The growth of cloud computing is hindered by concerns about privacy and security. Despite the widespread use of network intrusion detection systems (NIDS), the issue of false positives remains prevalent. Furthermore, few studies have approached the intrusion detection problem as a time series issue, requiring time series modeling. In this study, we propose a novel technique for the early detection of intrusions in cloud computing using time series data. Our approach involves a method for Feature Selection (FS) and a prediction model based on the Facebook Prophet model to assess its efficiency. The FS method we propose is a collaborative feature selection model that integrates time series analysis techniques with anomaly detection, stationary, and causality tests. This approach specifically addresses the challenge of misleading connections between time series anomalies and attacks. Our results demonstrate a significant reduction in predictors employed in our prediction model, from 70 to 10 predictors, while improving performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Median Absolute Percentage Error (MdAPE), and Dynamic Time Warping (DTW). Furthermore, our approach has resulted in reduced training, prediction, and cross-validation times of approximately 85%, 15%, and 97%, respectively. Although memory consumption remains similar, the utilization time has been significantly reduced, resulting in substantial resource usage reduction. Overall, our study presents a comprehensive methodology for effective early detection of intrusions in cloud computing based on time series anomalies, employing a collaborative feature selection model and the Facebook Prophet prediction model. Our findings highlight the efficiency and performance improvements achieved through our approach, contributing to the advancement of intrusion detection techniques in the context of cloud computing security.

In the rapidly evolving landscape of the Internet of Things (IoT), cybersecurity remains a critical challenge due to the diverse and complex nature of network traffic and the increasing sophistication of cyber threats. This study investigates the application of the Artificial Bee Colony (ABC) algorithm for hyperparameter optimization (HPO) in machine learning classifiers, specifically focusing on Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) for IoT network traffic analysis and malware detection. Initially, the basic machine learning models demonstrated accuracies ranging from 69.68% to 99.07%, reflecting their limitations in fully adapting to the varied IoT environments. Through the employment of the ABC algorithm for HPO, significant improvements were achieved, with optimized classifiers reaching up to 100% accuracy, precision, recall, and F1-scores in both training and testing stages. These results highlight the profound impact of HPO in refining model decision boundaries, reducing overfitting, and enhancing generalization capabilities, thereby contributing to the development of more robust and adaptive security frameworks for IoT environments. This study further demonstrates the ABC algorithm’s generalizability across different IoT networks and threats, positioning it as a valuable tool for advancing cybersecurity in increasingly complex IoT ecosystems.

The technological revolutions greatly impact current and future Classrooms. These advances in technology include the revolution of artificial intelligence, virtual reality, and super highspeed internet. In the coming next generation (6G), the data rate will be very sufficient for live scenes in virtual reality applications such as telepresence and teleoperation. This paper review and discuss next-generation technologies in AI, VR, and communication. Moreover, we examine the motivation for establishing an Advanced Technology based Smart and Immersive Classroom (SIC) and the advantages of its availability to the virtual society. Recent advances in computer and communications technology have delivered capabilities to tomorrow’s SIC. Advances in virtual reality and real-time streaming on the internet have created a revolution in curricula and classrooms. Index Terms—Virtual Reality in education; 6G for education, Artificial Intelligence in education, Immersive Classroom