Intrusion Methods Research Articles

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients’ privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore. In this paper, we propose an efficient three-factor cluster-based user authentication protocol (3ECAP). Specifically, we establish the security association between the user and the sensor cluster through fine-grained access control based on Merkle, which perfectly achieves the segmentation of permission. We then demonstrate that 3ECAP can address the privilege escalation attack caused by permission segmentation. Moreover, we further analyze the security performance and communication cost using formal and non-formal security analysis, Proverif, and NS3. Simulation results demonstrated the robustness of 3ECAP against various cyber-attacks and its applicability in an IoMT environment with limited storage resources.

Network Data Intrusion Detection and Data Feature Extraction of Electromechanical Facilities from Machine Learning

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset’s detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

Influence of Supercritical Conditions on Isothermal Adsorption Capacity Calculation of Methane and Model Optimization.

The study of isothermal adsorption models for coal and methane under supercritical conditions helps us to understand the gas content distribution in coal seams and improve gas management in coal mines. Coal samples from the Hebi mine and Longshan coal mine were selected for this research. Using the weight method, isothermal adsorption curves of supercritical methane at temperatures of 308, 313, and 318 K were measured. The adsorption phase density of supercritical methane was obtained by using the intercept method and model fitting, and the absolute adsorption capacity of methane was calculated. Simultaneously, the pore volume and specific surface area of the coal samples were tested, and the isothermal adsorption model for supercritical methane was studied and optimized. The results indicate that using mercury intrusion, low-temperature N2, and low-temperature CO2 adsorption methods for testing coal sample pore structures allows for multiscale characterization of the coal pore structure. Based on the Gibbs excess adsorption theory, the phase density of methane obtained from the intercept method and model fitting effectively represent the isothermal adsorption curve of supercritical methane. By combination of the specific surface area and pore volume distribution of the coal with the absolute adsorption capacity of methane, the number of methane adsorption layers on the coal surface was calculated to be between 1.11 and 1.3 layers. This suggests that the isothermal adsorption model for supercritical methane on coal is not solely micropore filling or single molecular layer adsorption but primarily single molecular layer adsorption with concurrent micropore filling adsorption. Based on this adsorption mechanism, an L-DA isothermal adsorption model for supercritical methane on coal was established. The L-DA isothermal model shows good fitting results and effectively explains the adsorption characteristics of supercritical methane on coal.

Pore Size Distribution and Fractal Characteristics of Deep Coal in the Daning-Jixian Block on the Eastern Margin of the Ordos Basin.

Important breakthroughs have recently been achieved in deep coalbed methane (CBM) exploration and development in regions such as the eastern margin of the Ordos Basin, China. Investigating the development characteristics of various-scale pores in deep coalbeds is of great significance for resource assessment and selection of favorable zones for CBM exploration. Herein, six deep coal samples were selected from the Shanxi and Taiyuan Formations in the Daning-Jixian block on the eastern margin of the Ordos Basin. Low-pressure CO2/N2 adsorption (LP-CO2/N2GA) and high-pressure mercury intrusion (HPMI) methods were employed to analyze pore volume, specific surface area, and pore size distribution, thereby evaluating the full-scale pore characteristics. Furthermore, the fractal dimension characteristics of deep coal rock pores were elucidated, revealing the influence of pore structure, burial depth, and coal composition. The results indicate that micropores in deep coal rocks have the highest volume and specific surface area proportions, while mesopores have the smallest volume proportion, and macropores make the least contribution to the total specific surface area. The V-S, Frenkel-Halsey-Hill, and Sierpinski models were suitable for calculating the fractal dimensions of micropores, mesopores, and macropores with LP-CO2GA, LP-N2GA, and HPMI experimental data, respectively. Other than the relatively smaller mesopore fractal dimension of samples 20-8 and 20-10, the micropore, mesopore, and macropore fractal dimensions successively increased in the other four samples. The comprehensive fractal dimension, which exhibited a decreasing trend with increasing pore volume and specific surface area, was negatively correlated with burial depth, mineral and moisture contents, and ash and volatile component yields, while it was positively correlated with vitrinite and fixed carbon contents.

