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.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.     

TiN and TaN diffusion barriers in copper interconnect technology: Towards a consistent testing methodology

The present status of work on diffussion barriers for copper in multilevel interconnects is surveyed briefly, with particular emphasis on TiN and TaN, and silicon dioxide as the interlayer dielectric. New results are presented for these materials, combining thermal annealing and bias temperature stress testing. With both stress methods, various testing conditions are compared using capacitance-vs-voltage (C-V) and leakage current-vs-voltage (I-V) measurements to characterize the stressed samples. From an evaluation of these data and a comparison with other testing approaches, conditions for a consistent testing methodology of barrier reliability are outlined.     

REBTAM: reliable energy balance traffic aware data reporting algorithm for object tracking in multi-sink wireless sensor networks

Recently, Multi-sink Wireless Sensor Networks (WSNs) have received more and more attention due to their significant advantages over the single sink WSNs such as improving network throughput, balancing energy consumption, and prolonging network lifetime. Object tracking is regarded as one of the key applications of WSNs due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. However, many object tracking researches usually focus on how to track the location of objects accurately, while few researches focus on data reporting. In this work, we propose an efficient data reporting method for object tracking in multi-sink WSNs. Due to the limited energy resource of sensor nodes, it seems especially important to design an energy efficient data reporting algorithm for object tracking in WSNs. Moreover, the reliable data transmission is an essential aspect that should be considered when designing a WSN for object tracking application, where the loss of data packets will affect the accuracy of the tracking and location estimation of a mobile object. In addition, congestion in WSNs has negative impact on the performance, namely, decreased throughput, increased per-packet energy consumption and delay, thus congestion control is an important issue in WSNs. Consequentially, this paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in our model where the sensed data can reach the sink node in a more reliable way. Finally, this paper presents a solution that sufficiently exerts the underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. This work first formulates the problem as 0/1 Integer Linear Programming problem, and proposes a Reliable Energy Balance Traffic Aware greedy Algorithm in multi-sink WSNs (REBTAM) to solve the optimization problem. Through simulation, the performance of the proposed approach is evaluated and analyzed compared with the previous work which is related to our topic such as DTAR, NBPR, and MSDDGR protocols.     

A MC-CDMA Mobile Communication System Employing Pre-Rake and Transmit Diversity

In this paper, a multi-carrier code division multiple access (MC-CDMA) downlink mobile communication system employing pre-rake and dual transmit diversity is proposed. It combines high spectral efficiency with an immunity to channel dispersion and fading. It also ensures small size, cost and power consumption of the terminal. Theoretical and simulation results for the system under consideration are obtained. Depicted results show appreciable improvements of the proposed system over those previously known.Emad K. Al-Hussaini received his B.Sc degree in Electrical Communication Engineering from Ain-Shams University, Cairo, Egypt, in 1964 and his M.Sc and Ph.D. degrees from Cairo University, Giza, Egypt, in 1974 and 1977, respectively. From 1964 to 1970, he was with the General Egyptian Aeroorganization. Since 1970, he has been with the Department of Electronics and Communications, Faculty of Engineering, Cairo University, and is currently professor there. He was a research fellow at Imperial College, London, UK, and at the Moore School of Electrical Engineering, University of Pennsylvania, Philadelphia, PA, USA, in the academic years 1976/1977 and 1981/1982, respectively. In 1990, he received the Egyptian national encouragement award for outstanding engineering research. He has written several papers for technical international journals and conferences. His research interests include signal processing, fading channel communication, modulation, and cellular mobile radio systems. Dr Al-Hussaini is a senior member of IEEE. He is listed in Marquis Whos Who in the World and in the IBC (International Biographical Center, Cambridge) for outstanding people of the 20th century.Hebat-Allah M.Mourad received her B.Sc, M.Sc and Ph.D degrees in Electrical Communication Engineering from Cairo University, Egypt, in 1983, 1987 and 1994 respectively. Since 1983 she has been with the Electronics and Communications Department, Faculty of Engineering Cairo University and is currently associate professor there. Her research interests include mobile communications, satellite communications and optical fiber communications.Fatma A. Newagy received her B.Sc and M.Sc degrees in electrical communication engineering from Cairo University, Egypt in 1998 and 2002 respectively. Since 1999, she has been a research assistant with the Department of Electronics and Communications, Faculty of Engineering, Cairo University. She is pursuing her Ph.D. there. Her research interests include mobile communications and modulation techniques for spread spectrum and wireless communications.     

Structural Stabilities and Elastic Thermodynamic Properties of SrTe Compound and SrTe<Subscript>1−<Emphasis Type="Italic">x</Emphasis></Subscript>Ca<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript> Alloy Under High Pressure

The effect of pressure on the structural, elastic, and thermodynamic properties of SrTe in both B1 (rocksalt) and B2 (CsCl-type) phases and the SrTe_1?x Ca _x alloys with Ca dopant concentrations at x = 0.16667, 0.20, 0.33333, 0.42857, 0.44444 and 0.50 have been investigated using the two new gradient-corrected functional developed by Perdew, J.P.; Burke, K.; Ernzerhof named Density-Gradient Expansion for Exchange in Solids (PBEsol) and generalized Wu–Cohen (WC), in a significant range of pressure from 0 GPa to 30 GPa. The structure parameters, elastic stiffness constants c _ij , the bulk modulus (B), Kleinman parameter (\( \xi \)), shear anisotropies A _shear are also determined. Furthermore, as reported in this study, the aggregate elastic modulus (B, G, E), Poisson’s ratio (ν) and the Lame’s coefficients (λ) are estimated. On the other hand, the ductility, brittleness, longitudinal, transverse sound velocities and the Debye temperature Θ_D(T) are also obtained. Importantly, our results are in reasonable agreement with the available theoretical and experimental data. To the best of our knowledge, this is the first study of the effect of the composition on the properties of the SrTe_1?x Ca _x alloys which may encourage other works for the confirmation of the reported results.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Time-variant balanced routing strategy for underwater wireless sensor networks

In the past decade, researchers’ interest in Underwater Wireless Sensors Networks has rapidly increased. There are several challenges facing the lifetime of UWSNs due to the harsh characteristics of the underwater environment. Energy efficiency is one of the major challenges in UWSNs due to the limited battery budget of the sensor nodes. In this paper, we aim at tackling the energy sink-hole problem that especially hits nodes close to the sink when they run out of battery power before other sensors in the network. We prove that we can evenly distribute the transmission load among mobile sensor nodes by letting sensor nodes adjust their transmission ranges. In this paper, we suppose that sensor nodes may adjust their transmission power up to three levels. Consequently, we strive for deriving the optimal load weight for each possible transmission power level that leads to fair energy consumption among all underwater sensors while taking into account the underwater sensors mobility. Performance results show that energy sink-hole problem is overcame and hence the network lifetime is maximized.

     

Inter-plant hydrogen integration with regeneration placement and multi-period consideration

Inter-plant hydrogen integration (IPHI) is getting more attention in recent years, as a result of the increasing demand for hydrogen in refinery processes, such as hydrotreating and hydrocracking. In this work, IPHI with regeneration scheme is analyzed. Indirect integration scheme is adopted, where hydrogen sources from different hydrogen networks are integrated via a centralized purifier, such as pressure swing adsorption (PSA) or membrane separation. The introduced model is able to select the optimum interception unit, which minimizes the total annualized cost. Besides, multi-period consideration is included in the analysis to address the effect of changes in operating conditions of the IPHI network on total hydrogen consumption. Two case studies are presented to demonstrate the applicability and usefulness of the proposed model.