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.     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.     

Optimization of annealing cycle parameters of dual phase and interstitial free steels by multiobjective genetic algorithms

Data-driven models have been constructed for Dual Phase (DP) and Interstitials Free (IF) steels using an evolutionary approach. DP steel data are utilized from an existing database, while for the IF steels, data generated at an integrated steel plant have been used. The objective function for Ultimate Tensile Strength (UTS) and % elongation, created as data-driven models, is simultaneously optimized for an optimum strength-ductility balance and the results indicate the possibilities of developing steels with better mechanical properties than what are known to have been existing so far.     

Efficient and 1,8-diiodooctane-free ternary organic solar cells fabricated via nanoscale morphology tuning using small-molecule dye additive

The ternary strategy for incorporating multiple photon-sensitive components into a single junction has emerged as an effective method for optimizing the nanoscale morphology and improving the device performance of organic solar cells (OSCs).In this study,efficient and stable ternary OSCs were achieved by introducing the small-molecule dye (5E,5'E)-5,5'-(4',4″-(1,2-diphenylethene-1,2-diyl)bis(biphenyl-4',4-diyl))bis(methan-1-yl-1-ylidene)bis(3-ethyl-2-thioxothia zolidin-4-one) (BTPERn) into poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b']dithiopheneco-3-fluorothieno[3,4-b]thiophene-2-carboxylate] (PTB7-Th):[6,6]-phenyl C71 butyric acid methyl ester (PC71BM) blend films processed using a 1,8-diiodooctane (DIO)-free solvent.The incorporation of BTPE-Rn enhanced the short-circuit current density and fill factor of the ternary OSCs compared with those of binary OSCs.An investigation of the optical,electronic,and morphological properties of the ternary blends indicated that the third component of BTPE-Rn not only promoted the photon utilization of blends through the energy-transfer process but also improved the electron mobility of the blends owing to the fullerene-rich nanophase optimization.More importantly,this ternary strategy of utilizing a small-molecule dye to replace the photounstable DIO additive enhanced the operational stability of the OSCs.     

Nanowire encapsulation with polymer for electrical isolation and enhanced optical properties

Light management and electrical isolation are essential for the majority of optoelectronic nanowire (NW) devices.Here,we present a cost-effective technique,based on vapor-phase deposition of parylene-C and subsequent annealing,that provides conformal encapsulation,anti-reflective coating,improved optical properties,and electrical insulation for GaAs nanowires.The process presented allows facile encapsulation and insulation that is suitable for any nanowire structure.In particular,the parylene-C encapsulation functions as an efficient antireflection coating for the nanowires,with reflectivity down to ＜1％ in the visible spectrum.Furthermore,the parylene-C coating increases photoluminescence intensity,suggesting improved light guiding to the NWs.Finally,based on this process,a NW LED was fabricated,which showed good diode performance and a clear electroluminescence signal.We believe the process can expand the fabrication possibilities and improve the performance of optoelectronic nanowire devices.     

Instance of hidden instability traps in intermittent transition of moving masses along a flexible beam

The problem of an elastic beam under the periodic loading of successive moving masses is investigated as a pragmatic case for studying dynamic stability of linear time-varying systems. This model serves to highlight the odds of multi-solutions coexistence, a form of hidden instability which reveals dangerous as it may be precipitated by the slightest disturbance or variation in the model. Since no engineering model perfectly represents a physical system, such situations for which Floquet theory naively predicts stability are potentially inevitable. The harmonic balancing method is used in order to thoroughly explore the stability diagrams for detecting these instability gaps. Although this phenomenon has also been described in other physical systems, it has not been addressed for beam–moving mass systems. This result may find particular importance in applications involving self-induced vibrations of elastic structures and hence also appears of practical relevance.     

Challenges in Liquid‐Phase Exfoliation,Processing, and Assembly of Pristine Graphene

Recent developments in the exfoliation, dispersion, and processing of pristine graphene (i.e., non‐oxidized graphene) are described. General metrics are outlined that can be used to assess the quality and processability of various “graphene” products, as well as metrics that determine the potential for industrial scale‐up. The pristine graphene production process is categorized from a chemical engineering point of view with three key steps: i) pretreatment, ii) exfoliation, and iii) separation. How pristine graphene colloidal stability is distinct from the exfoliation step and is dependent upon graphene interactions with solvents and dispersants are extensively reviewed. Finally, the challenges and opportunities of using pristine graphene as nanofillers in polymer composites, as well as as building blocks for macrostructure assemblies are summarized in the context of large‐scale production.     

Effect of Initial Annealing Temperature on Microstructural Development and Microhardness in High‐Purity Copper Processed by High‐Pressure Torsion

The effect of the initial annealing temperature on the evolution of microstructure and microhardness in high purity OFHC Cu is investigated after processing by HPT. Disks of Cu are annealed for 1 h at two different annealing temperatures, 400 and 800 °C, and then processed by HPT at room temperature under a pressure of 6.0 GPa for 1/4, 1/2, 1, 5, and 10 turns. Samples are stored for 6 months after HPT processing to examine the self‐annealing effects. Electron backscattered diffraction (EBSD) measurements are recorded for each disk at three positions: center, mid‐radius, and near edge. Microhardness measurements are also recorded along the diameters of each disk. Both alloys show rapid hardening and then strain softening in the very early stages of straining due to self‐annealing with a clear delay in the onset of softening in the alloy initially annealed at 800 °C. This delay is due to the relatively larger initial grain size compared to the alloy initially annealed at 400 °C. The final microstructures consist of homogeneous fine grains having average sizes of ≈0.28 and ≈0.34 µm for the alloys initially annealed at 400 and 800 °C, respectively. A new model is proposed to describe the behavior of the hardness evolution by HPT in high purity OFHC Cu.