Recently, there has been considerable growth in the internet of things (IoT)-based healthcare applications; however, they suffer from a lack of intrusion detection systems (IDS). Leveraging recent technologies, such as machine learning (ML), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this paper, FIDChain IDS is proposed using lightweight artificial neural networks (ANN) in a federated learning (FL) way to ensure healthcare data privacy preservation with the advances of blockchain technology that provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, which prevents poisoning attacks and provides full transparency and immutability over the distributed system with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the BoT-IoT dataset. The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as intensive care unit (ICU) in healthcare systems. Testing the FIDChain with different datasets (CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99) reveals that the BoT-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.

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The term "Internet of Things" (IoT) refers to a group of gadgets that are capable of connecting to the Internet in order to gather and share data. The growth of Internet connections and the arrival of new technologies like the Internet of Things (IoT) have increased the privacy and security threats associated with the introduction of various gadgets. In order to increase the detection of cyber-attacks, industries are increasing their research spending. Institutions choose wise testing and verification techniques by comparing the highest rates of accuracy. IoT use has been accelerating recently across a variety of industries, including health care, smart homes, intelligent transportation, smart cities, and smart grids. where technology researchers and developers started to take notice of the IoT possibilities. Unfortunately, the privacy and security concerns imposed on by energy restrictions and the scalability of IoT devices present the most significant challenge to IoT. Therefore, how to address the IoT's security and privacy challenges remains an essential issue in the field of information security. With a decentralized design, edge computing plays a vital role in enabling IoT devices to compute, make decisions, take actions, and push only pertinent information to the cloud. Since sensitive data is more readily available and can be used right away, the IDS performs better when employing machine learning (ML) and deep learning (DL) algorithms to identify and prevent various threats. In terms of technical limitations, this study classifies the current, recent research in IoT intrusion detection systems employing machine learning, deep learning, and edge computing architecture.

Otitis media (OM) is a worldwide major health and economic issue leading to hearing loss, especially in developing countries due to high antibiotic resistance among the causative pathogens. This study aims to isolate, purify and determine the prevalence of MDR microorganisms causing OM. This is in addition to estimate in vitro effectiveness of irradiated and un-irradiated aqueous garlic extract (AGE). Finally, the present study aims to evaluate the enhancement of CIP, TOB, and NYS, commonly used in OM treatment, with IAGE against identified MDR bacteria and fungi. In the current investigation, the in vitro data revealed that OM is more prevalent in middle aged adults than in children. OM were predominated by bacterial isolates (59.0%), followed by fungal isolates (41.0%), including Pseudomonas aeruginosa (5 isolates), Proteus mirabilis (3 isolates), in addition to Klebsiella pneumoniae, Alcaligenes faecalis, Enterococcus faecalis, Bacillus cereus, Penicillium chrysogenum, Aspergillus flavus, and Aspergillus niger (1 isolate). Irradiated AGE (IAGE) showed nearly the same antimicrobial activity as the un-irradiated AGE. The inhibition activity enhancement was more statistically significant (P< 0.001) on combination of IAGE with CIP\NYS than with TOB (P< 0.05) and (P< 0.001) compared with each alone. Noticeable morphological changes in P. aeruginosa and A. flavus were observed by TEM images after the combination of IAGE with CIP/NYS. This study is one of primarily attempts to evaluate the IAGE to be used in future as a natural, safe, sterile, low cost, and available whole additive in therapeutic protocols to overcome MDR problem