Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

PERSISTENCE AND EXTINCTION OF A STOCHASTIC LOGISTIC MODEL WITH DELAYS AND IMPULSIVE PERTURBATION

A stochastic logistic model with delays and impulsive perturbation is proposed and investigated. Sufficient conditions for extinction are established as well as nonpersistence in the mean, weak persistence and stochastic permanence. The threshold between weak persistence and extinction is obtained. Furthermore, the theoretical analysis results are also derivated with the help of numerical simulations.

Genetic variation of height growth rhythm between clones of Larix kaempferi × L. gmelini based on logistic models

Fifty-three larch interspecific hybrid clones（Larix kaempferi × L.gmelini） and their parent clones were used for growth curve analysis of height variations.The growth curves of the 55 clones were ＇S＇-shaped and 36 exhibited similar curves as the male parent.The coefficients of the logistic models were higher than 0.943,indicating that our results were effective in the simulation of the growth curves.ANOVA analysis showed significant differences in height of different clones （P／0.01）.Average date of maximum height growth was Day 173,and average duration of rapid growth lasted for 50 days.Annual average increase in height was 9.7cm d^（-1） and daily average increase was 0.2 cm.The ratio of GR to the total annual increase in height ranged from 51.2 to 68.8%,with the average being 59.8%.There was a positive correlation between k values and plant heights which benefited from the evaluation of early plant height.There was also a positive correlation between GR（growth stage）,GD（plant height） and annual increase in height.These results are informative to the evaluation of the elite clone selection and provide a theoretical basis for breeding and management.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.

POSITIVE PERIODIC SOLUTION FOR A NONAUTONOMOUS LOGISTIC MODEL WITH LINEAR FEEDBACK REGULATION

A nonautonomous delayed logistic model with linear feedback regulation is proposed in this paper. Sufficient conditions are derived for the existence, uniqueness and global asymptotic stability of positive periodic solution of the model

The asymptotic stability analysis in stochastic logistic model with Poisson growth coefficient

The asymptotic stability of a discrete logistic model with random growth coefficient is studied in this paper. Firstly, the discrete logistic model with random growth coefficient is built and reduced into its deterministic equivalent system by orthogonal polynomial approximation. Then, the linear stability theory and the Jury criterion of nonlinear deterministic discrete systems are applied to the equivalent one. At last, by mathematical analysis, we find that the parameter interval for asymptotic stability of nontrivial equilibrium in stochastic logistic system gets smaller as the random intensity or statistical parameters of random variable is increased and the random parameter’s influence on asymptotic stability in stochastic logistic system becomes prominent.

On a new fractional-order Logistic model with feedback control

In this paper,we formulate and analyze a new fractional-order Logistic model with feedback control,which is different from a recognized mathematical model proposed in our very recent work.Asymptotic stability of the proposed model and its numerical solutions are studied rigorously.By using the Lyapunov direct method for fractional dynamical systems and a suitable Lyapunov function,we show that a unique positive equilibrium point of the new model is asymptotically stable.As an important consequence of this,we obtain a new mathematical model in which the feedback control variables only change the position of the unique positive equilibrium point of the original model but retain its asymptotic stability.Furthermore,we construct unconditionally positive nonstandard finite difference(NSFD)schemes for the proposed model using the Mickens’methodology.It is worth noting that the constructed NSFD schemes not only preserve the positivity but also provide reliable numerical solutions that correctly reflect the dynamics of the new fractional-order model.Finally,we report some numerical examples to support and illustrate the theoretical results.The results indicate that there is a good agreement between the theoretical results and numerical ones.

Transmission Based Conditional Logistic Model for Testing Main and Interaction Effects

Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmitted from parents and pseudo-offspring (control) with allele non-transmitted from parents, was built to detect the main effects of genes and gene-covariate interactions. When there exist genotype uncertainties, expectation-maximization (EM) algorithm was adopted to estimate the coefficients. The transmission model was applied to detect the association between M235T polymorphism in AGT gene and essential hypertension (ESH). Most of parents are not available in the 126 families from HongKong Chinese population. The results showed M235T is associated with hypertension and there is interaction between M235T and the case’s sex. The allele T is higher risk for male than female.

Analysis of Logistic Model with Constant Harvesting in a View of Non-Integer Derivative

The conformable fractional derivative method has been utilized in order to examine the logistic model with constant harvesting.Such method introduces a generalization to the classical analysis of Logistic model,and hence the features of the Logistic model,such as subcritical and supercritical harvesting,have been investigated in a view of fractional calculus.The positive auxiliary parameter,σ,with dimension of time is implemented to maintain the dimensionality of the system.The significant information of such parameter to the population has been discussed.The population expressions,obtained by conformable description,are compared with the expressions of the classical derivative.This comparison shows that the non-integer expressions are in a parallel line with that of the classical one.

Establishment of Fungal Decomposition Model Based on OLS and Logistic Model

By using the OLS model,an equation for the rate of decomposing wood by a variety of fungi was established.We analyzed the effects of various fungi in the experimental data under different temperature and humidity.Based on the growth performance of different fungi at different temperatures and humidity,we use the method of systematic cluster to divide the fungi into 5 categories,and introduce competition levels as the viability of different species of fungi.We have established a logistic model that introduces competition levels to obtain a fungal habitat model.The fungal habitat model includes predictions about the relative advantages and disadvantages for each species and combinations of species likely to persist,and do so for different environments including arid,semi-arid,temperate,arboreal,and tropical rain forests.