Beyond the Meso/Macroporous Boundary: Extending Capillary Condensation-Based Pore Size Characterization in Thin Films Through Tailored Adsorptives.

The characterization of thin films containing nanopores with diameters exceeding 50 nm poses significant challenges, especially when deploying sorption-based techniques. Conventional volumetric physisorption or mercury intrusion methods have limited applicability in thin films due to constraints in sample preparation and nondestructive testing. In this context, ellipsometric porosimetry represents a viable alternative, leveraging its optical sensitivity to thin films. With existing setups relying on the capillary condensation of volatile compounds such as water, applicability is typically restricted to pore dimensions <50 nm. In this study, we introduce two high-molar-mass hydrocarbon adsorptives, namely ethylbenzene and n-nonane. These adsorptives exhibit substantial potential in improving the accuracy of physisorption measurements beyond mesoporosity (i.e., >50 nm). Specifically, with n-nonane, applicability is extended up to 80 nm pores. Our measurement guidelines propose a nondestructive, expeditious (<60 min), low-pressure (<0.03 bar) approach to investigate nanoporous thin films with potential adaptability to diverse structural architectures.

Effects of SEM image resolution on organic pore structure estimation of shale reservoirs

SEM images on ion-polished shale samples can visualize organic pores and can be used to analyze the organic pore structures quantitatively. Compared to the routine fluid intrusion methods (e.g., helium porosity, N2 adsorption, mercury intrusion, etc.), the SEM image-based organic pore structure analysis has many unique benefits. However, image resolution can significantly influence the analysis results. This should be investigated carefully. The image resolution effects on the analysis of organic pore structures was confirmed and estimated through (1) imaging organic matters and their organic pores at different resolutions (0.7–29.2 nm/pixel), and (2) comparing the differences of 2D organic porosity, pore size distribution (PSD), and organic pore geometry calculated at different image resolutions. The two ways of image resolution effect on organic pore analysis was revealed. First, organic pores with a wall close to the sample surface were more challenging to recognize at low image resolution. Any incorrect recognition of these organic pores can significantly affect the calculated 2D organic porosity and the sectional area-based PSD. Second, the small size organic pores were also difficult to recognize at low image resolution, considerably affecting the estimation of pore amount-based PSD but only slightly affecting the calculated organic porosity and sectional area-based PSD due to their small pore size. When SEM image resolution is less than 10nm/pixel, organic porosity will be significantly underestimated, and the estimated PSD and geometry usually have high uncertainty. Therefore, SEM images with 5nm/pixel or higher resolution are recommended to assess organic pore structures.

An Optimized Hybrid Deep Intrusion Detection Model (HD-IDM) for Enhancing Network Security

Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments.

