Cyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection system (IDS) capable of identifying various modern and traditional attacks with high accuracy. Most existing machine-learning-based intrusion-detection systems for IoT have been trained using outdated datasets that do not accurately reflect IoT scenarios. Additionally, current research does not adequately address which machine-learning classifiers are most suitable for developing an efficient IDS in IoT environments. In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, and Artificial Neural Networks. Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT–IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. The second-best performer is Naïve Bayes, with an accuracy of 98.8%.

Current deep learning convolutional neural network (DCNN) -based hand gesture detectors with acute precision demand incredibly high-performance computing power. Although DCNN-based detectors are capable of accurate classification, the sheer computing power needed for this form of classification makes it very difficult to run with lower computational power in remote environments. Moreover, classical DCNN architectures have a fixed number of input dimensions, which forces preprocessing, thus making it impractical for real-world applications. In this research, a practical DCNN with an optimized architecture is proposed with DCNN filter/node pruning, and spatial pyramid pooling (SPP) is introduced in order to make the model input dimension-invariant. This compact SPP-DCNN module uses $65\%$ fewer parameters than traditional classifiers and operates almost $3\times$ faster than classical models. Moreover, the new improved proposed algorithm, which decodes gestures or sign language finger-spelling from videos, gave a benchmark highest accuracy with the fastest processing speed. This proposed method paves the way for various practical and applied hand gesture input-based human-computer interaction (HCI) applications.

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.