Internet of Things (IoT) plays an essential contribution in connecting devices and enabling seamless data exchange, leading to increased efficiency and convenience. However, security concerns in IoT systems are significant, as compromised devices can lead to data breaches and privacy violations. Blockchain technology can enhance IoT security by providing decentralized consensus, immutability, and transparent transaction records, ensuring secure and trustworthy communication and data integrity. This review article gives a succinct but thorough understanding of blockchain technology, covering architecture of blockchain, working principles, types, applications, platforms, and its role in the IoT environment. The study highlights potential benefits of blockchain like enhanced security and privacy, and explores its integration with IoT. Additionally, the study discusses various real-world applications, examines blockchain platforms, and addresses the limitations and challenges associated with blockchain technology. This review serves as a valuable resource for researchers and practitioners seeking a deeper understanding of blockchain’s potential and its implications in the IoT landscape.

In recent years, the proliferation of fake accounts on social media has become a significant concern for individuals, organizations, and society. Fake accounts play an important role in spreading fake news, rumors, spam, unethical harassment, and other mischievous motives on social media platforms. Detecting such accounts manually is time-consuming and challenging, especially with the increasing sophistication of the methods used to create them. Therefore, there is a need for automated approaches to detect these fake accounts. This article aims to reveal fake accounts and their role on social media platforms. This article has explored various methodologies to detect fake accounts from social platforms, which will help prevent cybercrime. We have also proposed a generalized deep learning (DL) model to detect such accounts on social media using multimodal data. The proposed architecture uses a combination of textual, visual, and network-based features to capture the various characteristics of fake accounts. Specifically, we use a deep neural network that combines convolutional neural networks (CNNs) for visual data, long short-term memory (LSTM) networks for textual data, and convolutional graph networks (GCNs) for network-based data. We evaluated our model on a publicly available dataset of Twitter accounts and achieved state-of-the-art performance in detecting fake accounts, with an $F$ 1 score of 0.96. We also conducted experiments to show the effectiveness of each feature and the combination of the three features.

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.

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) has revolutionized our daily lives, impacting everything from healthcare to transportation and even home automation and industrial control systems. However, as the number of connected devices continues to rise, so do the security risks. In this review, we explore the different types of attacks that target various layers of IoT infrastructure. To counter these threats, researchers have proposed using machine learning (ML) and deep learning (DL) techniques for detecting different types of attacks. However, our examination of existing literature reveals that the effectiveness of these techniques can vary greatly depending on factors like the dataset used, the features considered, and the evaluation methods employed. Finally, we delve into the current challenges facing Intrusion Detection Systems (IDS) in their mission to protect IoT environments from evolving threats.

Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and remotely accessible smart devices, more and more cybersecurity issues are being witnessed among cyber-physical systems. A perfect intrusion detection system (IDS) can probably identify various cybersecurity issues and their sources. In this article, using various telemetry datasets of different Internet of Things scenarios, we exhibit that external users can access the IoT devices and infer the victim user’s activity by sniffing the network traffic. Further, the article presents the performance of various bagging and boosting ensemble decision tree techniques of machine learning in the design of an efficient IDS. Most of the previous IDSs just focused on good accuracy and ignored the execution speed that must be improved to optimize the performance of an ID model. Most of the earlier pieces of research focused on binary classification. This study attempts to evaluate the performance of various ensemble machine learning multiclass classification algorithms by deploying on openly available “TON-IoT” datasets of IoT and Industrial IoT (IIoT) sensors.