Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Regarding all performance metrics, the proposed approach outperforms both conventional and cutting-edge methods.

The Internet of Things (IoT) is being prominently used in smart cities and a wide range of applications in society. The benefits of IoT are evident, but cyber terrorism and security concerns inhibit many organizations and users from deploying it. Cyber-physical systems that are IoT-enabled might be difficult to secure since security solutions designed for general information/operational technology systems may not work as well in an environment. Thus, deep learning (DL) can assist as a powerful tool for building IoT-enabled cyber-physical systems with automatic anomaly detection. In this paper, two distinct DL models have been employed i.e., Deep Belief Network (DBN) and Convolutional Neural Network (CNN), considered hybrid classifiers, to create a framework for detecting attacks in IoT-enabled cyber-physical systems. However, DL models need to be trained in such a way that will increase their classification accuracy. Therefore, this paper also aims to present a new hybrid optimization algorithm called “Seagull Adapted Elephant Herding Optimization” (SAEHO) to tune the weights of the hybrid classifier. The “Hybrid Classifier + SAEHO” framework takes the feature extracted dataset as an input and classifies the network as either attack or benign. Using sensitivity, precision, accuracy, and specificity, two datasets were compared. In every performance metric, the proposed framework outperforms conventional methods.

With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s vital point, which gathers data from the network and categorizes it as “Attack” or “Normal”. Nevertheless, these schemes were unsuccessful in achieving noteworthy results due to the diverse necessities of IoT devices such as distribution, scalability, lower latency, and resource limits. The present paper proposes a hybrid model for the detection of attacks in an IoT environment that involves three stages. Initially, the higher-order statistical features (kurtosis, variance, moments), mutual information (MI), symmetric uncertainty, information gain ratio (IGR), and relief-based features are extracted. Then, detection takes place using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM) to recognize the existence of network attacks. For improving the classification accuracy, the weights of Bi-LSTM are optimally tuned via a self-upgraded Cat and Mouse Optimizer (SU-CMO). The improvement of the employed scheme is established concerning a variety of metrics using two distinct datasets which comprise classification accuracy, and index, f-measure and MCC. In terms of all performance measures, the proposed model outperforms both traditional and state-of-the-art techniques.

The impact of computing on society is intricate and far-reaching both physically and virtually. Computers have led to an increase in productivity since they have made a wide variety of operations simpler and more efficient. Specifically, Internet of Things has made maintaining infrastructure faster, cheaper, and greener. When it comes to the relevance of the virtual world, technology has completely altered the way we engage with one another. Communication has been streamlined and quickened thanks to the internet and social media platforms. But along with the rising use of technology, new societal challenges are being witnessed including security, freedom, and privacy. Preserving these values is crucial for users, and machine learning may play a key role in mitigating these risks and challenges. This paper attempts to provide a machine learning strategy for safeguarding users' personal and sensitive data against intrusion, fraud, malware, spam, and fake profile, etc. Furthermore, the paper also presents the fruitful and encouraging results of various experiments based on proposed machine learning strategies while utilizing two separate datasets.

Metaverse is making education more interesting and interactive by making learning spaces that are more like real life. Metaverse education is different from traditional e-learning since it uses AI, IoT, and augmented and virtual reality (AR/VR) to make classrooms which are more welcoming, interesting, and available to all students. This chapter looks at how these technologies can work together and change how teachers teach and students learn. The chapter is about human-centered design that means making learning systems flexible enough to meet the needs of each student, make it easier for students with disabilities to learn, and give everyone the same chances to learn, no matter where they live. IoT devices, AI-driven personalization, and immersive simulations can enhance the learning experience and student outcomes, as demonstrated by real-world use cases like smart classrooms and AR-based campus navigation. This chapter offers a road map for developing an inclusive and democratized metaverse for education by combining case studies, current trends, and future directions.