Intrusion Problems Research Articles

Those Infinite, Multiform Stories without Fixity: Myth and History, a Very Long Engagement

This paper deals with the problematic relationship between ancient historiography and mythology, or mythological stories as historical sources for historical research. Although Greek historiography had, in a sense, already been confronted with the problem of mythological intrusions into the substance of methodologically well-developed historical procedures (as early as the 5th century BC), Roman historiography continued for a long time to critically investigate and purify the stories of the foundation myths. This paper presents a detailed analysis of some methodologically relevant passages that reveal the attitudes of individual Greek and Roman historiographers towards myth as a (non-)source. At the same time, it questions the attitude of (early) Roman historiography towards myth by examining the philosophical treatise of the 2nd/3rd century philosopher Sextus Empiricus.

Swarm learning anomaly detection framework for cloud data center using multi-channel BiWGAN-GTN and CEEMDAN

Anomaly detection is an important task for maintaining the performance of cloud data center. Traditional anomaly detection primarily examines individual Virtual Machine (VM) behavior, neglecting the impact of interactions among multiple VMs on Key Performance Indicator (KPI) data,e.g., memory utilization. Furthermore, the non-stationarity, high complexity, and uncertain periodicity of KPI data in VM also bring difficulties to deep learning-based anomaly detection tasks. To settle these challenges, this paper proposes MCBiWGAN-GTN, a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network (BiWGAN-GTN) and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). (a)The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data. (b) The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process. (c) MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components. (d) To adapt the proposed algorithm for the entire cloud data center, a cloud data center anomaly detection framework based on Swarm Learning (SL) is designed. The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline, with an F1-score of 0.96, an accuracy of 0.935, a precision of 0.954, a recall of 0.967, and an FPR of 0.203. The experiments also verify the stability of MCBiWGAN-GTN, the impact of parameter configurations, and the effectiveness of the proposed SL framework.

WSUD Techniques for Urban Development with Focus on Saltwater Intrusion Problem, A Case Study of Kedonganan Village - Bali

Water is one of the most important resources in human’s life, and groundwater known as the largest freshwater source in the world. Along the development of the cities and population growth, the demand for water supply is also increasing. With the increasing of the freshwater needs, groundwater is also widely used for human’s daily activities. However, the irresponsible use and exploration of groundwater also certainly generate many problems, one of those is saltwater intrusion. Many coastal areas in Indonesia are starting to face saltwater intrusion problem which cause groundwater is no longer be used properly by the community. Therefore, the problem of saltwater intrusion needs to be addressed. This research aims to see the WSUD techniques which can be applied to prevent saltwater intrusion in coastal areas with Kedonganan Village as a case study. The research methodology used is descriptive qualitative method. For the result of this research, those are some WSUD techniques which can be applied in urban development to prevent saltwater intrusion occurs: rainwater tank, downpipe diverters, biofilters, infiltration basins & trenches, soakwells, swales & buffer strips, and pervious paving which have focus on accelerate infiltration into the aquifer.

The Potential of Cynodon Dactylon and Lolium Perenne 'Brightstar' as Phytoremediator Agents in Dealing with the Problem of Sea Water Intrusion in the North Coastal Area of Karawang

The Potential of Cynodon Dactylon and Lolium Perenne 'Brightstar' as Phytoremediator Agents in Dealing with the Problem of Sea Water Intrusion in the North Coastal Area of Karawang

Open-set adversarial domain match for electronic nose drift compensation and unknown gas recognition

In practical applications, electronic noses (EN) must tackle not only sensor drift but also resist interference from unknown gases. In prolonged open environments, electronic noses often encounter unknown gases that cannot be predicted in advance. Current gas detection methods typically rely on prior knowledge of target analytes but struggle to comprehensively address sensor drift and interference from unknown gases. Such as indoor air quality monitoring, long-term stability is crucial, and not all relevant analytes can be predetermined. However, the recent research cannot solve both the sensor drift and unknown gas intrusion problem simultaneously well. In this work, we unify above problems in to the open-set risk boundary. We propose an open-set adversarial domain match (OSADM) model and introduce the considers of open-set domain adaptation (OSDA). OSDA trains a target classifier through matching the domain distribution to recognize the known and unknown gases. First, a binary adversarial loss divides the class boundary. Secondly, adversarial domain adaptation unifies the distribution of different domains. Compared with the metric methods, it avoids complex distribution computation and parameter adjustment to reduce negative transfer. Extensive experimental results on two benchmark datasets, Gas Sensor Array Drift and Twin gas sensor arrays Dataset show that OSADM outperformance of the existing open-set models.

