The Internet-of-Things (IoT) environment has revolutionized the quality of living standards by enabling seamless connectivity and automation. However, the widespread adoption of IoT has also brought forth significant security challenges for manufacturers and consumers alike. Detecting network intrusions in IoT networks using machine learning techniques shows promising potential. However, selecting an appropriate machine learning algorithm for intrusion detection poses a considerable challenge. Improper algorithm selection can lead to reduced detection accuracy, increased risk of network infection, and compromised network security. This article provides a comparative evaluation to six state-of-the-art boosting-based algorithms for detecting intrusions in IoT. The methodology overview involves benchmarking the performance of the selected boosting-based algorithms in multi-class classification. The evaluation includes a comprehensive classification performance analysis includes accuracy, precision, detection rate, F1 score, as well as a temporal performance analysis includes training and testing times.

Botnets are a major security threat in the Internet of Things (IoT), posing significant risks to user privacy, network availability, and the integrity of IoT devices. With the increasing availability of large datasets that contain hundreds or even thousands of variables, selecting the right set of features can be a challenging task. Feature selection is a critical step in developing effective machine learning-based botnet detection systems, as it enables the selection of a subset of features that are most relevant for detection. This paper provides a comprehensive review of filtering based feature selection techniques for botnet detection in IoT. It examines a range of filtering based techniques and evaluates their effectiveness in addressing the challenges and limitations of botnet detection in IoT. It aims to identify the gaps in the literature and areas for future research, and discuss the broader implications of findings for the field of IoT botnet detection. This review provides valuable insights and guidance for researchers and practitioners working on botnet detection in IoT, and highlights the importance of effective feature selection in developing robust and reliable detection systems.

The rapid expansion of Internet of Things (IoT) adoption has brought about significant cybersecurity challenges, with botnet attacks being a critical concern. To address this issue, machine learning algorithms, particularly boosting-based approaches, have shown promise in detecting and mitigating botnet intrusions. However, the selection of an appropriate algorithm plays a crucial role in achieving accurate detection and reducing the probability of infection. This article focuses on the utilization of boosting-based algorithms for botnet detection in IoT environments. It evaluates the performance of five boosting-based machine learning algorithms in botnet binary detection. The empirical findings underscored the significant potential of boosting-based algorithms in effectively detecting botnet attacks within IoT environments. The histogram gradient boosting algorithm achieved the best performance for binary detection with an accuracy rate of 0.999977. In addition, a temporal evaluation is presented to evaluate the computational requirements of each algorithm to cope with the resources constrained nature of IoT.

Nowadays, image–text matching (retrieval) has frequently attracted attention due to the growth of multimodal data. This task returns the relevant images to a textual query or descriptions that describe a visual scene and vice versa. The core challenge is how to precisely determine the similarity computation between the text and image, which requires understanding the different modalities by extracting the related information accurately. Although many approaches are established for matching textual data and visual content utilizing deep learning (DL) approaches, a few reviews of the studies of image–text matching are obtainable using DL. In this review study, we contribute to present and clarify the modern techniques based on DL in the image–text matching problem by providing an extensive study of the existing matching models, different current architectures, benchmark datasets, and evaluation methods. First, we explain the matching task and illustrate frequently used architecture. Second, we classify present approaches according to two important concepts the alignment between image and text, and the learning approach. Third, we report standard datasets and evaluation techniques. Finally, we show up current challenges to serve as an inspiration to new researchers in this field.

Digital Terrain Modeling (DTM) is widely used in various applications such as terrain analysis, hydrological modeling, and urban planning. However, the accuracy of DTM heavily relies on the quality of input data and the algorithms used to process the data. Errors in DTM can have significant impacts on downstream applications, such as flood forecasting and land-use planning. Error assessment and propagation in DTM is the process of identifying and quantifying errors in the DTM and evaluating how these errors propagate through downstream applications. This paper provides an overview of error assessment and propagation in DTM, including sources of errors, methods of error assessment, and techniques for error propagation analysis. The paper also discusses the importance of error assessment and propagation in ensuring the accuracy and reliability of DTM and downstream applications.