The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of end-users.However,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user awareness.DoH significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security measures.Therefore,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user privacy.This paper provides an in-depth analysis of the effects of DoH on cybersecurity.We discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security studies.Overall,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

Single crystal growth and electronic structure of Rh-doped Sr_(3)Ir_(2)O_(7)

Ruddlesden-Popper iridate Sr_(3)Ir_(2)O_(7)is a spin-orbit coupled Mott insulator.Hole doped Sr_(3)Ir_(2)O_(7)provides an ideal platform to study the exotic quantum phenomena that occur near the metal-insulator transition(MIT)region.Rh substitution of Ir is an effective method to induce hole doping into Sr_(3)Ir_(2)O_(7).However,the highest doping level reported in Sr_(3)(Ir_(1-x)Rh_(x))_(2)O_(7)single crystals was only around 3%,which is far from the MIT region.In this paper,we report the successful growth of single crystals of Sr3(Ir_(1-x)Rh_(x))_(2)O_(7)with a doping level of~9%.The samples have been fully characterized,demonstrating the high quality of the single crystals.Transport measurements have been carried out,confirming the tendency of MIT in these samples.The electronic structure has also been examined by angle-resolved photoemission spectroscopy(ARPES)measurements.Our results establish a platform to investigate the heavily hole doped Sr_(3)Ir_(2)O_(7) compound,which also provide new insights into the MIT with hole doping in this material system.

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing capabilities.Cloud computing has traditionally played an important role in establishing IoT.However,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic distribution.However,IoT networks are vulnerable to unwanted assaults because of their open and shared nature.As a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available.In this study,we examined the time-related aspects of network traffic data.We presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset.We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of things.There is a wide demand for cooperation between equipment and management institutions in the smart city.Therefore,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious temminals.However,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered.Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is proposed.Unlike most trust models,this model uses self-certified trust.The cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction.At the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity weight.Finally,the simulation results show that CDTM has good anti collusion attack ability.The success rate of malicious interaction will not increase significantly.Compared with other models,the resource consumption of our proposed model is significantly reduced.

New strategy of S,N co‐doping of conductive‐copolymer‐derived carbon nanotubes to effectively improve the dispersion of PtCu nanocrystals for boosting the electrocatalytic oxidation of methanol

Efficacious regulation of the geometric and electronic structures of carbon nanomaterials via the introduction of defects and their synergy is essential to achieving good electrochemical performance.However,the guidelines for designing hybrid materials with advantageous structures and the fundamental understanding of their electrocatalytic mechanisms remain unclear.Herein,superfine Pt and PtCu nanoparticles supported by novel S,N‐co‐doped multi‐walled CNT(MWCNTs)were prepared through the innovative pyrolysis of a poly(3,4‐ethylenedioxythiophene)/polyaniline copolymer as a source of S and N.The uniform wrapping of the copolymer around the MWCNTs provides a high density of evenly distributed defects on the surface after the pyrolysis treatment,facilitating the uniform distribution of ultrafine Pt and PtCu nanoparticles.Remarkably,the Pt_(1)Cu_(2)/SN‐MWCNTs show an obviously larger electroactive surface area and higher mass activity,stability,and CO poisoning resistance in methanol oxidation compared to Pt/SN‐MWCNTs,Pt/S‐MWCNTs,Pt/N‐MWCNTs,and commercial Pt/C.Density functional theory studies confirm that the co‐doping of S and N considerably deforms the CNTs and polarizes the adjacent C atoms.Consequently,both the adsorption of Pt1Cu2 onto the SN‐MWCNTs and the subsequent adsorption of methanol are enhanced;in addition,the catalytic activity of Pt_(1)Cu_(2)/SN‐MWCNTs for methanol oxidation is thermodynamically and kinetically more favorable than that of its CNT and N‐CNT counterparts.This work provides a novel method to fabricate high‐performance fuel cell electrocatalysts with highly dispersed and stable Pt‐based nanoparticles on a carbon substrate.