Model fitting of the seasonal population dynamics of the soybean aphid, Aphis glycines Matsumura, in the field

The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management(IPM). Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field. Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year. In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions. This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data. Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Two-sex logistic model for human papillomavirus and optimal vaccine

We formulate a two-gender susceptible-infectious-susceptible(SIS)model to search for optimal childhood and catch-up vaccines over a 20-year period.The optimal vaccines should minimize the cost of Human Papillomavirus(HPV)disease in random logistically growing population.We find the basic reproduction number Ro for the model and use it to describe the local-asymptotic stability of the disease-free equilibrium(DPE).We estimate the solution of the model to show the role of vaccine in reducing Rq and controlling the disease.We formulate some optimal control problems to find the optimal vaccines needed to control HPV under limited resources.The optimal vaccines needed to keep R_(0)≤1 are the catch-up vaccine rates of 0.004 and 0.005 for females and males,respectively;100%is needed to reduce R_(0)to its minimum value.To reduce the expenses for HPV disease and its vaccines,we need 100%childhood vaccines(both genders)for the first 13-14 years and then gradually reduce the vaccine to reach 0%at year 20.For adults(both genders),we need maximum rates(one)for the first 9 years,then 0.2 for the next 3-4 years before reducing gradually to zero rate at year 20.Although the childhood vaccines provide very early protection strategy against HPV,its time to control HPV is longer than that for adult vaccines.Thus,full adults’only vaccines for enough period is a viable choice to control HPV at minimal cost and short time.

Regional growth model for summer maize based on a logistic model:Case study in China

The growing degree days(GDD)is an important factor for crop growth because it affects dry matter formation and crop yield.In this study,the universal logistic models were established employing GDD and the relative GDD(RGDD)as the main parameters to characterize summer maize growth indices such as plant height(H),leaf area index(LAI),and dry matter accumulation(DMA).The relationships were analyzed between the growth indices,harvest index(HI),water consumption,and yield in maize.By considering China as an example,the results showed that the logistic model performed well at simulating the changes in the summer maize growth indices in different regions and the universal model parameters were within specific ranges.Furthermore,the logistic model with RGDD as the independent variable was more suitable for modeling summer maize growth in large areas than GDD.The relationship between the maximum LAI and HI was described by a quadratic polynomial function.HI was optimal(0.53)when the maximum LAI was about 5.13.The maximum LAI,maximum H,and maximum DMA could be described by a quadratic polynomial function of water consumption during the growing season.The summer maize yield could be predicted with a binary quadratic equation using the maximum GDD and water consumption.This study confirmed that a logistic model can be used to establish a universal growth model for summer maize in large areas.Reasonable ranges of parameters were recommended for the logistic model,as well as the reasonable water consumption and each growth index value for summer maize.These results will be helpful for predicting the growth and yield of summer maize.

Influencing factors analysis of helmet wearing for electric bicycle riders based on ordinal multinomial logistic model

The helmet of riders of electric bicycles plays an important role in reducing injuries and deaths in traffic accidents.This paper conducts a questionnaire survey,data analysis and modelling to investigate the influencing factors of electric bicycle helmet wearing.First,living area,gender,age,marital status and education level are selected as independent variables for data analysis.The factor analysis divides the sentiments of electric bicyclists for wearing helmets into four aspects:safety perception,practical sensation,satisfaction perception and emergency perception,and ordinal multiple logistic models are built to analyse the influencing factors.The result shows that people aged 18−25,26−35,36−45 and 46−55 are 1.3,1.8,2.0 and 2.3 times more likely,respectively,to have at least a grade higher safety perception than those aged 56 and over;men are 0.77 times more likely than women to feel at least one grade higher about the practical perception and 1.48 times more than women about the satisfaction perception;people with primary school,junior high school,senior high school,junior college and bachelor’s degree education are 1.64,2.44,1.50,1.70 and 1.55 times more likely,respectively,than people with a master’s degree to feel at least one grade higher about the satisfaction perception.

GLOBAL ATTRACTIVITY OF A LOGISTIC MODEL WITH UNBOUNDED DELAY

In this paper, we consider the following Logistic model where {rn}n=0 is a sequence of nonnegative real number, {kn} is a sequence of nonnegative integers satisfying lim (n-kn)= , lim sup kn=∞ , and K is a positive constant. We obtain a new sufficient condition for the positive equilibrium of Eq.() to be globally attractive, which improves some recent known results established in [3-4].

Choice behavior of commuters’rail transit mode during the COVID-19 pandemic based on logistic model

To understand whether commuters will take rail transit during the COVID-19 pandemic,a logistic regression model was constructed from three aspects of personal attributes,travel attributes and perception of COVID-19 based on 559 valid questionnaires.The results show that:occupation,commuting tools before the COVID-19 pandemic,walking time from residence to the nearest subway station,the possibility of being infected in private car and the possibility of being infected in public transport have significant influence on the commuters’choice of rail transit.Self-employed people and freelancers,commuters who used non-public transport before the COVID-19 pandemic,and commuters who take longer to walk from their residences to the nearest subway station are less likely to commute by rail transit during the COVID-19 pandemic.Commuters who think that the risk of being infected with the virus in public transport is higher have a lower probability of choosing rail transit.The confidence in bus/subway/taxi/taxi-hailing of commuters who do not choose to commute by rail transit during the COVID-19 pandemic is not high.The study of this paper can provide reference for the formulation of urban rail transit control measures during the COVID-19 pandemic,so as to formulate more perfect measures to ensure the safety of the returning workers.

GLOBAL ATTRACTIVITY IN A DISCRETE DELAY LOGISTIC MODEL

Consider the discrete delay logistic model where α∈(1,∞), β(0,∞), and k∈ {0,1,2,…}. We obtain new sufficient conditions for the positive equilibrium (α-1)/β of (1) to be a global attractor.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

Partial Oscillation of m-dimensional Logistic Ecologic Models

We present and discuss the partial oscillation with respect to equilibrium state ofm-dimensional Logistic delay ecologic models, and obtain some simple criteria.