The Internet of Things (IoT) has dramatically changed human context with the environment, ensuring productivity, comfort, and quality of life through a variety of services and applications. Nevertheless, the rapid growth of IoT devices has introduced significant security concerns like device vulnerabilities, unauthorized access, and potential data breaches.This article deals with an immediate call to empower IoT resilience against a wide range of sophisticated and prevalent cybersecurity threats. We developed two novel hybrid deep learning mechanisms, CNN-GRU (Convolutional Gated Recurrent Neural Networks) and CNN-LSTM (Convolutional Long Short-Term Memory Neural Networks), and extensively evaluated their performance on the state-of-the-art Kitsune and TON-IoT publicly available datasets. These benchmark datasets contain a variety of multivariate IoT attacks. The aim is to demonstrate the robustness of the proposed algorithms in effectively identifying telnet, password, distributed denial of service (DDoS), injection, and backdoor vulnerabilities in IoT ecosystems. We achieved approximately 99.6% accuracy in correctly distinguishing between malevolent and non-malicious activities on the Kitsune dataset. Additionally, the TON-IoT dataset demonstrated a remarkable accuracy rate of 99.00%, with minimal drops and low false alert rates. The time efficiency of both proposed algorithms renders them well-suited for deployment in IoT ecosystems. We evaluated and cross validated the proposed techniques with current benchmarks. Consequently, the proposed hybrid deep learning anomaly detection approaches not only enhance IoT security but also provide a robust control system for addressing emerging multivariate cyber threats.

Cloud-based technology, which is now well established, helps in reducing costs and providing accessibility, reliability and flexibility. However, the Yemen higher educational institutions (HEIs) have not yet embraced the technology due to security and privacy concerns, lack of trust, negative cultural attitudes (i.e., tribalism), and most importantly, lack of digital devices experience in educational settings as well as lack of knowledge and technical know-how. Thus, this study proposes a conceptual model of cloud computing adoption in Yemen HEIs by investigating the influence of technology, organization and environment (TOE) factors. In addition, this study investigates the moderating effect of tribalism culture in the relationships between the identified factors and cloud computing adoption. The study employed the quantitative approach to determine the factors that influence cloud computing adoption in Yemen HEIs through a questionnaire survey. Data were collected from 328 respondents in 38 HEIs and analyzed using partial least square (PLS) structural equation modelling (SEM). The results indicate that relative advantage, reliability, compatibility, security, technology readiness, top management support, regulatory policy and competitive pressure have positive significant impacts on the cloud computing adoption, except tribalism culture with negative significant impact. The study also found that tribalism culture moderates the relationship between compatibility, reliability, security, relative advantage, regulatory policy and cloud computing adoption. This study contributes to the TOE adoption model by including the cultural factor as a moderator towards cloud computing adoption in Yemen HEIs. The study also provides a model and insights for HEIs, technology consultants, vendors and policy makers in better understanding of the factors that influence cloud computing adoption in least developed countries (LDCs), specifically, Yemen.

The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications.

A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.

One of the challenges in image processing involves single-image deraining (SID). Existing methods do not exploit images at multi-scales and often overlook spatial and channel information. These methods fail to account for different rain conditions, leading to challenges in effectively removing a wide range of rain streak patterns, such as diverse directional or dense streaks. Moreover, they often result in the loss of fine texture details or a blurred background in the process of eliminating rain streaks from images captured in heavy rain. Mostly state-of-the-art (SOTA) deraining models achieve higher performance in removing rain from rainy images but at the expense of a high number of parameters, which results in computational complexity and memory requirements. Furthermore, they also do not consider high-level visioned evaluation metrics to perform deep evaluations of the proposed models. In this paper, a simple lightweight network with few parameters and relatively shallow depth is proposed by fusing the traditional Gaussian-Laplacian pyramid technique with the attention module. We propose a novel attention-based lightweight pyramid network for image deraining and detection (LPN-IDD) to achieve better deraining performance. The proposed model includes a dual attention module integrated with the Gaussian-Laplacian pyramids network. In LPN-IDD, different levels of laplacian pyramids can extract multi-scale features to adapt to different shapes and types of rain streaks. Residual and recursive blocks are used in each subnetwork along with dual attention blocks to resist the occlusion or texture feature while suppressing unnecessary features. Extensive experimentation performed on the SID synthetic and real-world datasets demonstrate the effectiveness of the proposed model in image deraining tasks. For deep evaluation of the proposed methodology, object detection models including faster-RCNN, YOLO-V3, YOLO-V7 are used along with full reference evaluation metrics.

Crime Pattern Theory is a way of elucidating the reasons why specific types of crime happen at certain areas only. According to the theory, the offenders, rather than venturing into unknown territories, frequently commit crimes by taking advantage of the opportunities they encounter in the places of their comfort zones or the places they are most familiar with. Spatial analysis of crimes and areas of higher concentration assist in preventing or at least reducing the amount of future crime from the knowledge discovery of past data. Our approach of crime prediction consists of five modules, namely data extraction, pre-processing, classification, pattern identification, prediction and google map visualization. This paper compares machine learning algorithms namely naive bayes, bayesian networks, weighted k-nearest neighbors, multi-layer perceptron classifier, guassian naive bayes, decision tree, random forest, adaboost, gradient boosting, linear discriminant analysis and quadratic discriminant analysis for crime category identification and crime prediction. The results have been evaluated on a real dataset of crimes in the city of San Francisco. The work successfully uses the temporal and spatial information in the data to locate the crime hotspots, predict the category of a crime in a region and additionally, the emergence of the crimes.