Covalent Bond Based Android Malware Detection Using Permission and System Call Pairs

The prevalence of smartphones is deeply embedded in modern society,impacting various aspects of our lives.Their versatility and functionalities have fundamentally changed how we communicate,work,seek entertainment,and access information.Among the many smartphones available,those operating on the Android platform dominate,being the most widely used type.This widespread adoption of the Android OS has significantly contributed to increased malware attacks targeting the Android ecosystem in recent years.Therefore,there is an urgent need to develop new methods for detecting Android malware.The literature contains numerous works related to Android malware detection.As far as our understanding extends,we are the first ones to identify dangerous combinations of permissions and system calls to uncover malicious behavior in Android applications.We introduce a novel methodology that pairs permissions and system calls to distinguish between benign and malicious samples.This approach combines the advantages of static and dynamic analysis,offering a more comprehensive understanding of an application’s behavior.We establish covalent bonds between permissions and system calls to assess their combined impact.We introduce a novel technique to determine these pairs’Covalent Bond Strength Score.Each pair is assigned two scores,one for malicious behavior and another for benign behavior.These scores serve as the basis for classifying applications as benign or malicious.By correlating permissions with system calls,the study enables a detailed examination of how an app utilizes its requested permissions,aiding in differentiating legitimate and potentially harmful actions.This comprehensive analysis provides a robust framework for Android malware detection,marking a significant contribution to the field.The results of our experiments demonstrate a remarkable overall accuracy of 97.5%,surpassing various state-of-the-art detection techniques proposed in the current literature.

Biomimetic Synthesis of Silver Nanoparticle Using Leaf Extract of Mangifera Indica and Their Antibacterial Efficacy

The aim of this study to focused on bioinspired synthesis of silver nanoparticles(AgNPs)as a viable alternative to eradicate the existing physicochemical processes.In this context,the bioinspired AgNPs were synthesized using leaf extract of M.indica.Optimization of the experimental conditions for the rapid and high yield of AgNPs in minimum investment of time and expense have been carried out along with their antibacterial efficacy evaluated.For this reason,the variation of parameters like the concentration of the silver precursors,reducing agent,time,pH,and temperature of synthesis were realized.Synthesized AgNPs were characterized by UV-Visible spectroscopy,X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR)and transmission electron microscopy(TEM)techniques.UV-Visible spectra gave surface plasmon resonance(SPR)at 440 nm for AgNPs.Fourier transform infrared spectroscopy(FTIR)and X-ray diffraction(XRD)techniques were further confirmed the synthesis and crystalline nature of AgNPs respectively.Transmission electron microscopy(TEM)observed spherical shapes of synthesized AgNPs within range 5～20 nm.The results of the current study indicate that optimization process play apivotal role in the AgNPs synthesis and biogenic synthesized AgNPs might be used against bacterial pathogens.

E-MOGWO Algorithm for Computation Offloading in Fog Computing

Despite the advances mobile devices have endured,they still remain resource-restricted computing devices,so there is a need for a technology that supports these devices.An emerging technology that supports such resource-con-strained devices is called fog computing.End devices can offload the task to close-by fog nodes to improve the quality of service and experience.Since com-putation offloading is a multiobjective problem,we need to consider many factors before taking offloading decisions,such as task length,remaining battery power,latency,communication cost,etc.This study uses the multiobjective grey wolf optimization(MOGWO)technique for optimizing offloading decisions.This is thefirst time MOGWO has been applied for computation offloading in fog com-puting.A gravity reference point method is also integrated with MOGWO to pro-pose an enhanced multiobjective grey wolf optimization(E-MOGWO)algorithm.Itfinds the optimal offloading target by taking into account two parameters,i.e.,energy consumption and computational time in a heterogeneous,scalable,multi-fog,multi-user environment.The proposed E-MOGWO is compared with MOG-WO,non-dominated sorting genetic algorithm(NSGA-II)and accelerated particle swarm optimization(APSO).The results showed that the proposed algorithm achieved better results than existing approaches regarding energy consumption,computational time and the number of tasks successfully executed.

Analysis of Radio over Fiber system for mitigating four-wave mixing effect

In this paper,an efficient 8-channel 32Gbps RoF(Radio over Fiber)system incorporating Bessel Filter(8/32 RoFBF)has been demonstrated to reduce the impact of non-linear transmission effects,specifically Four-Wave Mixing(FWM).The simulation results indicate that the proposed 8/32 RoF-BF system provides an optimum result w.r.t.channel spacing(75 GHz),input source power(0 dBm)and number of input channels(8).In comparison with the existing RoF system,the proposed 8/32 RoF-BF system has been validated analytically and it is found that the performance of the proposed system is in close proximity particularly in FWM sideband power reduction of the order of 4 dBm for the 8-channel 32Gbps RoF system.

