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This paper provides an extensive and complete survey on the process of detecting and preventing various types of IoT-based security attacks. It is designed for software developers, researchers, and practitioners in the Internet of Things field who aim to understand the process of detecting and preventing these attacks. For each entry identified from the list, a brief description is provided along with references where more information can be found. However, We surveyed the current state-of-the-art IoT security solutions and focused on four main aspects: (1) handpicking representative attacks, (2) identifying potential solutions, (3) performing a threat analysis for each attack and solution, and (4) ranking solutions according to the threats they overcome. By adopting this framework, we identified five main categories of defense mechanisms: distributed denial of service detection/prevention, default password protection, encryption mechanisms, intrusion detection/prevention, and anomaly detection. These solutions are relatively mature in terms of utility and usability. However, the security analysis is conducted only concerning specific attacks, which may or may not be relevant to real-world deployment. Appropriate IoT security solutions should incorporate threat modeling while considering other factors such as resource consumption and implementation effort. Overall, evaluation of IoT security solutions is arduous due to the complexity of IoT OSes, heterogeneous IoT devices (e.g., various hardware platforms), limited availability of open-source codebases, and restrictive policies towards intellectual property disclosure. In addition, we note that there remains a lack of studies that perform a systematic evaluation of the state-of-the-art in terms of both frameworks/methodologies and mechanisms proposed.

The architecture employed by most of the researchers for the deployment of latency-sensitive Internet of Things (IoT) applications is fog computing. Fog computing architecture offers less delay as compared to the cloud computing paradigm by providing resource constraint fog devices close to the edge of the network. Fog nodes process the incoming data by utilizing available resources which reduces the volume of data to be sent to the cloud server. Fog devices having dissimilar processing capabilities are present in a system. The connection of suitable sensor nodes to the parent fog node plays an essential role in achieving the optimum performance of the system. In this paper, we have designed an algorithm that dynamically assigns appropriate sensor devices to fog nodes to achieve a reduction in network utilization and latency. The proposed algorithm estimates the volume of information detected by an edge device from the rate of sensing frequency of the sensor attached to the edge device. The proposed policy while connecting the network nodes takes into account the heterogeneity and processing capability of the devices. Several evaluations are performed on multiple scales for the evaluation of the proposed algorithm. The outcomes of the evaluations confirm the effectiveness of the proposed algorithm in achieving a reduction in network consumption and end-to-end delay.

A Brain Computer Interface (BCI) is a device for establishing direct communication between the user's brain and the machine. It can control devices such as exoskeletons, wheel chairs and prostheses using brain signals. One of the main challenges in this area is to successfully extract robust and sufficient control signals from very limited and noisy information sources to enable program control procedures. In this paper, we proposed a method based on the combination of five CNN models to improve the classification performance of motor imagery. The results of the comparison on the BCI Competition IV 2adataset prove that our proposed method gave better performance by presenting the highest accuracy value compared to the methods of the state of the art, which is equal to 64.17%.

Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models.

In many locations, reverse transcription polymerase chain reaction (RT-PCR) tests are used to identify COVID-19. It could take more than 48 h. It is a key factor in its seriousness and quick spread. Images from chest X-rays are utilized to diagnose COVID-19. Which generally deals with the issue of imbalanced classification. The purpose of this paper is to improve CNN’s capacity to display Chest X-ray pictures when there is a class imbalance. CNN Training has come to an end while chastening the classes for using more examples. Additionally, the training data set uses data augmentation. The achievement of the suggested method is assessed on an image’s two data sets of chest X-rays. The suggested model’s efficiency was analyzed using criteria like accuracy, specificity, sensitivity, and F1 score. The suggested method attained an accuracy of 94% worst, 97% average, and 100% best cases, respectively, and an F1-score of 96% worst, 98% average and 100% best cases, respectively.