Penetration testing plays a critical role in ensuring security in an increasingly interconnected world. Despite advancements in technology leading to smaller, more portable devices, penetration testing remains reliant...Penetration testing plays a critical role in ensuring security in an increasingly interconnected world. Despite advancements in technology leading to smaller, more portable devices, penetration testing remains reliant on traditional laptops and computers, which, while portable, lack true ultra-portability. This paper explores the potential impact of developing a dedicated, ultra-portable, low-cost device for on-the-go penetration testing. Such a device could replicate the core functionalities of advanced penetration testing tools, including those found in Kali Linux, within a compact form factor that fits easily into a pocket. By offering the convenience and portability akin to a smartphone, this innovative device could redefine the way penetration testers operate, enabling them to carry essential tools wherever they go and ensuring they are always prepared to conduct security assessments efficiently. This approach aims to revolutionize penetration testing by merging high functionality with unparalleled portability.展开更多
Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attack...Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.展开更多
In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield w...In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.展开更多
Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the...Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical application.In this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive scale.CLAP employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large networks.It also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between them.Experimental results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 hosts.Additionally,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.展开更多
Cone penetration testing(CPT)and its variant with pore pressure measurements(CPTu)are versatile tools that have been traditionally used for in situ geotechnical site investigations.These investigations are among the m...Cone penetration testing(CPT)and its variant with pore pressure measurements(CPTu)are versatile tools that have been traditionally used for in situ geotechnical site investigations.These investigations are among the most challenging yet indispensable tasks,providing a crucial reference for infrastructure planning,design and construction.However,data obtained through the CPT/CPTu testing often exhibit significant variability,even at closely spaced test points.This variability is primarily attributed to the complex mineral compositions and sedimentary process of the Quaternary sediments.Problems induced by the scattering data include the difficulties in estimating the shear strength of the sediments and determining the appropriate bearing stratum for pile foundations.In this paper,the conventional interpretation methods of the CPT/CPTu data are enhanced with sedimentary facies knowledge.The geotechnical investigation mainly involves 42 CPTu tests(39 essential data sets available)and 4 boring samples.Sediment types are interpreted from the CPTu data and calibrated by the nearby boring samples.Sedimentary facies are derived from the interpreted sequence stratigraphy,for which the interpretation skills are summarized in the form of characteristic curves of the CPTu data.Scattering distribution of the sediment types and their mechanical parameters are well explained by the sedimentary facies.The sediments are then categorized into a few groups by their sedimentary facies,resulting in reduced uncertainties and scattering in terms of shear strength.Bearing stratum of pile foundations is also suggested based on the sedimentary regulations.展开更多
Conventional empirical equations for estimating undrained shear strength(s_(u))from piezocone penetration test(CPTu)data,without incorporating soil physical properties,often lack the accuracy and robustness required f...Conventional empirical equations for estimating undrained shear strength(s_(u))from piezocone penetration test(CPTu)data,without incorporating soil physical properties,often lack the accuracy and robustness required for geotechnical site investigations.This study introduces a hybrid virus colony search(VCS)algorithm that integrates the standard VCS algorithm with a mutation-based search mechanism to develop high-performance XGBoost learning models to address this limitation.A dataset of 372 seismic CPTu and corresponding soil physical properties data from 26 geotechnical projects in Jiangs_(u)Province,China,was collected for model development.Comparative evaluations demonstrate that the proposed hybrid VCS-XGBoost model exhibits s_(u)perior performance compared to standard meta-heuristic algorithm-based XGBoost models.The res_(u)lts highlight that the consideration of soil physical properties significantly improves the predictive accuracy of s_(u),emphasizing the importance of considering additional soil information beyond CPTu data for accurate s_(u)estimation.展开更多
With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement c...With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement curve of uplift piles is crucial for evaluating their uplift bearing characteristics,which facilitates the risk evaluation,design,and construction of large infrastructural supports.In this study,a load-displacement curve model based on piezocone penetration test(CPTU)data is proposed via the load transfer method.Experimental tests are conducted to analyze the uplift bearing characteristics and establish a correlation between the proposed model and CPTU data.The results of the proposed load-displacement curve are compared with the results from numerical simulations and those calculated by previous methods.The results show that the proposed curves appropriately evaluated the uplift bearing characteristics and improved the accuracy in comparison with previous methods.展开更多