Common Fixed Point Theorems for Compatible and Weakly Compatible Maps in G-Metric Spaces

In this paper first we prove common fixed point theorems for compatible and weakly compatible maps. Secondly, we prove common fixed point theorems for weakly compatible maps along with property (E.A.) and (CLRg) property respectively.

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

SSA-HIAST: A Novel Framework for Code Clone Detection

In the recent era of software development,reusing software is one of the major activities that is widely used to save time.To reuse software,the copy and paste method is used and this whole process is known as code cloning.This activity leads to problems like difficulty in debugging,increase in time to debug and manage software code.In the literature,various algorithms have been developed to find out the clones but it takes too much time as well as more space to figure out the clones.Unfortunately,most of them are not scalable.This problem has been targeted upon in this paper.In the proposed framework,authors have proposed a new method of identifying clones that takes lesser time to find out clones as compared with many popular code clone detection algorithms.The proposed framework has also addressed one of the key issues in code clone detection i.e.,detection of near-miss(Type-3)and semantic clones(Type-4)with significant accuracy of 95.52%and 92.80%respectively.The present study is divided into two phases,the first method converts any code into an intermediate representation form i.e.,Hashinspired abstract syntax trees.In the second phase,these abstract syntax trees are passed to a novel approach“Similarity-based self-adjusting hash inspired abstract syntax tree”algorithm that helps in knowing the similarity level of codes.The proposed method has shown a lot of improvement over the existing code clones identification methods.

Synthesis and characterization of rare earth metal doped tungsten trioxide photocatalyst for degradation of Rhodamine B dye

Nowadays,it is concern for researchers that due to high recombination rate of photogenerated charge carriers in tungsten trioxide(WO_(3)) nanoparticles,the future applications are limited in the field of photocatalysis.Herein we attempt to synthesize tungsten trioxide nanoparticles with different doping concentrations of lanthanum i.e.2 wt%,4 wt%,6 wt% and 8 wt%.The synthesized samples were characterized by using various characterization techniques:X-ray diffraction(XRD),Raman spectroscopy,Fourier transform infrared spectroscopy(FTIR),photoluminescence spectra(PL),transmission electron microscopy(TEM),energy dispersive X-ray(EDX) and UV-Vis spectroscopy.WO_(3) retains its monoclinic structure even after doping which was confirmed by XRD analysis.FTIR helps to descry functional groups present in the samples.The size of nanoparticles was calculated by using TEM.EDX confirms the absence of any impurity in the synthesized samples.Raman spectroscopy confirms the presence of a large number of imperfections induced in the lattice of WO_(3).The rate of recombination was analyzed by photoluminescence(PL) spectroscopy and is minimum in 4 wt% doping of lanthanum.The optical bandgap was calculated using UV-Vis spectroscopy and becomes narrow along with the doping concentrations.Intriguingly,it is found that doping of La in WO_(3) has considerably ameliorate the photocatalytic activity by reducing rate of recombination due to the trapping of electrons by defects introduced in the lattice.Photocatalytic decolorization of Rhodamine B(RhB) dye was performed and the values of c/c_0 and rate constant(k) confirm that the 4 wt% doping shows maximum degradation efficiency.The kinetic study for photodegradation of Rhodamine B was done by using various kinetic models and results show that the reaction follows first order kinetics very well.Therefore,optimum doping of lanthanum increases the decolorization ability of WO_(3) towards RhB dye.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.

Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.

Zn^(2+) substituted superparamagnetic MgFe_(2)O_(4) spinel-ferrites:Investigations on structural and spin-interactions

Nano-magnetic ferrites with composition Mg_(1-x)Zn_(x)Fe_(2)O_(4)(x=0.3,0.4,0.5,0.6,and 0.7)have been prepared by coprecipitation method.X-ray diffraction(XRD)studies showed that the lattice parameter was found to increase from 8.402 to 8.424A with Zn^(2+) ion content from 0.3 to 0.7.Fourier transform infrared(FTIR)spectra revealed two prominent peaks corresponding to tetrahedral and octahedral at around 560 and 430 cm^(-1) respectively that confirmed the spinel phase of the samples.Transmission electron microscopy(TEM)images showed that the particle size was noted to increase from 18 to 24 nm with an increase in Zn content from x=0.3 to 0.7.The magnetic properties were studied by vibrating sample magnetometer(VSM)and electron paramagnetic resonance(EPR)which ascertained the superparamagnetic behavior of the samples and contribution of superexchange interactions.The maximum magnetization was found to vary from 23.80 to 32.78 emu/g that increased till x=0.5 and decreased thereafter.Further,X-ray photoelectron spectroscopy(XPS)was employed to investigate the chemical composition and substantiate their oxidation states.

