Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complexity and maximum detection accuracy. The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique, which incorporates the concepts of chaotic maps with CSOA. Besides, the OWKELM technique is applied for the intrusion detection and classification process. In addition, the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization (SFO) algorithm. The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance. In order to guarantee the supreme performance of the CCSOA-OWKELM technique, a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promising performance of the CCSOA-OWKELM technique over the recent state of art techniques.     

Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

Determination of methyl gallate in Bauhinia retusa extract by high-performance liquid and thin-layer chromatography

Two stability-indicating chromatographic methods are reported for the determination of methyl gallate in crude extracts of Bauhinia retusa. Separation by high performance thin layer chromatography was conducted on silica gel aluminum sheets using 9.5:0.5:0.2 (v/v/v) chloroform:methanol:acetic acid at 280 nm. The results from the 2–40 µg/band were used to prepare a linear calibration graph. The limits of detection and quantitation were 0.5 and 1.5 µg/band, respectively. The reverse phase high performance liquid chromatographic isolation of methyl gallate was performed at ambient temperature with an injection volume of 10 μL. The mobile phase consisted of 40:60 (v/v) methanol:0.1% ortho-phosphoric acid. The separation was performed at 1 mL/min using a detection wavelength of 280 nm. The calibration graph for methyl gallate was rectilinear from 0.02–40 µg/mL with limits of detection and quantitation of 0.004 and 0.010 µg/mL, respectively. For both methods, intra-day and inter-day precision were evaluated and the relative standard deviation was less than 2%, indicating good precision. The robustness was evaluated by making small and deliberate changes to appropriate parameters and the calculated relative standard deviation was less than 2%.The chromatographic methods were employed to determine methyl gallate in crude Bauhinia retusa extracts.     

Mixing of low-dose cohesive drug and overcoming of pre-blending step using a new gentle-wing high-shear mixer granulator

The objective of this study was to examine the influence of drug amount and mixing time on the homogeneity and content uniformity of a low-dose drug formulation during the dry mixing step using a new gentle-wing high-shear mixer. Moreover, the study investigated the influence of drug incorporation mode on the content uniformity of tablets manufactured by different methods. Albuterol sulfate was selected as a model drug and was blended with the other excipients at two different levels, 1% w/w and 5% w/w at impeller speed of 300?rpm and chopper speed of 3000?rpm for 30?min. Utilizing a 1?ml unit side-sampling thief probe, triplicate samples were taken from nine different positions in the mixer bowl at selected time points. Two methods were used for manufacturing of tablets, direct compression and wet granulation. The produced tablets were sampled at the beginning, middle, and end of the compression cycle. An analysis of variance analysis indicated the significant effect (p?相似文献   

Relative importance of informational units and their role in long-term recall by closed-head-injured patients and control groups.

Applied qualitative analysis to the information recalled by control Ss and closed-head-injured (CHI) patients. The Logical Memory subtest of the Wechsler Memory Scale (WMS) was administered to 40 CHI and 40 control Ss. Recall was tested immediately after administration, 40 min later, and 24 hrs later. The analysis took into account the importance of recalled information as determined by a prior rating according to 3 levels of importance. Results suggest that CHI patients have difficulty selectively retrieving the most important information after a long delay. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Exploring the potential of lead-chalcogenide monolayers for room-temperature thermoelectric applications

The development of materials in two-dimensions has been established as an effective approach to improve their thermoelectric performance for renewable energy production. In this article, we generated monolayers of the orthorhombic structured lead-chalcogenides PbX (X = S, Se, and Te) for room-temperature thermoelectric applications. The Density functional theory and semiclassical Boltzmann transport theory-based computational approaches have been adopted to carry out this study. The band structures of PbX monolayers exhibited narrow indirect bandgaps with a large density of states corresponding to their bandgap edges. Accordingly, substantial electrical conductivities and Seebeck coefficients have been obtained at moderate level doping that has caused significant thermoelectric power factors (PFs) and figures-of-merit (zT) ~1. The single-layered PbX showed anisotropic dispersion of electronic states in the band structure. A relatively lighter effective mass of charge carriers has been extrapolated from the bands oriented in the y-direction than that of the x-direction. As a result, the electrical conductivities and PFs have been observed larger in the y-direction. The optimum PFs recorded for single-layered PbS, PbSe, and PbTe in y-direction amounts to 9.90 × 10¹⁰ W/mK²s at 1.0 eV, 10.40 × 10¹⁰ W/mK²s at 0.82 eV, and 10.80 × 10¹⁰ W/mK²s 0.66 eV respectively. Moreover, a slight increase in p-type doping is found to improve the x-component of the PF, whereas n-type doping has led to improvement in the y-component of PF. Our results show an improved thermoelectric response of PbX monolayer (PbTe in particular) than their bulk counterparts reported in the literature, which indicates the promise of PbX monolayers for nanoscale thermoelectric applications at room temperature.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.