A secure edge computing model using machine learning and IDS to detect and isolate intruders.

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. •The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network.

Deep convolutional neural network and IoT technology for healthcare.

Background: Deep Learning is an AI technology that trains computers to analyze data in an approach similar to the human brain. Deep learning algorithms can find complex patterns in images, text, audio, and other data types to provide accurate predictions and conclusions. Neuronal networks are another name for Deep Learning. These layers are the input, the hidden, and the output of a deep learning model. First, data is taken in by the input layer, and then it is processed by the output layer. Deep Learning has many advantages over traditional machine learning algorithms like a KA-nearest neighbor, support vector algorithms, and regression approaches. Deep learning models can read more complex data than traditional machine learning methods. Objectives: This research aims to find the ideal number of best-hidden layers for the neural network and different activation function variations. The article also thoroughly analyzes how various frameworks can be used to create a comparison or fast neural networks. The final goal of the article is to investigate all such innovative techniques that allow us to speed up the training of neural networks without losing accuracy. Methods: A sample data Set from 2001 was collected by www.Kaggle.com. We can reduce the total number of layers in the deep learning model. This will enable us to use our time. To perform the ReLU activation, we will make use of two layers that are completely connected. If the value being supplied is larger than zero, the ReLU activation will return 0, and else it will output the value being input directly. Results: We use multiple parameters to determine the most effective method to test how well our method works. In the next paragraph, we'll discuss how the calculation changes secret-shared Values. By adopting 19 train set features, we train our reliable model to predict healthcare cost's (numerical) target feature. We found that 0.89503 was the best choice because it gave us a good fit (R2) and let us set enough coefficients to 0. To develop our stable model with this Set of parameters, we require 26 iterations. We use an R2 of 0.89503, an MSE of 0.01094, an RMSE of 0.10458, a mean residual deviance of 0.01094, a mean absolute error of 0.07452, and a root mean squared log error of 0.07207. After training the model on the train set, we applied the same parameters to the test set and obtained an R2 of 0.90707, MSE of 0.01045, RMSE of 0.10224, mean residual deviation of 0.01045, MAE of 0.06954, and RMSE of 0.07051, validating our solution approach. The objective value of our secured model is higher than that of the scikit-learn model, although the former performs better on goodness-of-fit criteria. As a result, our protected model performs quite well, marginally outperforming the (very optimized) scikit-learn model. Using a backpropagation algorithm and stochastic gradient descent, deep Learning develops artificial neural systems with several interconnected layers. There may be hidden layers of neurons in the network that have the tanh, rectification, and max-out hyperparameters. Modern features like momentum training, dropout, active learning rate, rate annealed, and L1 or L2 regularization provide exceptional prediction performance. The worldwide model's parameters are multi-threadedly (asynchronously) trained on the data from that node, and the model-based data is then gradually augmented by model averaging over the entire network. The method is executed on a single-node, direct H2O cluster initiated by the operator. The operation is parallel despite there just being a single node involved. The number of threads may be adjusted in the settings menu under Preferences and General. The optimal number of threads for the system is used automatically. Successful predictions in the healthcare data sets are made using the H2O Deep Learning operator. There will be a classification done since its label is binomial. The Splitting Validation operator creates test and training datasets to evaluate the model. By default, the settings of the Deep Learning activator are used. To put it another way, we'll construct two hidden layers, each containing 50 neurons. The Accuracy measure is computed by linking the annotated Sample Set with a Performer (Binominal Classification) operator. Table 3 displays the Deep Learning Model, the labeled data, and the Performance Vector that resulted from the technique. Conclusions: Deep learning algorithms can be used to design systems that report data on patients and deliver warnings to medical applications or electronic health information if there are changes in the patient's health. These systems could be created using deep Learning. This helps verify that patients get the proper effective care at the proper time for each specific patient. A healthcare decision support system was presented using the Internet of Things and deep learning methods. In the proposed system, we examined the capability of integrating deep learning technology into automatic diagnosis and IoT capabilities for faster message exchange over the Internet. We have selected the suitable Neural Network structure (number of best-hidden layers and activation function classes) to construct the e-health system. In addition, the e-health system relied on data from doctors to understand the Neural Network. In the validation method, the total evaluation of the proposed healthcare system for diagnostics provides dependability under various patient conditions. Based on evaluation and simulation findings, a dual hidden layer of feed-forward NN and its neurons store the tanh function more effectively than other NN. To overcome challenges, this study will integrate artificial intelligence with IoT. This study aims to determine the NN's optimal layer counts and activation function variations.

A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things.

In IoT networks, the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is vulnerable to various attacks. Routing attacks in RPL-based IoT are becoming critical with the increase in the number of IoT applications and devices globally. To address routing attacks in RPL-based IoT, several security solutions have been proposed in literature, such as machine learning techniques, intrusion detection systems, and trust-based approaches. Studies show that trust-based security for IoT is feasible due to its simple integration and resource-constrained nature of smart devices. Existing trust-based solutions have insufficient consideration of nodes' mobility and are not evaluated for dynamic scenarios to satisfy the requirements of smart applications. This research work addresses the Rank and Blackhole attacks in RPL considering the static as well as mobile nodes in IoT. The proposed Security, Mobility, and Trust-based model (SMTrust) relies on carefully chosen trust factors and metrics, including mobility-based metrics. The evaluation of the proposed model through simulation experiments shows that SMTrust performs better than the existing trust-based methods for securing RPL. The improvisation in terms of topology stability is 46%, reduction in packet loss rate is 45%, and 35% increase in throughput, with only 2.3% increase in average power consumption.