РОЗРОБКАМОДЕЛІ ФОРМАЛІЗАЦІЇ ПРОЦЕСУ ВИЯВЛЕННЯ ІНСАЙДЕРСЬКИХ КІБЕРЗАГРОЗ У БАНКАХ: ОНТОЛОГІЧНИЙ ПІДХІД

The article is devoted to developing an ontological model of the formalization of detecting insider cyber threats for banking institutions. This model's general requirements and criteria, elements and stages of construction were defined. As part of the model implementation, the structure of classes, relationships between them, characteristics of rules and restrictions, states of objects, temporal and spatial aspects, and aspects of authentication, access control, encryption and other areas of cyber security were proposed. In the modern realities of constantly growing cyber threats, developing effective means of their detection and countermeasures becomes a critical task for ensuring the cyber security of banking systems. The specificity of banking, where sensitive financial information is at constant risk, makes this problem particularly relevant. This is especially felt when solving tasks related to abuse of trust by internal users, so identifying insider threats becomes a significant challenge for banks. The difficulty of their detection is determined by several factors, such as the internal privileges of individuals with access to confidential information, as well as various traffic and intrusion methods. Therefore, this article aims to create a model for formalising the process of detecting insider cyber threats in banks based on an ontological approach. The development of an ontological model seeks to analyse and standardise knowledge about insider threats to improve the possibilities of their timely detection and reduce the reaction time to security events. Applying such an approach allows us to systematise and standardise knowledge about insider threats, defining relationships between concepts and objects in this context. As part of the research goal, the general requirements and criteria, elements and stages of building an ontological model of the formalisation of the process of detecting insider cyber threats in banks were determined. The structure of classes, relationships between them, characterisation of rules and restrictions, states of objects, temporal and spatial aspects, and aspects of authentication, access control, encryption and other areas of cyber security were proposed. The developed model is designed to solve the problems of identification, analysis and response to insider threats in the banking sector, contributing to increasing cyber security and responsibility in this crucial sector.

An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today’s network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model’s validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall.

The Effects of Intrusion of Anterior Teeth by Skeletal Anchorage in Deep Bite Patients; A Systematic Review and Meta-Analysis.

Deep bite is known as one of the most common malocclusions, and its treatment and retention are often challenging. The use of mini-screws has been suggested as an ideal method for the intrusion of incisors in deep-bite patients. Still, there are conflicting reports regarding the superiority of this method compared to other common treatments. The aim of this systematic review and meta-analysis was to evaluate the effects of the intrusion of anterior teeth by skeletal anchorage in deep bite patients. From the beginning to 15 September 2022, articles on the topic of interest were searched in electronic databases including PubMed, Web of Science, Scopus, EMBASE, and Cochrane's CENTRAL. Additionally, a hand search for pertinent studies and a search of the grey literature were carried out. After the selection of eligible studies, data extraction was performed using piloted forms. Inverse-variance random-effects meta-analyses were used to combine the outcome measures of dental indices, skeletal cephalometric indices, and dental cephalometric indices. A total of 15 studies (6 RCT; 9 CCT) were included in the systematic review and 14 were used in the meta-analyses. The differences in overbite changes (MD = -0.45, p = 0.04), true incisor intrusion [u1-pp] (MD = -0.62, p = 0.003) and molar extrusion [u6-pp] (MD = -0.40, p = 0.01) were statistically significant and TADs showed better treatment results than other intrusion methods (segmented intrusion arch, utility arch, J hook headgear). No significant differences regarding overjet, molar and incisor tipping, and skeletal indices between mini-screw and other intrusion methods could be found. The use of mini-screws leads to lower overbite and higher true intrusion (about 0.45 and 0.62 mm, respectively) compared to the use of other methods for intruding upper incisors. Furthermore, the effect of TAD on extrusion of molar teeth is less (by 0.4 mm) than other methods.

Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

Cloud Computing (CC) provides data storage options as well as computing services to its users through the Internet. On the other hand, cloud users are concerned about security and privacy issues due to the increased number of cyberattacks. Data protection has become an important issue since the users’ information gets exposed to third parties. Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools. Intrusion Detection Systems (IDSs) can be used in a network to manage suspicious activities. These IDSs monitor the activities of the CC environment and decide whether an activity is legitimate (normal) or malicious (intrusive) based on the established system’s confidentiality, availability and integrity of the data sources. In the current study, a Chaotic Metaheuristics with Optimal Multi-Spiking Neural Network-based Intrusion Detection (CMOMSNN-ID) model is proposed to secure the cloud environment. The presented CMOMSNN-ID model involves the Chaotic Artificial Bee Colony Optimization-based Feature Selection (CABC-FS) technique to reduce the curse of dimensionality. In addition, the Multi-Spiking Neural Network (MSNN) classifier is also used based on the simulation of brain functioning. It is applied to resolve pattern classification problems. In order to fine-tune the parameters relevant to the MSNN model, the Whale Optimization Algorithm (WOA) is employed to boost the classification results. To demonstrate the superiority of the proposed CMOMSNN-ID model, a useful set of simulations was performed. The simulation outcomes inferred that the proposed CMOMSNN-ID model accomplished a superior performance over other models with a maximum accuracy of 99.20%.

