Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection techniques. The current research article develops a Hybrid Deep Learning to Combat Sophisticated False Data Injection Attacks detection (HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD model majorly recognizes the presence of FDI attacks in the IoT environment. The HDL-FDIAD model exploits the Equilibrium Optimizer-based Feature Selection (EO-FS) technique to select the optimal subset of the features. Moreover, the Long Short Term Memory with Recurrent Neural Network (LSTM-RNN) model is also utilized for the purpose of classification. At last, the Bayesian Optimization (BO) algorithm is employed as a hyperparameter optimizer in this study. To validate the enhanced performance of the HDL-FDIAD model, a wide range of simulations was conducted, and the results were investigated in detail. A comparative study was conducted between the proposed model and the existing models. The outcomes revealed that the proposed HDL-FDIAD model is superior to other models.     

An Access Control Scheme Using Heterogeneous Signcryption for IoT Environments

When the Wireless Sensor Network (WSN) is combined with the Internet of Things (IoT), it can be employed in a wide range of applications, such as agriculture, industry 4.0, health care, smart homes, among others. Accessing the big data generated by these applications in Cloud Servers (CSs), requires higher levels of authenticity and confidentiality during communication conducted through the Internet. Signcryption is one of the most promising approaches nowadays for overcoming such obstacles, due to its combined nature, i.e., signature and encryption. A number of researchers have developed schemes to address issues related to access control in the IoT literature, however, the majority of these schemes are based on homogeneous nature. This will be neither adequate nor practical for heterogeneous IoT environments. In addition, these schemes are based on bilinear pairing and elliptic curve cryptography, which further requires additional processing time and more communication overheads that is inappropriate for real-time communication. Consequently, this paper aims to solve the above-discussed issues, we proposed an access control scheme for IoT environments using heterogeneous signcryption scheme with the efficiency and security hardiness of hyperelliptic curve. Besides the security services such as replay attack prevention, confidentiality, integrity, unforgeability, non-repudiations, and forward secrecy, the proposed scheme has very low computational and communication costs, when it is compared to existing schemes. This is primarily because of hyperelliptic curve lighter nature of key and other parameters. The AVISPA tool is used to simulate the security requirements of our proposed scheme and the results were under two backbends (Constraint Logic-based Attack Searcher (CL-b-AtSER) and On-the-Fly Model Checker (ON-t-FL-MCR)) proved to be SAFE when the presented scheme is coded in HLPSL language. This scheme was proven to be capable of preventing a variety of attacks, including confidentiality, integrity, unforgeability, non-repudiation, forward secrecy, and replay attacks.

     

Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management

COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan. Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset. A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables. The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases. However, age and the human development index had a negative influence on the cases. The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases. During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0.91. Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0.95239) when predicting the number of confirmed COVID-19 cases in an area. However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI). In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected parameters. Therefore, this tool can help authorities in different ecological settings effectively manage COVID-19.     

Design and Simulations of an Energy Harvesting Capable CMOS Pixel for Implantable Retinal Prosthesis

A new pixel is designed with the capability of imaging and energy harvesting for the retinal prosthesis implant in 0.18 µm standard Complementary Metal Oxide Semiconductor technology. The pixel conversion gain and dynamic range, are 2.05 \(\upmu{\text{V}}/{\text{e}}^{ - }\) and 63.2 dB. The power consumption 53.12 pW per pixel while energy harvesting performance is 3.87 nW in 60 klx of illuminance per pixel. These results have been obtained using post layout simulation. In the proposed pixel structure, the high power production capability in energy harvesting mode covers the demanded energy by using all available p-n junction photo generated currents.     

The structural,morphological and thermal properties of grafted pH-sensitive interpenetrating highly porous polymeric composites of sodium alginate/acrylic acid copolymers for controlled delivery of diclofenac potassium

In present investigation new formulations of Sodium Alginate/Acrylic acid hydrogels with high porous structure were synthesized by free radical polymerization technique for the controlled drug delivery of analgesic agent to colon. Many structural parameters like molecular weight between crosslinks (M_c), crosslink density (M_r), volume interaction parameter (v_2,s), Flory Huggins water interaction parameter and diffusion coefficient (Q) were calculated. Water uptake studies was conducted in different USP phosphate buffer solutions. All samples showed higher swelling ratio with increasing pH values because of ionization of carboxylic groups at higher pH values. Porosity and gel fraction of all the samples were calculated. New selected samples were loaded with the model drug (diclofenac potassium).The amount of drug loaded and released was determined and it was found that all the samples showed higher release of drug at higher pH values. Release of diclofenac potassium was found to be dependent on the ratio of sodium alginate/acrylic acid, EGDMA and pH of the medium. Experimental data was fitted to various model equations and corresponding parameters were calculated to study the release mechanism. The Structural, Morphological and Thermal Properties of interpenetrating hydrogels were studied by FTIR, XRD, DSC, and SEM.     

Intronic Polymorphisms in the CDKN2B-AS1 Gene Are Strongly Associated with the Risk of Myocardial Infarction and Coronary Artery Disease in the Saudi Population

Recent genome-wide association studies identified single nucleotide polymorphisms (SNPs) on the chromosome 9p21.3 conferring the risk for CAD (coronary artery disease) in individuals of Caucasian ancestry. We performed a genetic association study to investigate the effect of 12 candidate SNPs within 9p21.3 locus on the risk of CAD in the Saudi population of the Eastern Province of Saudi Arabia. A total of 250 Saudi CAD patients who had experienced an myocardial infarction (MI) and 252 Saudi age-matched healthy controls were genotyped using TaqMan assay. Controls with evidenced lack of CAD provided 90% of statistical power at the type I error rate of 0.05. Five percent of the results were rechecked for quality control using Sanger sequencing, the results of which concurred with the TaqMan genotyping results. Association analysis of 12 SNPs indicated a significant difference in the genotype distribution for four SNPs between cases and controls (rs564398 p = 0.0315, χ² = 4.6, odds ratio (OD) = 1.5; rs4977574 p = 0.0336, χ² = 4.5, OD = 1.4; rs2891168 p = 1.85 × 10 − 10, χ² = 40.6, OD = 2.1 and rs1333042 p = 5.14 × 10 − 9, χ² = 34.1, OD = 2.2). The study identified three protective haplotypes (TAAG p = 1.00 × 10 − 4; AGTA p = 0.022 and GGGCC p = 0.0175) and a risk haplotype (TGGA p = 2.86 × 10 − 10) for the development of CAD. This study is in line with others that indicated that the SNPs located in the intronic region of the CDKN2B-AS1 gene are associated with CAD.     

Visual methods for determining ambient illumination conditions when viewing medical images in mobile display devices

Abstract— The quality of the displayed image on mobile devices is affected by the varying ambient illumination conditions. Determining appropriate viewing conditions for particular visual tasks requires time and the appropriate instrumentation. To this end, the usefulness of more practical visual tests for use in clinical environments were explored. Experiments to determine the limitations of mobile displays in terms of the visibility of subtle targets for different background luminance and ambient illumination with two mobile devices were conducted. A noise‐embedded text detection task and a threshold estimation staircase technique for a range of illuminance between 1 and 80,000 lx encompassing conditions found in dark reading rooms, office spaces, and outdoor scenarios has been compared. It was found that the text detection method holds promise as a surrogate for more complicated tests in the framework of a clinically practical implementation.     

Discriminative compact pyramids for object and scene recognition

Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.     

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

The driver’s cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.