Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware variants.This model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors.The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware behavior.From these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under scrutiny.The outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final decision.The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of features.The efficacy of our proposed APIbased hybrid model is evident in its performance metrics.It outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

Model Detection and Variable Selection for Varying Coefficient Models with Longitudinal Data

In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and the separation of varying and constant coefficients, and the penalized estimators have the oracle property. Finite sample performances of the proposed method are illustrated by some simulation studies and the real data analysis.

Establishment of an optimized CTC detection model consisting of EpCAM,MUC1 and WT1 in epithelial ovarian cancer and its correlation with clinical characteristics

Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.Methods:We enrolled 160 patients with epithelial ovarian cancer as the experimental group,and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group.We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens(EpCAM and MUC1),and used multiple reverse transcription-polymerase chain reaction(RT-PCR)detecting three markers(EpCAM,MUC1 and WT1)for quantification.And then we used a binary logistic regression analysis and focused on EpCAM,MUC1 and WT1 to establish an optimized CTC detection model.Results:The sensitivity and specificity of the optimized model is 79.4%and 92.2%,respectively.The specificity of the CTC detection model is significantly higher than CA125(92.2%vs.82.2%,P=0.044),and the detection rate of CTCs was higher than the positive rate of CA125(74.5%vs.58.2%,P=0.069)in early-stage patients(stage I and II).The detection rate of CTCs was significantly higher in patients with ascitic volume≥500 mL,suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy(P<0.05).The detection rate of CTC;and CTC;was significantly higher in chemo-resistant patients(26.3%vs.11.9%;26.4%vs.13.4%,P<0.05).The median progression-free survival time for CTC;patients trended to be longer than CTC;patients,and overall survival was shorter in CTC;patients(P=0.043).Conclusions:Our study presents an optimized detection model for CTCs,which consists of the expression levels of three markers(EpCAM,MUC1 and WT1).In comparison with CA125,our model has high specificity and demonstrates better diagnostic values,especially for early-stage ovarian cancer.Detection of CTC;and CTC;had predictive value for chemotherapy resistance,and the detection of CTC;suggested poor prognosis.

A Novel Shilling Attack Detection Model Based on Particle Filter and Gravitation

With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Numerical analysis of the resonance mechanism of the lumped parameter system model for acoustic mine detection

The method of numerical analysis is employed to study the resonance mechanism of the lumped parameter system model for acoustic mine detection. Based on the basic principle of the acoustic resonance technique for mine detection and the characteristics of low-frequency acoustics, the “soil-mine” system could be equivalent to a damping “mass-spring” resonance model with a lumped parameter analysis method. The dynamic simulation software, Adams, is adopted to analyze the lumped parameter system model numerically. The simulated resonance frequency and anti-resonance frequency are 151 Hz and 512 Hz respectively, basically in agreement with the published resonance frequency of 155 Hz and anti-resonance frequency of 513 Hz, which were measured in the experiment. Therefore, the technique of numerical simulation is validated to have the potential for analyzing the acoustic mine detection model quantitatively. The influences of the soil and mine parameters on the resonance characteristics of the soil–mine system could be investigated by changing the parameter setup in a flexible manner.

A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures

There exists model uncertainty of probability of detection for inspecting ship structures with nondestructive inspection techniques. Based on a comparison of several existing probability of detection (POD) models, a new probability of detection model is proposed for the updating of crack size distribution. Furthermore, the theoretical derivation shows that most existing probability of detection models are special cases of the new probability of detection model. The least square method is adopted for determining the values of parameters in the new POD model. This new model is also compared with other existing probability of detection models. The results indicate that the new probability of detection model can fit the inspection data better. This new probability of detection model is then applied to the analysis of the problem of crack size updating for offshore structures. The Bayesian updating method is used to analyze the effect of probability of detection models on the posterior distribution of a crack size. The results show that different probabilities of detection models generate different posterior distributions of a crack size for offshore structures.

Density-based rough set model for hesitant node clustering in overlapping community detection

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

A Novel System for Moving Object Detection Using Bionic Compound Eyes

Conventional moving target detection focuses on algorithms to improve detection efficiency. These algorithms pay less attention to the image acquisition means, and usually solve specific problems. This often results in poor flexibility and reus- ability. Insect compound eyes offer unique advantages for moving target detection and these advantages have attracted the attention of many researchers in recent years. In this paper we proposed a new system for moving target detection. We used the detection mechanism of insect compound eyes for the simulation of the characteristics of structure, control, and function. We discussed the design scheme of the system, the development of the bionic control circuit, and introduced the proposed mathe- matical model of bionic cqmpound eyes for data acquisition and object detection. After this the integrated system was described and discussed. Our paper presents a novel approach for moving target detection. This approach effectively tackles some of the well-known problems in the field of view, resolution, and real-time processing problems in moving target detection.

Method to Detect Granary Storage Weight Based on the Janssen Model

This study proposes a new model of granary storage weight detection based on the Janssen model to satisfy the strategic requirements of granary storage quantity detection in China. The model theoretically elucidates the relationship between granary storage weight and bottom/side pressure. A new layout of pressure sensors along the inner and outer rings is also proposed to obtain the pressure value. The experimental results indicate that the detection error of the proposed model is significantly lower than 1% with respect to the low-cost detection system, and this effectively satisfies the actual requirement for real-time monitoring of granary storage quantity.