Abnormal Traffic Prediction and Classification based on Information Big Data

Intrusion Detection System (IDS) is a proactive security technique for detecting and alerting suspicious signals. However, the intrusion method developed as a fast and traditional method for detecting malicious traffic has a lot of shortcomings like low accuracy and low efficiency. To determine the different intrusion methods' features and promote the accuracy of malicious traffic detection, several Machine Learning models for classifying different intrusion methods such as KNN, Naive Bayes, SVM, LightGBM are compared. To further improve the accuracy of the model, ensemble models like Voting, Stacking for comparison are also introduced. Grid Search is used for the best parameters. The accuracy, precision, recall score and F1 score are used as metrics to evaluate the performances of different models. The experimental comparison and analysis show that the integrated learning algorithm based on Stacking has the highest accuracy for malicious traffic detection.

Network Intrusion Detection Method Based on Improved CNN in Internet of Things Environment

In view of most existing intrusion detection technologies that cannot meet the actual needs of the Internet of Things and facing the problems of poor detection effect of complex network intrusion methods, a network intrusion detection method based on deep learning algorithm in the environment of the Internet of Things is proposed. Firstly, the Internet of Things intrusion detection model is constructed based on edge computing, in which the concept of gated convolution is introduced to improve the convolution neural network model. Data passes through convolution layer, pooling, dropout, full connection, and Softmax function to realize multiclassification. Finally, the Focal Loss function is used to modulate the training ratio of positive and negative samples to solve the problem of uneven distribution of sample data. The proposed algorithm is demonstrated experimentally based on KDD99 data set. The results show that the accuracy, precision, recall, and F1 values are 92.14%, 95.97%, 90.89%, and 90.03%, which are better than other comparison algorithms. The proposed method can better meet the needs of Internet of Things intrusion detection.

Graphene oxide-based a network porous poly (trially isocyanurate-co-methacrylate) monolithic column for HPLC separation of aromatic molecular and lipopeptide antibiotics

Graphene oxide (GO) was introduced into a monolithic column and a network porous poly (GO-co-TAIC-co-MMA) monolith was prepared by redox polymerization. The internal morphology and pore size distribution of the polymer were observed by scanning electron microscopy, and the nitrogen adsorption-desorption and mercury intrusion methods. After optimization, 8 kinds of aromatic compounds were effectively separated in 5 min, and the theoretical plates number of the monolithic column exceeded 33, 070 plates m−1. Five kinds of main ingredients were separated from the traditional Chinese medicine (Schisandra) ingredients and 26 peaks were successfully separated from the fermentation broth containing natural lipopeptide antibiotics. The addition of GO material enhanced the interaction between the compound and the monolithic column, increased the binding sites, improved the uniformity of the internal pore structure of the monolithic column, and improved the separation performance of the monolith. Methodologic validation of five ingredients in Schisandra showed that the correlation coefficients of the linear regressions were in the range of 0.9987–0.9997. The intra- and inter-day values of the relative standard deviation for precision were in the range of 0.6–4.1% and 1.1–4.8%, respectively. The values of accuracy (expressed as recovery) were in the range of 97.7–103.2%, 100.5–105.0%, 98.2–101.8%, 101.3–104.1%, and 101.2–103.3% for the 5 ingredients in order. In terms of the relative standard deviation of the retention time, the reproducibility of the monolithic column M1 was <3.7%. The monolithic column based on GO has great potential in chromatographic separation.