Power System to Prevent External Damage Detection Method

An improved YOLOv5s multi-scale target detection algorithm is proposed to address the increasingly threatening problem of foreign object intrusion in power transmission lines, which poses a risk to the safety and stable operation of the power grid. To enhance the model's ability to extract features of different sizes, a new C3 feature extraction module was designed. It uses deformable convolutional networks to replace the original fixed convolutional layers, and then introduces the ECA attention mechanism to take into account global context information, reducing the decrease in model detection accuracy caused by feature loss. An improved form of NWD fusion Wise IoU is proposed as the network's predicted box regression loss function, improving the model's generalization ability and its ability to detect features of small targets, and achieving faster network convergence speed. Experimental results show that the improved algorithm has increased the accuracy P, recall rate R, and average precision mAP by 5.21%, 4.71%, and 6.24%, respectively, compared to the original YOLOv5s model in detecting foreign object intrusion in power transmission lines, while also enhancing the detection performance of small targets.

A lattice Boltzmann model for incompressible gas and liquid two-phase flows combined with free-surface method

A novel lattice Boltzmann (LB) model is proposed to study the gas and liquid two-phase flows with large density and viscosity ratios. In the model, both the gas and liquid phases are considered as viscous incompressible fluids, which are governed separately by the two-relaxation-time LB equations. They are coupled by a momentum exchange method at the interface. The interaction between the gas and liquid phases is explicitly described and naturally involved in the model. The interfacial conditions in the model are validated by the benchmarks of the layered Poiseuille flow and the Laplace law. The feasibility of combining this model with the bubble model and the wetting scheme is proven through transient flow problems of single bubble rising and capillary intrusion. The validity of this model is confirmed by more complex flows including solid–liquid–gas coupling and droplet breaking problems by simulating shearing a droplet on a substrate and a droplet falling on a liquid film. The results demonstrate that the present model can be used to describe both the gas and the liquid flows. This work provides a solution to model the simulation of the dynamical behaviors of multi-phase flows.

Uncertainty quantification in the Henry problem using the multilevel Monte Carlo method

We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random fields. The classical deterministic Henry problem is non-linear and time-dependent, and can easily take several hours of computing time. Uncertain settings require the solution of multiple realisations of the deterministic problem, and the total computational cost increases drastically. Instead of computing of hundreds random realisations, typically the mean value and the variance are computed. The standard methods such as the Monte Carlo or surrogate-based methods are a good choice, but they compute all stochastic realisations on the same, often, very fine mesh. They also do not balance the stochastic and discretisation errors. These facts motivated us to apply the MLMC method. We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs. To reduce the computing cost further, parallelization is performed in both physical and stochastic spaces. To solve each deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion.

Advances in Modeling Dense Granular Media

This review focuses on how the modeling of dense granular media has advanced over the last 15 years. The jumping-off point of our review is the μ( I) rheology for dry granular flow, which opened the door to generic flow field modeling but was primarily geared toward problems involving small monodisperse grains of simple shapes. Our review focuses on advances in modeling more material types and behaviors including new approaches for modeling finite-grain-size effects or nonlocality, polydispersity and unmixing, and nontrivial grain shapes. We also discuss growing application areas with tractable order-reduction strategies with a focus on intrusion and locomotion problems.

