A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes.

The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available.

An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing.

The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: IoT-edge and Cloud-processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. Optimized Yet Another Resource Negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic dataset using Apache Spark. The comparative analysis is decorated with existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.

Trustworthy and Reliable Deep Learning-based Cyberattack Detection in Industrial IoT.

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics such as trust, privacy, security, reliability, resilience and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this paper, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep learning-based Pyramidal Recurrent Units (PRU) and Decision Tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The non-linear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on fifteen datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems.

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes.

Target-AMP: Computational prediction of antimicrobial peptides by coupling sequential information with evolutionary profile.

Antimicrobial peptides (AMPs) are gaining a lot of attention as cutting-edge treatments for many infectious disorders. The effectiveness of AMPs against bacteria, fungi, and viruses has persisted for a long period, making them the greatest option for addressing the growing problem of antibiotic resistance. Due to their wide-ranging actions, AMPs have become more prominent, particularly in therapeutic applications. The prediction of AMPs has become a difficult task for academics due to the explosive increase of AMPs documented in databases. Wet-lab investigations to find anti-microbial peptides are exceedingly costly, time-consuming, and even impossible for some species. Therefore, in order to choose the optimal AMPs candidate before to the in-vitro trials, an efficient computational method must be developed. In this study, an effort was made to develop a machine learning-based classification system that is effective, accurate, and can distinguish between anti-microbial peptides. The position-specific-scoring-matrix (PSSM), Pseudo Amino acid composition, di-peptide composition, and combination of these three were utilized in the suggested scheme to extract salient aspects from AMPs sequences. The classification techniques K-nearest neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were employed. On the independent dataset and training dataset, the accuracy levels achieved by the suggested predictor (Target-AMP) are 97.07% and 95.71%, respectively. The results show that, when compared to other techniques currently used in the literature, our Target-AMP had the best success rate.

Exploring Saudi Individuals' Perspectives and Needs to Design a Hypertension Management Mobile Technology Solution: Qualitative Study.

Hypertension is a chronic condition caused by a poor lifestyle that affects patients' lives. Adherence to self-management programs increases hypertension self-monitoring, and allows greater prevention and disease management. Patient compliance with hypertension self-management is low in general; therefore, mobile health applications (mHealth-Apps) are becoming a daily necessity and provide opportunities to improve the prevention and treatment of chronic diseases, including hypertension. This research aims to explore Saudi individuals' perspectives and needs regarding designing a hypertension management mobile app to be used by hypertension patients to better manage their illnesses. Semi-structured interviews were conducted with 21 Saudi participants to explore their perspectives and views about the needs and requirements in designing a hypertension mobile technology solution, as well as usability and culture in the Saudi context. The study used NVivo to analyze data and divided the themes into four main themes: the app's perceived health benefits, features and usability, suggestions for the app's content, and security and privacy. The results showed that there are many suggestions for improvements in mobile health apps that developers should take into consideration when designing apps. The mobile health apps should include physical activity tracking, related diet information, and reminders, which are promising, and could increase adherence to healthy lifestyles and consequently improve the self-management of hypertension patients. Mobile health apps provide opportunities to improve hypertension patients' self-management and self-monitoring. However, this study asserts that mobile health apps should not share users' data, and that adequate privacy disclosures should be implemented.

NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite.

Plasmodium falciparum causes malaria, which is an infectious and fatal disease. In early days, malaria-infected cells were diagnosed using a microscope. owing to a huge number of instances for analysis and intricacy of time, it may lead to false detection. Automated parasite detection technologies are in high demand due to increased time consumption and erroneous detection. To create effective cures and treatments, it is critical to use an accurate approach for predicting malaria parasite. Here, numerous protein sequences formulation techniques namely: discrete methods, Biochemical, physiochemical and Natural language processing techniques are applied for transformation of protein sequences in to numerical descriptors. Four classification algorithms are utilized and the anticipated results of these classifiers were then fused to establish ensemble classification model via simple majority and genetic algorithm. In addition, BCH error correction code is incorporated with support vector machine using all the feature spaces. The simulated results demonstrate the remarkable achievement of proposed compared to previous models. Thus, our proposed model may be an effective tool for discriminating the secretory and non-secretory proteins of malaria parasite.

A Smart Device for a Preliminary Dental Examination Based on the Internet of Things.