Evaluation of Humic Acid Application Methods for Yield and Yield Components of Mungbean

A triplicate field experiment laid out in randomized complete block design was conducted to evaluate different humic acid (HA) application methods at Agricultural Research Farm, of KPK Agricultural University, Peshawar. Three methods of HA application: seed priming, foliar spray and soil application were included in the experiment. Humic acid application methods significantly affected pods plant-1, grains pod-1, 1000 grain weights, and grain yield whereas biological yield was not significantly affected by HA application methods. Humic acid application at the rate of 3 kg&middotha-1 resulted in higher number of pods plant-1, thousand grain weights and grain yield, however it was statistically similar to the treatments where HA was soil applied at rate of 1 and 2 kg&middotha-1, seed priming with 0% (water soaked), 1%, 2% HA solution and foliar spray with 0.01%, 0.05% and 0.1% of HA solution. It is concluded that HA application in all the three methods significantly enhances grain yield and yield components of mungbean.

An Efficient Impersonation Attack Detection Method in Fog Computing

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

Effective(kinetic freeze-out) temperature, transverse flow velocity, and kinetic freeze-out volume in high energy collisions

The transverse momentum spectra of different types of particles produced in central and peripheral gold–gold(Au–Au)and inelastic proton–proton(pp)collisions at the Relativistic Heavy Ion Collider,as well as in central and peripheral lead-lead(Pb–Pb)and pp collisions at the Large Hadron Collider,are analyzed by the multi-component standard(Boltzmann–Gibbs,Fermi–Dirac,and Bose–Einstein)distributions.The obtained results from the standard distribution give an approximate agreement with the measured experimental data by the STAR,PHENIX,and ALICE Collaborations.The behavior of the effective(kinetic freeze-out)temperature,transverse flow velocity,and kinetic freeze-out volume for particles with different masses is obtained,which observes the early kinetic freezeout of heavier particles as compared to the lighter particles.The parameters of emissions of different particles are observed to be different,which reveals a direct signature of the mass-dependent differential kinetic freeze-out.It is also observed that the peripheral nucleus–nucleus(AA)and pp collisions at the same center-of-mass energy per nucleon pair are in good agreement in terms of the extracted parameters.

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.

Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

Cyber-physical wireless systems have surfaced as an important data communication and networking research area.It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies.Due to their high reliability,sensitivity and connectivity,their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping,spoofing,botnets,man-in-the-middle attack,denial of service(DoS)and distributed denial of service(DDoS)and impersonation.Existing methods use physical layer authentication(PLA),themost promising solution to detect cyber-attacks.Still,the cyber-physical systems(CPS)have relatively large computational requirements and require more communication resources,thus making it impossible to achieve a low latency target.These methods perform well but only in stationary scenarios.We have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile scenarios.The features are fed to ensemble learning algorithms,such as AdaBoost,LogitBoost and Gentle Boost,to classify data.The authentication of the received signal is considered a binary classification problem.The transmitted data is labeled as legitimate information,and spoofing data is illegitimate information.Therefore,this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing attacks.It detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are mobile.The proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time.

Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

Secret key generation(SKG)is an emerging technology to secure wireless communication from attackers.Therefore,the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’uncertainty.However,the physical layer secret key generation(PHY-SKG)depends on two fundamental parameters,i.e.,coherence time and power allocation.The coherence time for PHY-SKG is not applicable to secure wireless channels.This is because coherence time is for a certain period of time.Thus,legitimate users generate the secret keys(SKs)with a shorter key length in size.Hence,an attacker can quickly get information about the SKs.Consequently,the attacker can easily get valuable information from authentic users.Therefore,we considered the scheme of power allocation to enhance the secret key generation rate(SKGR)between legitimate users.Hence,we propose an alternative method,i.e.,a power allocation,to improve the SKGR.Our results show 72%higher SKGR in bits/sec by increasing power transmission.In addition,the power transmission is based on two important parameters,i.e.,epsilon and power loss factor,as given in power transmission equations.We found out that a higher value of epsilon impacts power transmission and subsequently impacts the SKGR.The SKGR is approximately 40.7%greater at 250 from 50 mW at epsilon=1.The value of SKGR is reduced to 18.5%at 250 mW when epsilonis 0.5.Furthermore,the transmission power is also measured against the different power loss factor values,i.e.,3.5,3,and 2.5,respectively,at epsilon=0.5.Hence,it is concluded that the value of epsilon and power loss factor impacts power transmission and,consequently,impacts the SKGR.