Machine learning enabled system for intelligent classification of host-based intrusion severity

Intrusion severity classification or the analysis of the impact of intrusion is a much needed solution to effectively manage intrusion events in an organization. A lot of intrusion scenarios have been carried out by systems administrators or the internal workers over the years in different organizations and the external hackers are berated for it. Many deliberate inversions have happened from the internal actors with top management board members only swinging into actions to manage the effect of it without digging into the inversion to apprehend the actors or the source of the intrusion. So, this work has been designed to assist IT firms to effectively carry out the analysis of the impact of intrusion, especially those from the internal workers. In this work, we proposed a Machine Learning Enabled System for Intelligent Classification of Host-based Intrusion Severity. The proposed model is aimed at detecting the severity of intrusion problems, carryout source analysis and give security recommendation for effective management of intrusion problems. The model is divided into three phases; the detection of intrusion severity, source analysis and security recommendation using counterfacatual reasoning.We built a system that aided us to gather user interaction over time, we captured these interaction in the activity log, our dataset was extracted from these activity log data.We used Bayesian Network to design the intrusion severity classification system, source analysis is carried out immediately, then counterfactual model is employed to give security recommendation. The accuracy of Bayesian Network in the intrusion severity classification model is 82%. An API was generated and deployed to allow scalability.

Uncertainty quantification in coastal aquifers using the multilevel Monte Carlo method

AbstractWe are solving a problem of salinisation of coastal aquifers. As a test case example, we consider the Henry saltwater intrusion problem. Since porosity, permeability and recharge are unknown or only known at a few points, we model them using random fields and random variables. The Henry problem describes a two‐phase flow and is non‐linear and time‐dependent. The solution to be found is the expectation of the salt mass fraction, which is uncertain and time‐dependent. To estimate this expectation, we use the well‐known multilevel Monte Carlo (MLMC) method. The MLMC method takes just a few samples on computationally expensive (fine) meshes and more samples on cheap (coarse) meshes. Then, by building a telescoping sum, the MLMC method estimates the expected value at a much lower computational cost than the classical Monte Carlo method. The deterministic solver used here is the well‐known parallel and scalable ug4 solver.

Improving Salt Leaching in a Simulated Saline Soil Column by Compost and Biochar in Vietnamese Mekong Delta

The problem of seawater intrusion in recent years has been serious in the coastal provinces of the Vietnamese Mekong River Delta. For rice cultivation in salt-affected areas, more effective techniques are required to remediate the saline soils for lowing salinity to secure rice growth and productivity. The objective of this study was to evaluate the potential reclamation of compost and biochar in laboratory experiment using a saline soil sample from the rice cropping system. Our hypothesis was that the addition of compost and biochar might improve the infiltration rate, resulted in more effectively salts removing from saline soil. The experiment was set up with compost and biochar at rates of 5 g kg-1, 10 g kg-1 and 20 g kg-1, respectively and 10 g kg-1 compost combined with 10 g kg-1 biochar. Soil chemical characteristics such as amount of Na+, K+, Ca2+, and Mg2+ cations removed from soil into eluent by leaching, soil availabe N, P after finishing leaching were analyzed. Results indicated that all compost and biochar amendments enhanced Na+ leaching process. This study proved that material amendment such as 20g kg-1 biochar and 10g kg-1 compost plus 10g kg-1 biochar could speed up water infiltration. Compost additions increase the available N and P content in the soil after leaching. Available N and P contents have not been improved in treatments supplemented with biochar.

N-STGAT: Spatio-Temporal Graph Neural Network Based Network Intrusion Detection for Near-Earth Remote Sensing

With the rapid development of the Internet of Things (IoT)-based near-Earth remote sensing technology, the problem of network intrusion for near-Earth remote sensing systems has become more complex and large-scale. Therefore, seeking an intelligent, automated, and robust network intrusion detection method is essential. Many researchers have researched network intrusion detection methods, such as traditional feature-based and machine learning methods. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-Earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the factors of existing near-Earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes and applies them to the network intrusion detection of near-Earth remote sensing systems. Finally, the proposed method in this article is validated using the latest flow-based datasets NF-BoT-IoT-v2 and NF-ToN-IoT-v2. The results demonstrate that the binary classification accuracy for network intrusion detection exceeds 99%, while the multi-classification accuracy exceeds 93%. These findings provide substantial evidence that the proposed method outperforms existing intrusion detection techniques.

Multiresolution Discriminative Mixup Network for Fine-Grained Visual Categorization.