A comprehensive review and analysis of solar forecasting techniques

In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

NiCo2O4 decorated PANI–CNTs composites as supercapacitive electrode materials

Hierarchical Ni CoO/PANI/CNTs hybrid composites were designed and fabricated having a layer of Ni CoOon the surface of PANI encapsulated CNTs with different morphologies. Physicochemical attributes of the synthesized composites were examined by FTIR, UV–visible and X-ray diffraction（XRD）techniques. Morphological aspects were evaluated by field-emission scanning microscopy（FESEM）,electron diffraction spectroscopy（EDS）, high resolution transmission electron microscopy（HRTEM） and selected area electron diffraction（SAED） studies. Electrochemical measurements revealed an improved specific capacitance of 2250 F/g at a scan rate of 5 m V/s and 2000 F/g at a current density of 1 A/g with good rate capability using a three-electrode system. These enhanced features are achieved from the well designed nanostructure and the synergistic contributions of individual components in the electrode material.

Solar-PV inverter for the overall stability of power systems with intelligent MPPT control of DC-link capacitor voltage

This paper demonstrates the controlling abilities of a large PV-farm as a Solar-PV inverter for mitigating the chaotic electrical,electromechanical,and torsional oscillations including Subsynchronous resonance in a turbogenerator-based power system.The oscillations include deviations in the machine speed,rotor angle,voltage fluctuations(lead-ing to voltage collapse),and torsional modes.During the night with no solar power generation,the PV-plant switches to PV-STATCOM mode and works as a Solar-PV inverter at its full capacity to attenuate the oscillations.During full sun in the daytime,on any fault detection,the PV-plant responds instantly and stops generating power to work as a Solar-PV inverter.The PV-farm operates in the same mode until the oscillations are fully alleviated.This paper mani-fests the control of the DC-link capacitor voltage of the Solar-PV inverter with a bacterial foraging optimization-based intelligent maximum power point tracking controller for the optimal control of active and reactive power.Kundur’s multi-machine model aggregated with PV-plant is modeled in the Matlab/Simulink environment to examine the rotor swing deviations with associated shaft segments.The results for different test cases of interest demonstrate the posi-tive outcomes of deploying large PV-farms as a smart PV-STATCOM for controlling power system oscillations.

Linear Recurring Sequences and Explicit Factors of x2nd—1 in Fq[x]

Let F_(q) be a finite field of odd characteristic containing q elements,and n be a positive integer.An important problem in finite field theory is to factorize x^(n)-1 into the product of irreducible factors over a finite field.Beyond the realm of theoretical needs,the availability of coefficients of irreducible factors over finite fields is also very important for applications.In this paper,we introduce second order linear recurring sequences in F_(q) and reformulate the explicit factorization of x^(2nd)-1 over in such a way that the coefficients of its irreducible factors can be determined from these sequences when d is an odd divisor of q+1.

Revisiting Some Recently Developed Conducting Polymer@Metal Oxide Nanostructures for Electrochemical Sensing of Vital Biomolecules:A Review

Polymers and metal oxides are well-known materials for various electrochemical sensing applications due to their distinctive properties.In the current decades,the use of composites and nanocomposites of polymer and/or metal oxide in the sensing domain has been drastically explored.This review conveys the pursuance of several conducting and non-conducting polymers such as polypyrrole,polyvinylpyrrolidone,polyaniline,polythiophene,polyacetylene and their derivatives with several metal oxides(such as TiO_(2),SiO_(2),CeO2,ZnO,CuO,MnO_(2),Fe_(3)O_(4))in the area of electrochemical sensing of some important biomolecules.Herein,various methods of synthesis of nanocomposites,different characterization techniques employed,electrode modification and fabrication methods adopted are cited with rich comparative discussion for sensing applications,specifically in the field of electrochemical and bio-sensing impact using various electrochemical methods are also reviewed.The review will provoke and support the modern researchers of the field to approach new avenues.