Synthesis of light olefins from syngas catalyzed by supported iron-based catalysts on alumina

The modified alumina-supported iron-based catalysts for the direct synthesis of light olefins from syngas were prepared by incipient wetness impregnation method and tested in a fixed-bed reactor. The catalysts were characterized by H2-TPR, NH3-TPD, TEM, N2-physical adsorption, mercury intrusion and XRD methods. And the effects of calcination temperature of alumina supporter, iron loadings, S precursor, S/Fe molar ratio, Na/Fe molar ratio and reaction conditions on the catalytic performance were investigated. Experimental results showed that the change in calcination temperature of alumina supporter had significant influences on the structure properties and catalytic performances of the catalysts, and when the calcination temperature of alumina supporter was 1200 ºC, the catalyst exhibited the optimal catalytic performance with a CO conversion of 93.5%, a CO2 selectivity of 24.7%, a C2−4 olefins selectivity of 34.1% and an olefins to paraffins ratio in C2–4 fraction of 6.7. Below 1200 ºC, strong metal-supporter interaction and surface acidity impair the catalytic performance of the catalysts; above 1200 ºC, the catalyst exhibited a poor stability. Moreover, all the S precursors were found with the ability to increase the light olefins selectivity but decrease the activity. The influence degree of these S precursors is following the order: Na2S > Na2S2O3 > Na2SO3 > Na2SO4. The synergistic effect of Na and S promoters can optimize the catalytic performance of the catalyst. Under the reaction conditions of 340 ºC, 2.0 MPa, and 4000 h−1, the 1200 ºC-calcined alumina supported iron-based catalyst with a Na/Fe molar ratio of 0.8 and a S/Fe molar ratio of 0.02 exhibited the best catalytic performance with a CO conversion of 90.3%, a CO2 selectivity of 27.9%, a light olefins selectivity of 47.8% and an olefins to paraffins ratio in C2–4 fraction of 9.2.

The influence of calcium hydroxide on the performance of MK-based geopolymer

Zeolite Socony Mobile-5 (ZSM-5) micropores are blocked after repeated use; therefore, their performance is compromised, forming ZSM-5 waste. This work synthesized a new type of multiscale pore structured geopolymer that could be exploited as a zeolite material precursor. The raw materials were metakaolin, calcium hydroxide, and ZSM-5 waste. The action of ZSM-5 waste is synergistic foaming with hydrogen peroxide and as an extra source of silicon in geopolymer generation. The final geopolymer samples were analysed via mechanical performance tests, thermogravimetry, X-ray diffraction, FT-IR spectral analysis, scanning electron microscopy and mercury intrusion methods. The influence of adding calcium hydroxide on the properties of the products was investigated. Metakaolin was replaced by 0, 10, 20 and 30 g of calcium hydroxide. The geopolymer specimens with added calcium hydroxide showed a more compact structure, a higher bulk density and an enhanced compressive strength performance. Moreover, the incorporation of calcium hydroxide increases the geopolymer paste viscosity and limits the foaming action of ZSM-5 waste and hydrogen peroxide, which makes the geopolymer product structure denser. The gel product was significantly associated with the added calcium hydroxide, and the amount of gel product, mainly hydrated calcium aluminosilicate gels (C-(A)-S-H), increased with the incorporation of calcium hydroxide. In general, calcium hydroxide accelerates the geopolymerization process and has a positive impact on the performance of geopolymer products. This study described the influence of calcium hydroxide on multiscale pore structure metakaolin-based geopolymers, and the results can be considered as a reference for the application of multiscale pore structure geopolymers with calcium hydroxide.

Experimental research on different metamorphic grades of coal bodies with macro–mesoscopic structure fractal characteristics