Fine-grained visual categorization (FGVC) is a challenging task because there are many hard examples existing between fine-grained classes which differ subtly in particular local regions. To address this issue, many methods have recourse to high-resolution source images and others adopt effective regularization like "mixup" or "between class learning." Despite their promising achievements, mixup tends to cause the manifold intrusion problem which would result in under-fitting and degradation of the model performance and high-resolution input inevitably leads to high computational costs. In view of this, we present a multiresolution discriminative mixup network (MRDMN). Different from standard mixup, the proposed discriminative mixup strategy mixes discriminative regions linearly instead of entire images to avoid manifold intrusion, which makes it learn the local detail features more effectively and contributes to more precise categorization. Furthermore, an innovative resolution-based distillation strategy is designed to transfer the multiresolution detail feature representations to a low-resolution network, which speeds up the testing and boosts the categorization accuracy simultaneously. Extensive experiments demonstrate that our proposed MRDMN remarkably outperforms most competitive approaches with less computation time on the CUB-200-2011, Stanford-Cars, Stanford-Dogs, Food-101, and iNaturalist 2017 datasets. The codes are in https://github.com/aztc/MRDMN.

The impact of buildup proposed barrage on tidal hydrodynamics in the Shatt Al-Arab ‎River, South of Iraq

The influence of the suggested regulator barrage of the Shatt Al-Arab river on water level ‎fluctuations was tested by using a one-dimensional model. The numerical model period ‎covered six months in 2017. The performance of the model was evaluated by comparing ‎the estimated water level at two locations, Sihan, and Abu Flous. A good fit between the ‎observed and simulated water levels at the two locations was achieved with root mean ‎square error at 0.13 and 0.06 m, respectively. The model results showed that there is an ‎increase in high water levels near areas located below the barrage in both the spring and ‎neap tidal phases. The increasing of water level below the barrage could be attributed to ‎behave the river as a convergent tidal channel that characterizes by the increasing water ‎level as tidal wave propagated further toward inland areas. The highest increase in the ‎water level reaches about 0.4 m below the barrage. Additionally, the tidal range also ‎increased along the river course. The maximum increase in tidal range reaches about 0.9 m ‎below the barrage. The results obtained could be useful for a general assessment of the ‎hydrodynamic of the Shatt Al- Arab river and could give a general view in the case of the ‎construction of such a barrage to tackle the salinity intrusion problem in this river.

Organisation of Fish Assemblage in the Mekong Delta as a Potential Indicator of Salt Water Intrusion

The problem of salt water intrusion into the Mekong Delta is a research topic involving various branches of science. Among the causes of this phenomenon are global sea level rise and regulation of the natural river discharge due to hydropower dam construction. The scale of the delta and its water dynamics complicate instrument-based observations. Meanwhile, communities of living organisms can serve as indicators of environmental heterogeneity. This study aimed to analyze the spatial variability of fish assemblage organisation in the Mekong Delta for use as an indicator of salt water intrusion. The composition of fish assemblages in different parts of the delta were determined by midwater trawl catches during January and April 2021. A total of ~15 thousand individual fish from 74 trawl hauls were captured and analyzed. The null hypothesis was that the fish assemblages in the Mekong Delta are represented by three taxonomic complexes that are localized in the upper course (freshwater), the lower course (brackish) and the middle course (zone of the contact of the fresh and brackish waters) of the delta. The taxonomic composition of the freshwater complex is the most poor and is represented by 9 families. Meanwhile, marginal (the contact zone) and brackish complexes include 26 and 23 families, respectively. Specimens of families Cobitidae, Eleotridae, Plotosidae and Siluridae that are only found in the zone of contact of fresh and brackish waters could be considered as indicator species of its position. The actual boundaries of the brackish and freshwater complexes were determined and the distance from them to the marine delta edge equals 34 and 78 km, respectively. Assessment of the salinity values that correspond to the complex boundaries is a task for future investigation. The position of the boundaries of freshwater and brackish fish taxonomic complexes has a biological basis and could be used as an indicator of salt water intrusion into the Mekong Delta.

Adaptive Intrusion Detection in Edge Computing Using Cerebellar Model Articulation Controller and Spline Fit

Internet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices’ connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS)</i> . Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm.