The COVID-19 pandemic has threatened the lives of many people, especially the elderly and those with chronic illnesses, as well as threatening the global economy. In response to the pandemic, many medical centers, including dental facilities, have significantly reduced the treatment of patients by limiting clinical practice to exclusively urgent, nondeferred care. Dentists are more vulnerable to contracting COVID-19, due to the necessity of the dentist being close to the patient. One of the precautions that dentists take to avoid transmitting infections is to wear a mask and gloves. However, the basic condition for nontransmission of infection is to leave a safe distance between the patient and the dentist. This system can be implemented by using an Arduino microcontroller, which is designed as a preliminary device by a dentist to examine a patient's teeth so that a safe distance of three meters between the dentist and the patient can be maintained. The project is based on hardware and has been programmed through Arduino. The proposed system uses a small wired camera with a length of five meters that is connected to the dentist's mobile or laptop and is installed on a robotic arm. The dentist can control the movement of the arm in all directions using a joystick at a distance of three meters. The results showed the effectiveness of this system for leaving a safe distance between the patient and the dentist. In our future work, we will control the movement of the arm via Bluetooth, and we will use a wi-fi-based camera.

Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications.

Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing.

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV's residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal.

Augmentation in Healthcare: Augmented Biosignal Using Deep Learning and Tensor Representation.

In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other hand, shallow learning feature extraction techniques depend on user experience in selecting a powerful feature extraction algorithm. In this article, we proposed a multistage model that is based on the spectrogram of biosignal. The proposed model provides an appropriate representation of the input raw biosignal that boosts the accuracy of training and testing dataset. In the next stage, smaller datasets are augmented as larger data sets to enhance the accuracy of the classification for biosignal datasets. After that, the augmented dataset is represented in the TensorFlow that provides more services and functionalities, which give more flexibility. The proposed model was compared with different approaches. The results show that the proposed approach is better in terms of testing and training accuracy.

The Development of an Arabic Weight-Loss App Akser Waznk: Qualitative Results.

BACKGROUND: Obesity and its related illnesses are a major health problem around the world. Saudi Arabia has one of the highest national obesity rates globally; however, it is not easy to intervene to prevent obesity and becoming overweight owing to Saudi Arabia's cultural and social norms, and linguistic barriers. In recent years, there has been an exponential growth in the usage of smartphones and apps in Saudi Arabia. These could be used as a cost-effective tool to facilitate the delivery of behavior-modification interventions for obese and overweight people. There are a variety of health and fitness apps that claim to offer lifestyle-modification tools. However, these do not identify the motivational features required to overcome obesity, consider the evidence-based practices for weight management, or enhance the usability of apps by considering usability attributes. OBJECTIVE: This study aimed to explore the opportunity and the need to develop an Arabic weight-loss app that provides localized content and addresses the issues with existing apps identified here. This study has explained the steps taken to design an Arabic weight-loss app that was developed to facilitate the adjustment of key nutritional and physical activities and behaviors, which considers the social and cultural norms of Saudi Arabia. METHODS: Qualitative studies were conducted with 26 obese Saudi Arabians, who tested the level of usability of 2 weight-loss apps and then provided feedback and recommendations. The app Akser Waznk is an interactive, user-friendly app designed primarily for iPhones. It has several features intended to assist users to monitor and track their food consumption and physical activities. The app provides personalized diet and weight loss advice. Unique features such as Let's Walk are designed to motivate users to walk more. An augmented reality function is implemented to provide information regarding fitness equipment, fruits, and vegetables. The app uses behavior-change techniques to increase activities and healthy behaviors and evidence-informed practices for weight-loss management. The Akser Waznk app considers user privacy and data security by applying a number of guidelines and procedures. RESULTS: The development of the app took 26 months. In all, 7 experts (5 dietitians, and 2 physical activity professionals) evaluated the app's contents. Moreover, 10 potential users (5 men and 5 women) tested the app's level of usability, its features, and performance during a pilot study. They reported that the app's design is interactive, and the motivational features are user-friendly. CONCLUSIONS: Mobile technology, such as mobile apps, has the potential to be an effective tool that facilitates the changing of unhealthy lifestyle behaviors within the Saudi community. To be successful, the target group, the usability, motivational features, and social and cultural norms must be considered.