A generalized model of TiOx-based memristive devices and its application for image processing

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.

Quantum Spin Liquid Phase in the Shastry–Sutherland Model Detected by an Improved Level Spectroscopic Method

We study the spin-1/2 two-dimensional Shastry–Sutherland spin model by exact diagonalization of clusters with periodic boundary conditions, developing an improved level spectroscopic technique using energy gaps between states with different quantum numbers. The crossing points of some of the relative(composite) gaps have much weaker finite-size drifts than the normally used gaps defined only with respect to the ground state, thus allowing precise determination of quantum critical points even with small clusters. Our results support the picture of a spin liquid phase intervening between the well-known plaquette-singlet and antiferromagnetic ground states, with phase boundaries in almost perfect agreement with a recent density matrix renormalization group study, where much larger cylindrical lattices were used [J. Yang et al., Phys. Rev. B 105, L060409(2022)]. The method of using composite low-energy gaps to reduce scaling corrections has potentially broad applications in numerical studies of quantum critical phenomena.

Rapid Detection of Cement Raw Meal Composition Based on Near Infrared Spectroscopy

The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.

Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.

Diffusion tensor imaging as a tool to detect presymptomatic axonal degeneration in a preclinical spinal cord model of amyotrophic lateral sclerosis

The G93A-SOD1 mice model and MRI diffusion as a preclinical tool to study amyotrophic lateral sclerosis （ALS）： ALS is a progressive neurological disease characterized primarily by the development of limb paralysis, which eventually leads to lack of control on muscles under voluntary control and death within 3–5 years. Genetic heterogeneity and environmental factors play a critical role in the rate of disease progression and patients display faster declines once the symptoms have manifested. Since its original discovery, ALS has been associated with pathological alterations in motor neurons located in the spinal cord （SC）, where neuronal loss by a mutation in the protein superoxide dismutase in parenthesis （mSOD1） and impairment in axonal connectivity, have been linked to early functional impairments. In addition,mechanisms of neuroinflammation, apoptosis, necroptosis and autophagy have been also implicated in the development of this disease. Among different animal models developed to study ALS, the transgenic G93A-SOD1 mouse has become recognized as a benchmark model for preclinical screening of ALS therapies. Furthermore, the progressive alterations in the locomotor phenotype expressed in this model closely resemble the progressive lower limb dysfunction of ALS patients. Among other imaging tools, MR diffusion tensor imaging （DTI） has emerged as a crucial, noninvasive and real time neuroimaging tool to gather information in ALS. One of the current concerns with the use of DTI is the lack of biological validation of the microstructural information given by this technique. Although clinical studies using DTI can provide a remarkable insight on the targets of neurodegeneration and disease course,they lack histological correlations. To address these shortcomings, preclinical models can be designed to validate the microstructural information unveiled by this particular MRI technique. Thus, the scope of this review is to describe how MRI diffusion and optical microscopy evaluate axonal structural changes at early stages of the disease in a preclinical model of ALS.

Power Curve Modeling for Wind Turbine Using Hybrid-driven Outlier Detection Method

Wind power curve modeling is essential in the analysis and control of wind turbines(WTs),and data preprocessing is a critical step in accurate curve modeling.As traditional methods do not sufficiently consider WT models,this paper proposes a new data cleaning method for wind power curve modeling.In this method,a model-data hybrid-driven(MDHD)outlier detection method is constructed,and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model.Simultaneously,because the MDHD outlier detection method considers multiple types of operating data of WTs,anomaly detection results require further analysis.Accordingly,an expert system is developed in which a knowledgebase and an inference engine are designed based on the coupling relationships of different operating data.Finally,abnormal data are eliminated and the power curve modeling is completed.The proposed and traditional methods are compared in numerical cases,and the superiority of the proposed method is demonstrated.

A hyperspectral detection model for permeability coefficient of debris flow fine-grained sediments, Southwestern China

Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm.They startfirst when meeting water,their stability is related to the initial water volume triggering debrisflow,and thus plays an important role in debrisflow hazards early warning.The permeability coefficient is the inter-controlled factor offine-grained sediment stability.However,there is no hyperspectral model for detecting thefine-grained sediment permeability coefficient in large areas,which seriously affects the progress of debrisflow hazards early warning.Therefore,it is of great significance to establish a hyperspectral detection model for the permeability coefficient offine-grained sediments.Taking Beichuan County,Southwestern China as the case,a permeability coefficient hyperspectral detection model was established.The results show that eight bands are sensitive to the permeability coefficient with correlation coefficient(R)of 0.6343.T-test on the model shows that P-a values for sensitive bands are all less than 0.05,indicating the established model has a good prediction ability with a precision of 85.83%.These sensitive bands also indicate the spectral characteristics of the permeability coefficient.Therefore,it provides a scientific basis forfine-grained sediment stability detection in large areas and lays a theoretical foundation for debrisflow hazards’early warning.