A Systematic Method to Investigate the EOR Mechanism of Nanospheres: Laboratory Experiments from Core to Micro Perspective

Nanospheres have been used as an available plugging material to solve the problem of water intrusion, which is of crucial importance to enhanced oil recovery (EOR). However, the microflow of nanospheres in a low-permeability reservoir is complex, and its plugging performance and EOR mechanism need to be further investigated by a variety of experiments. In this paper, the expansion experiments were given priority to explore the water absorption and salt tolerance of nanospheres. The displacement experiments were conducted to investigate the plugging performance of nanospheres at the core scale. The flow characteristic and EOR mechanism of nanospheres at the microscale were revealed by nuclear magnetic resonance (NMR) experiments and microfluidic experiments. Expansion and displacement experiments results demonstrate that high salinity inhibits the expansibility and agglomeration of nanospheres, and the formation damage and plugging performance of nanospheres should be comprehensively considered. The suitable particle size of nanospheres should be optimized for field application. As for the microscale, NMR experiments revealed that nanospheres would first enter the large pore and then enter the middle and small pores. In microfluidic experiments, the oil recovery increased by 23.02% original oil in place after the injection of nanospheres. The systematic and comprehensive investigations of expansion property, plugging performance, flow characteristic, and EOR mechanism of nanospheres were conducted from core to microscale, which yielded significant insights into preventing water intrusion and enhancing oil recovery.

The Role of Machiavellianism and Interdependent Agency in Facebook Intrusion.

Facebook is one the most popular social networking sites in the world. The social aspect and perceiving oneself in the context of relationships seem to be crucial in the analysis of Facebook use. In recent years the number of Facebook users has been increasing, which is related to the problem of Facebook intrusion. Machiavellianism and selected interdependence agency indicators (conformity, relatedness, and autonomy) are associated with social functioning, which is an important aspect of Facebook intrusion. The aim of the study was to check how Machiavelianism and the interdependent agency indicators mentioned above affect Facebook intrusion and whether Facebook intrusion decreases satisfaction with life. The participants in the online study were 715 Polish Facebook users. We administered the Facebook Intrusion Scale, Conformity, Relatedness, and Autonomy Scales, and the Satisfaction With Life Scale. The results show that the selected interdependence agency indicators predict Facebook intrusion. Our results revealed that a high level of Machiavellianism was related to a high level of Facebook intrusion in women. They also show that conformity is positively and autonomy negatively related to Facebook intrusion and that Facebook decreases reduces satisfaction with life. The presented research thus contributes to knowledge about Facebook usage patterns.

An efficient discontinuous Galerkin - mixed finite element model for variable density flow in fractured porous media

Modeling variable density flow (VDF) in fractured porous media is computationally challenging. The challenges are mainly related to: (i) the high permeability contrast between the matrix and the fractures and, (ii) the nonlinearity induced by density variations. Due to their local mass conservation property, cell-centered methods, such as finite volumes (FV), mixed finite elements (MFE) or Discontinuous Galerkin (DG) methods are well suited for modeling mass transfer in highly heterogeneous domains. When applied for fractured media, these methods require small grid cells next to fractures because of the cross-flow equilibrium assumption (i.e. the pressure and concentration in the fracture and in the adjacent matrix grid-cells are assumed the same). To avoid this constraint, an efficient model is developed in this work using advanced cell-centered numerical methods for VDF in fractured porous media with cross-flow equilibrium assumed only across the fractures. The new model uses the hybrid MFE method for the flow discretization in the matrix and in the fracture continua. Mass transport in the matrix is modeled using a monotonic upwind MFE scheme. Advection-dominated transport in fractures is discretized with the DG method, which is well adapted for hyperbolic equations. The new model ensures continuity of pressure, concentration, fluid mass flux, advective and dispersive contaminant fluxes at matrix-fracture interfaces as well as at the intersection of several fractures. The time discretization is performed using high-order adaptive time integration techniques via the method of lines (MOL). The developed model is first validated by comparison against a 2D-2D model for a test problem dealing with linear flow and transport in a 2D domain involving a “+”-shaped barrier/fracture network. Then, the MFE-DG model is used for the simulation of the Henry saltwater intrusion problem and the results are validated by comparison against the semi-analytical solution in the case of unfractured and fractured aquifers. Finally, the simulation of a transport problem with variable viscosity in a fractured heterogeneous domain points out the superiority of the new model in terms of accuracy and efficiency when compared to a standard finite element model.