This paper presents a Dynamic Access Control Framework for the Internet of Things (DACIoT). The main objective of DACIoT is to prevent unauthorized access to IoT devices and tightens the authorized access while an IoT device is in use. The rigidness of existing access control techniques in terms of manual policy specification, discontinuity of access decision-making and immutability to changing access behaviors makes these solutions fall short in highly dynamic IoT environments. DACIoT supports three functionalities that are lacking in existing access control solutions: (1) Automatic policy generation, (2) Continuous policy enforcement, and (3)adaptive policy adjustment. The DACIoT extends the standard reference model of the eXtensible Access Control Markup Language (XACML) with the added three functionalities to improve the adaptability of attribute-based access control policies to highly dynamic IoT environments. Results show that DACIoT provides improved security, dynamic adaptability and can scale efficiently to IoT environments.

The rapid spread of fake news in the digital age poses significant challenges, necessitating effective detection methods. This study presents a comprehensive evaluation of various ensemble and machine learning classifiers, combined with different feature selection techniques, to improve the accuracy and reliability of the detection of fake news. Using the TruthSeeker dataset, this research examines feature selection methods such as Recursive Feature Elimination (RFE), SelectKBest, Principal Component Analysis (PCA) and Genetic Algorithms (GA), analyzing their impact on model performance. Key metrics such as accuracy, precision, recall, F1 score, and AUC-ROC were used to assess the effectiveness of each classifier. The results reveal that ensemble methods, particularly Random Forest (RF) and Gradient Boosting, demonstrate superior performance, achieving high accuracy and AUC-ROC scores. Moreover, feature selection techniques like RFE and SelectKBest significantly improve model outcomes by optimizing the feature set, while PCA is less effective in this context. This study highlights the importance of integrating robust classifiers with optimal feature selection methods to improve the efficacy of fake news detection systems.

Task offloading in vehicular edge networks must satisfy strict latency limits. At the same time, it must resist security threats such as replay attacks. Existing offloading models treat cryptographic settings as fixed, separate from the scheduling decision. This is a real gap, and it exists in part because jointly choosing the best node and the best cryptographic curve for each task is computationally hard. We prove that this joint problem reduces to a generalized assignment problem, a well-known NP-hard problem, once curve-selection variables are fixed. Because of this hardness, we design a polynomial-time greedy heuristic as a practical approximation. Our model treats Elliptic Curve Cryptography (ECC) curve selection as an adaptive decision, and jointly optimizes it together with latency, CPU load, and bandwidth, using one unified scoring function. Results: Task success rate (TSR) with dynamic ECC is within 0.3 percentage points of latency-only scheduling (85.27% vs. 85.58%), while replay success falls from 100% to 8.93%, more than a 10-fold reduction. A single fixed curve that never refreshes (static ECC) performs far worse on both counts (40.29% TSR, 84.54% replay success), since it cannot serve tasks needing stronger security and accumulates staleness without bound. Measured cryptographic overhead is 0.34–1.19 ms per task depending on curve, and the heuristic runs within roughly 2.34% of an exact ILP bound at full scale. These results, from an openly available Python simulation, confirm real-time feasibility for IoT-enabled vehicular deployments.

Task offloading in vehicular edge networks must satisfy strict latency limits. At the same time, it must resist security threats such as replay attacks. Existing offloading models treat cryptographic settings as fixed, separate from the scheduling decision. This is a gap, and it exists in part because jointly choosing the best node and the best cryptographic curve for each task is computationally hard. We prove that this joint problem reduces to a generalized assignment problem, a well-known NP-hard problem, once curve-selection variables are fixed. Because of this hardness, we design a polynomial-time greedy heuristic as a practical approximation. Our model treats Elliptic Curve Cryptography (ECC) curve selection as an adaptive decision, and jointly optimizes it together with latency, CPU load, and bandwidth, using one unified scoring function. Results: Task success rate (TSR) with dynamic ECC is within 0.3 percentage points of latency-only scheduling (85.26% vs. 85.58%), while replay success falls from 100% to 8.93%, more than a 10-fold reduction. A single fixed curve that never refreshes (static ECC) performs far worse on both counts (40.29% TSR, 84.54% replay success), since it cannot serve tasks needing stronger security and accumulates staleness without bound. Measured cryptographic overhead is 0.35–1.54 ms per task depending on curve, and the heuristic runs within roughly 2.34% of an exact ILP bound at full scale. These results, from an openly available Python (version 3.9 or later) simulation, confirm real-time feasibility for IoT-enabled vehicular deployments.