This paper studies the Daliuta mine, Xinglongzhuang mine, Qingdong mine and Qincheng mine. Here, nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) were performed on different metamorphic grades of coal samples from 4 main mining areas in China. The coal body fissure–pore structure parameters were measured in the range of 100 nm∼5000 nm, and the fractal characteristics were obtained for the different metamorphic grades of coal bodies at macro–meso-structure scales, revealing the influence of reservoir formation on the complexity of the coal body structure. The results show that with the increase in metamorphic degree, the ratio of total pore volume occupied by pores and macropores in coal samples gradually decreases, while the volume ratio of transition pores and micropores increases; in addition, the influence of coal reservoir formation processes on fissure–pore structure parameters of coal is obtained. Based on computer graphics theory and fractal geometry theory, the relationship between the 3D volume fractal dimension of the coal bodies, as measured by NMR, MIP and other fluid intrusion methods, and the box-dimension statistical method of SEM to measure the surface fractal dimension is analyzed. The 3D volume fractal dimension calculated by NMR and MIP is consistent with the change in metamorphism degree; that is, with the increase in metamorphism degree of the coal sample, the 3D volume fractal dimension increases. In addition, the 3D volume fractal dimension measured by NMR is larger than that measured by the MIP; the difference between the 3D volume fractal dimension measured by NMR and 2D surface fractal dimension measured by SEM is approximately 1. Here, the influence of reservoir formation on the characteristics of coal structure fractal is obtained. The research results provide important scientific guidance for enhancing coalbed methane recovery and mine disaster prevention.

Parameter estimation from spontaneous imbibition into volcanic tuff

AbstractTwo‐phase fluid flow properties underlie quantitative prediction of water and gas movement, but constraining these properties typically requires multiple time‐consuming laboratory methods. The estimation of two‐phase flow properties (van Genuchten parameters, porosity, and intrinsic permeability) is illustrated in cores of vitric nonwelded volcanic tuff using Bayesian parameter estimation that fits numerical models to observations from spontaneous imbibition experiments. The uniqueness and correlation of the estimated parameters is explored using different modeling assumptions and subsets of the observed data. The resulting estimation process is sensitive to both moisture retention and relative permeability functions, thereby offering a comprehensive method for constraining both functions. The data collected during this relatively simple laboratory experiment, used in conjunction with a numerical model and a global optimizer, result in a viable approach for augmenting more traditional capillary pressure data obtained from hanging water column, membrane plate extractor, or mercury intrusion methods. This method may be useful when imbibition rather than drainage parameters are sought, when larger samples (e.g., including heterogeneity or fractures) need to be tested that cannot be accommodated in more traditional methods, or when in educational laboratory settings.

Systematic Literature Review over IDPS, Classification and Application in its Different Areas

Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.

Comparison of cephalometric changes resulting from different upper incisor intrusion methods

Aim: The aim of this study was to provide intrusion of upper incisors with applying Connecticut Intrusion Arch (CIA) and Miniscrew and to evaluate the dental and skeletal cephalometric effects of these intrusion methods on individuals with deep bite caused by supraocclusion of upper incisors.&#x0D; Methodology: The study includes 40 adults, without making sexual distinction, who have at least 4 mm deep bite caused by supraocclusion of upper incisors. Two study groups each consisting 20 individuals formed as CIA and Miniscrew groups. Skeletal, dental, soft tissue measurements were done on lateral cephalograms and apical root resorption measurements were done on standard periapical radiographs that were taken from upper four incisor teeth. Statistically, Paired Student’s t-test was used in intragroup comparements and independent Student’s t-test was used in the investigation of differences between groups. Nevertheless, the concern of variables that seen as risk factors with the amount of resorption was investigated with Pearson correlation analysis. &#x0D; Results: Successful intrusion of four upper incisor teeth with CIA and Mini screw methods and in-significant difference was determined between two methods. Protrusion of upper and lower incisor teeth decrease in interincisal angle and overbite and increase in overjet was stated by intrusion at both of the methods. The decline of the mesiobuccal cusp of the upper first molar was observed in the CIA method. In soft tissue evaluation, decrease of upper lip length, upper lip thickness and distance of upper and lower lip to the Rickett’s plane was observed.&#x0D; Conclusion: The methods used for intrusion showed to cause similar ratio of root resorption.&#x0D; &#x0D; How to cite this article: Süer Tümen D, Hamamcı O. Comparison of cephalometric changes resulting from different upper incisor intrusion methods. Int Dent Res 2021;11(Suppl.1):177-94. https://doi.org/10.5577/intdentres.2021.vol11.suppl1.26&#x0D; &#x0D; Linguistic Revision: The English in this manuscript has been checked by at least two professional editors, both native speakers of English.