Fine-grained Ship Image Recognition Based on BCNN with Inception and AM-Softmax

The fine-grained ship image recognition task aims to identify various classes of ships.However,small inter-class,large intra-class differences between ships,and lacking of training samples are the reasons that make the task difficult.Therefore,to enhance the accuracy of the fine-grained ship image recognition,we design a fine-grained ship image recognition network based on bilinear convolutional neural network(BCNN)with Inception and additive margin Softmax(AM-Softmax).This network improves the BCNN in two aspects.Firstly,by introducing Inception branches to the BCNN network,it is helpful to enhance the ability of extracting comprehensive features from ships.Secondly,by adding margin values to the decision boundary,the AM-Softmax function can better extend the inter-class differences and reduce the intra-class differences.In addition,as there are few publicly available datasets for fine-grained ship image recognition,we construct a Ship-43 dataset containing 47,300 ship images belonging to 43 categories.Experimental results on the constructed Ship-43 dataset demonstrate that our method can effectively improve the accuracy of ship image recognition,which is 4.08%higher than the BCNN model.Moreover,comparison results on the other three public fine-grained datasets(Cub,Cars,and Aircraft)further validate the effectiveness of the proposed method.

A Framework of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines

Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification.However,the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters.To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters,this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines(LC-CKMSVM).The framework consists of a feature extraction module and a classification module.The feature extraction module first maps the data from low-dimensional to high-dimensional space by fusing the representations of different dimensional spaces through cross-connections;then,it uses depthwise separable convolution to replace part of the original convolution to reduce the number of parameters in the module;The classification module uses a soft margin support vector machine for classification.The results on 6 different visual datasets show that LC-CKMSVM obtains better classification accuracies on most cases than the other five models.

Extraction and antibacterial potential of traditional medicinal plant Cypreus compressus

Plants are naturally occurring sources of drugs and can be used to minimize the problem of curing diseases up to the greater extent;for that purpose,we select a medicinal plant named Cypreus compressus.The extract is taken in the ethanol solvent.The crude extract of this medicinal plant is taken in Erlenmeyer flask tapped with a cotton pad.The crude extract is diluted to different concentrations in order to check the antibacterial activity at different concentrations of crude.The main aim of our work is to determine the antibacterial sensitivity test of Cypreus compressus against two types of bacteria i.e.,E.coli and S.aureus.Among all the different concentrations,the most significant was 20 mg for both types of bacteria i.e.,E.coli and S.aureus showing the greater zone of inhibition among all other concentrations that may use as an antibacterial agent in the future.

Multiplicity dependence of the freezeout parameters in high energy hadron-hadron collisions

We examined the transverse momentum(pT)spectra of various identified particles,encompassing both light-flavored and strange hadrons(π++π−,K++K−,p+p¯,ϕ,K0s,Λ+Λ¯,Ξ−+Ξ¯+,andΩ−+Ω¯+),across different multiplicity classes in proton-proton collisions(p-p)at a center-of-mass energy of s√=7 TeV.Utilizing the Tsallis and Hagedorn models,parameters relevant to the bulk properties of nuclear matter were extracted.Both models exhibit good agreement with experimental data.In our analyses,we observed a consistent decrease in the effective temperature(T)for the Tsallis model and the kinetic or thermal freeze-out temperature(T0)for the Hagedorn model,as we transitioned from higher multiplicity(class-I)to lower multiplicity(class-X).This trend is attributed to the diminished energy transfer in higher multiplicity classes.Additionally,we observed that the transverse flow velocity(βT)experiences a decline from class-I to class-X.The normalization constant,which represents the multiplicity of produced particles,was observed to decrease as we moved toward higher multiplicity classes.While the effective and kinetic freeze-out temperatures,as well as the transverse flow velocity,show a mild dependency on multiplicity for lighter particles,this dependency becomes more pronounced for heavier particles.The multiplicity parameter for heavier particles was observed to be smaller than that of lighter particles,indicating a greater abundance of lighter hadrons compared to heavier ones.Various particle species were observed to undergo decoupling from the fireball at distinct temperatures:lighter particles exhibit lower temperatures,while heavier ones show higher temperatures,thereby supporting the concept of multiple freeze-out scenarios.Moreover,we identified a positive correlation between the kinetic freeze-out temperature and transverse flow velocity,a scenario where particles experience stronger collective motion at a higher freeze-out temperature.The reason for this positive correlation is that,as the multiplicity increases,more energy is transferred into the system.This increased energy causes greater excitation and pressure within the system,leading to a quick expansion.

Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data centers.Modern TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow rerouting.In this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance deterioration.We look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a solution.Simulations are used to assess the proposed NUFTCP.Our findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

Comparative analysis of jet and underlying event properties across various models as a function of charged particle multiplicity at 7 TeV

In this study,a comprehensive analysis of jets and underlying events as a function of charged particle multiplicity in proton-proton(pp)collisions at a center-of-mass energy of √s=7 TeV is conducted.Various Monte Carlo(MC)event generators,including Pythia8.308,EPOS 1.99,EPOSLHC,EPOS4_(Hydro),and EPOS4_(noHydro),are employed to predict particle production.The predictions from these models are compared with experimental data from the CMS collaboration.The charged particles are categorized into those associated with underlying events and those linked to jets,and the analysis is restricted to charged particles with|η|<2.4 and p_T>0.25 GeV/c.By comparing the MC predictions with CMS data,we find that EPOS4_(Hydro),EPOSLHC,and Pythia8 consistently reproduce the experimental results for all charged particles,underlying events,intrajets,and leading charged particles.For charged jet rates with p_T^(ch.jet)>5 GeV/c,EPOS4_(Hydro)and Pythia8 perform exceptionally well.In the case of charged jet rates with p_T^(ch.jet)→30 GeV/c,EPOSLHC reproduces satisfactorily good results,whereas EPOS4 Hydro exhibits good agreement with the data at higher charged particle multiplicities compared to the other models.This can be attributed to the conversion of energy into flow when"Hydro=on"leading to an increase in multiplicity.The EPOSLHC model describes the data better owing to the new collective flow effects,correlated flow treatment,and parameterization compared to EPOS 1.99.However,the examination of the jet p_T spectrum and normalized charged p_T density reveals that EPOS4_(Hydro),EPOS4_(noHydro),and EPOSLHC exhibit good agreement with the experimental results,whereas Pythia8 and EPOS 1.99 do not perform as well owing to the lack of correlated flow treatment.

Design and Synthesis of NiO@Co_(3)O_(4)@ZSM-5 Heterogeneous Multitask Hollow Structures for Tandem Catalysis

Precipitation and impregnation procedures unevenly distribute metals on zeolite,limiting chemical transformation in Lewis-acid,Brönsted-acid and metal-catalyzed tandem reactions.Although,heterogeneous multitask transition metals oxides@zeolites are promising catalysts for sustainable processes;nevertheless,synthesis is fascinating and complex.Herein,the construction of purposely designed multitask materials segregated in selective shells reveals the remarkable spatial organization of metals-zeolite,resulting in them being suitable for a wide range of tandem reactions.The synthesis of multi-site catalysts begins with a universal wet chemistry approach that yields nickel oxide(NiO)crystals.Then,the NiO crystals are stabilized using cationic dodecyltrimethylammonium bromide,followed by achieving cross-linking carbon growth by emulsion polymerization of glucose in hydrothermal treatment to yield uniformed NiO@carbon spheres(NiO@CSs).Next,sequential adsorption of cobalt cations and colloidal ZSM-5(1%in H_(2)O,mass fraction),followed by calcination in air,yielded NiO@cobalt oxide@zeolite denoted as NiO@Co_(3)O_(4)@ZEO hollow spheres.The hollowing mechanism and materials segregation within shells are revealed by scanning and transmission electron microscopy,thermogravimetric analysis,and X-ray diffraction.The finding advances the rational synthesis of heterogenous core-shell hollow structures for various gas phase catalytic tandem reactions to yield valuable chemicals.

JUNO sensitivity on proton decay p→νK^(+)searches

The Jiangmen Underground Neutrino Observatory(JUNO)is a large liquid scintillator detector designed to explore many topics in fundamental physics.In this study,the potential of searching for proton decay in the p→νK^(+)mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification.Moreover,the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals.Based on these advantages,the detection efficiency for the proton decay via p→νK^(+)is 36.9%±4.9%with a background level of 0.2±0.05(syst)±0.2(stat)events after 10 years of data collection.The estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 years,which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.