Multiwalled Carbon Nanotubes as Nanomaterial Tool in the Management of Prostate Cancer: A Possible Nanoformulation Approach.

Prostate cancer (PCa) is one of the leading diseases in men all over the world caused due to over-expression of prostate-specific membrane antigen (PSMA). Currently, the detection and targeting of PCa is one of the major challenges in the prostate gland. Therefore, Bruton tyrosine kinase inhibitor molecules like ibrutinib (Ibr) loaded with nanomaterials like multi-walled carbon nanotubes (MWCNTs), which has good physico-chemical properties may be the best regimen to treat PCa. In this strategy, the chemically modified MWCNTs have excellent 'Biosensing' properties makes it easy for detecting PCa without fluorescent agent and thus targets particular site of PCa. In the present study, Ibr/MWCNTs conjugated with T30 oligonucleotide may selectively target and inhibit PSMA thereby reduce the over-expression in PCa. Hence, the proposed formulation design can extensively reduce the dosage regimen without any toxic effect. Additionally, the present hypothesis also revealed the binding mode of Ibr in the catalytic pocket of PSMA by in silico method. Therefore, we presume that if this hypothesis proves correct, it becomes an additional novel tool and one of the conceivable therapeutic options in treating PCa.

Trust and Mobility-Based Protocol for Secure Routing in Internet of Things.

In the Internet of Things (IoT), the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is susceptible to several disruptive attacks based on its functionalities and features. Among various RPL security solutions, a trust-based security is easy to adapt for resource-constrained IoT environments. In the existing trust-based security for RPL routing attacks, nodes' mobility is not considered or limited to only the sender nodes. Similarly, these trust-based protocols are not evaluated for mobile IoT environments, particularly regarding RPL attacks. Hence, a trust and mobility-based secure routing protocol is proposed, termed as SMTrust, by critically analysing the trust metrics involving the mobility-based metrics in IoT. SMTrust intends to provide security against RPL Rank and Blackhole attacks. The proposed protocol is evaluated in three different scenarios, including static and mobile nodes in an IoT network. SMTrust is compared with the default RPL objective function, Minimum Rank with Hysteresis Objective Function (MRHOF), SecTrust, DCTM, and MRTS. The evaluation results indicate that the proposed protocol outperforms with respect to packet loss rate, throughput, and topology stability. Moreover, SMTrust is validated using routing protocol requirements analysis to ensure that it fulfils the consistency, optimality, and loop-freeness.

Recent Trends in Carbon Nanotubes Based Prostate Cancer Therapy: A Biomedical Hybrid for Diagnosis and Treatment.

At present, treatment methods for cancer are limited, partially due to the solubility, poor cellular distribution of drug molecules and the incapability of drugs to cross the cellular barriers. Carbon Nanotubes (CNTs) generally have excellent physio-chemical properties, which include High-level penetration into the cell membrane, high surface area, and high capacity of drug-loading by circulating modification with biomolecules, projecting them as an appropriate candidate to diagnose and deliver drugs to Prostate Cancer (PCa). Additionally, the chemically modified CNTs possess excellent 'biosensing' properties, thus helping them detect the PCa easily without a fluorescent agent and additionally, targeting the particular site of PCa. In this way, drug delivery can accomplish high efficacy, enhanced permeability with less toxic effects. While CNTs have been mainly engaged in cancer treatment, a few studies are focused on the diagnosis and treatment of PCa. Here, we have meticulously reviewed the current progress of the CNTs-based diagnosis and the targeted drug delivery system for managing and curing PCa.

Molecular docking, quantum chemical computational and vibrational studies on bicyclic heterocycle "6-nitro-2,3-dihydro-1,4-benzodioxine": Anti-cancer agent.

The heterocyclic aromatic compounds are primarily used to make pharmaceutical and agrochemicals. In addition, these compounds can be chosen as antioxidants, corrosion inhibitors, electro and opto-electronic devices, polymer material, dye stuff, developers, etc. On the account of this, the heterocyclic aromatic 6-nitro-2,3-dihydro-1,4-benzodioxine (6N3DB) was chosen and the structure is optimized to predict the important properties of it. The structural parameters such as bond length and bond angle have been obtained by DFT/B3LYP/6-311++G(d,p) basis set to know the geometry and orientation of 6N3DB. The molecule has been characterized by FT-IR and FT-Raman spectroscopic techniques to predict the functional groups, vibrational modes and aromatic nature of 6N3DB. The chemical shifts of 1H and 13C have been obtained experimentally and compared with the theoretical data. The parameters such as the band gap between HOMO-LUMO orbitals, λmax, and electron transition probability in frontier orbitals have been estimated to know the NLO and corrosion inhibition activity. HOMO-LUMO orbital diagram has been obtained for different energy levels and their band gap energies have been compared with UV-vis band gap values. The chemical significance of the molecule has been explained using ELF, LOL, and RDG. The binding energy and intermolecular energy values indicate that the title compound possesses anti-cancer property through hydrolase inhibition activity.