considering feature importance, feature stability, and feature redundancy. The proposed framework was evaluated on five benchmark software effort estimation datasets: Desharnais, China, ISBSG, COCOMO81, and Maxwell. Experimental evaluation using nine machine learning models and repeated cross-validation showed that AMRFO reduced the feature space by 50.00% to 72.22% across the evaluated datasets while maintaining or improving predictive performance in most cases. The largest improvement was observed for the COCOMO81 dataset, where the Mean Absolute Error (MAE) decreased from 454.66 to 320.09 after feature optimization. Overall, the results demonstrate that integrating data quality enhancement with multi-criteria feature optimization improves estimation reliability, reduces feature redundancy, and supports more accurate and generalizable software effort estimation. The proposed framework provides a practical and scalable solution for machine learning-based effort estimation using heterogeneous software engineering datasets.

Combining vehicle-to-everything (V2X) communication with smart grid technology enables intelligent electric vehicle management, dynamic charging, demand response, and cyber-physical monitoring in real time. Nevertheless, such a complex environment faces privacy leakage, data injection falsification, spoofing, denial of service, poisoning, evasion, and gradient inference attacks. Although existing federated learning-based approaches reduce the amount of raw data that must be shared, they apply fixed differential privacy noise and make assumptions about the uniformity of client reliability. Furthermore, existing frameworks show little resilience to poisoning, evasion, spoofing, data injection, and denial-of-service/DDoS attacks owing to their inability to work properly with non-IID vehicles and electric vehicle data. To solve the abovementioned problems, this study proposes a Trust-Aware Adaptive Differentially Private Hierarchical Federated Adversarial Learning framework, which employs a Convolutional Neural Network-Graph Convolutional Network-Gated Recurrent Unit (CNN-GCN-GRU), for intrusion detection in V2X-enabled smart grids. The architecture uses a three-layer approach with vehicle and electric vehicle clients, edge aggregators that can be either roadside units or charge stations, and a smart grid cloud center. The CNN component extracts local traffic data, the GCN models the topological dependencies between the vehicle and grid nodes, and the GRU component captures attack-related behavior patterns. Adaptive differential privacy takes advantage of the trust scores of different clients to adjust the amount of noise applied, whereas trust-aware aggregation is utilized to lower the contribution from potentially unreliable updates. Finally, adversarial learning is employed to improve resilience to the aforementioned attacks. Non-Independent and Identically Distributed (Non-IID) settings are evaluated using metrics such as accuracy, precision, recall, F1-score, false alarm rate, privacy budget, communication overhead, detection delay, reliability, scalability, and resilience. Based on the experimental results, the TADP-HFAL algorithm obtains an accuracy of 97.20%, an F1-score of 96.67%, a false alarm rate of 2.85%, and reduces communication overhead by 51.59% compared to the traditional flat federated learning approach.

The rapid expansion of the Ethereum network has created serious security threats, which require robust machine-learning-based fraud detection systems. However, these classifiers often struggle with high dimensionality, class imbalance and computational overhead. In this study, we address these problems by a comprehensive evaluation of 10 algorithms for feature selection based on mutual information (MI) based feature selection methods combined with three tree-based ensemble classifiers: Random Forest, XGBoost and LightGBM. We utilized two real-world datasets, the Ether Fraud Detection Dataset (EFDD) and the Labelled Ethereum Transaction Dataset (LETD). This research aims to identify the most optimal combinations to maximize predictive accuracy while minimizing the number of sub-sets of the features. It is observed from empirical results that MI-based feature selection techniques successfully isolate critical transactional patterns and gradient decedent models are superior to conventional methods. Specifically, Kurtosis Based Feature Selection (KBFS) paired with LGBM achieved high Matthews correlation coefficient (MCC) of 0.9641 for EFDD using only 15 features. In LETD, using only 6 features ranked by the Double-Input Symmetric Ratio (DISR) with LGBM yielded 0.9689 MCC. Finally, the optimal selection of MI-based features reduced the computation time while keeping the most up-to-date Ethereum fraud detection.