The water drop penetration time(WDPT)test consists of placing water drops on a material's surface in order to evaluate how long it takes to penetrate the pores.It is used to evaluate the hydrophobicity of material...The water drop penetration time(WDPT)test consists of placing water drops on a material's surface in order to evaluate how long it takes to penetrate the pores.It is used to evaluate the hydrophobicity of materials.This study aims at investigating in more detail the soil-water interaction during the test,exposing its mechanism.For that,a model soil named Hamburg Sand was coated with a hydrophobic fluoropolymer and then a WDPT test was performed while computed tomography(CT)images were taken.Tomography experiments were performed at the P07 high-energy materials science(HEMS)beamline,operated by Helmholtz–Zentrum Hereon,at the storage ring PETRA III at the Deutsches Elektronen-Synchrotron(DESY)in Hamburg.Using synchrotron radiation,a tomogram can be obtained in about 10 min,way less time than regular laboratory X-ray sources usually owned by universities.The faster imaging enables the observation of the drop penetration during time and thus provides insight into the dynamics of the process.After that,digital discrete image correlation is performed to track the displacement of the grains throughout time.From the results one can observe that,as the drop is absorbed at the material's surface,the grains directly around the droplet base are dragged to the liquid-air interface around the drop,revealing grain kinematics during capillary interactions of the penetrating liquid and sand grains.展开更多
To explore the penetration resistance of calcareous sand media,penetration tests have been conducted in the velocity range of 200-1000 m/s using conical-nosed projectiles with a diameter of 14.5 mm.Further,a pseudo fl...To explore the penetration resistance of calcareous sand media,penetration tests have been conducted in the velocity range of 200-1000 m/s using conical-nosed projectiles with a diameter of 14.5 mm.Further,a pseudo fluid penetration model applicable to the penetration of rigid projectiles in sand media is established according to the approximate flow of compacted sand in the adjacent zone of penetration.The correlation between the impedance function of projectile-target interaction and the internal friction features of pseudo fluid is clarified,and the effects of sand density,cone angle of nose-shaped projectile,and dynamic hardness on the penetration depth are investigated.The results verify the feasibility,wide applicability,and much lower error(with respect to the experimental data)of the proposed model as compared to the Slepyan hydrodynamic model.展开更多
The interpretation of the cone penetration test(CPT)still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers.This paper presen...The interpretation of the cone penetration test(CPT)still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers.This paper presents a CPT virtual calibration chamber using deep learning(DL)approaches,which allow for the consideration of depth-dependent cone resistance profiles through the implementation of two proposed strategies:(1)depth-resistance mapping using a multilayer perceptron(MLP)and(2)sequence-to-sequence training using a long short-term memory(LSTM)neural network.Two DL models are developed to predict cone resistance profiles(qc)under various soil states and testing conditions,where Bayesian optimization(BO)is adopted to identify the optimal hyperparameters.Subsequently,the BO-MLP and BO-LSTM networks are trained using the available data from published datasets.The results show that the models with BO can effectively improve the prediction accuracy and efficiency of neural networks compared to those without BO.The two training strategies yielded comparable results in the testing set,and both can be used to reproduce the whole cone resistance profile.An extended comparison and validation of the prediction results are carried out against numerical results obtained from a coupled Eulerian-Lagrangian(CEL)model,demonstrating a high degree of agreement between the DL and CEL models.Ultimately,to demonstrate the usability of this new virtual calibration chamber,the predicted qc is used to enhance the preceding correlations with the relative density(Dr)of the sand.The improved correlation with superior generalization has an R^(2) of 82%when considering all data,and 89.6%when examining the pure experimental data.展开更多
Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was ...Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was carried out to determinate the coal bursting liability,and the empirical criterion of coal bursting liability was proposed.Moreover,the machine learning method was applied to coal bursting liability determination.Through analyzing the elastic strain energy release and failure time,the residual elastic strain energy release rate index K_(RE)was proposed to evaluate the coal bursting liability.According to the relationship between needle penetration index(NPI),K_(RE)and the critical value of K_(RE),the Needle Penetration Test-based Empirical Classification Criterion(NPT-ECC)was obtained.In addition,four machine learning classification models were constructed.After training and testing of the models,Needle Penetration Test-based Machine Learning Classification Model(NPT-MLCM)was proposed.The research results show that the accuracy of NPT-ECC is 6.66%higher than that of China National Standard Comprehensive Evaluation(CNSCE)according to verification of the coal fragment ejection ratio F.Gridsearch cross validation-extreme gradient boosting(GSCV-XGBoost)has the best prediction performance among all the models,and accuracy,Macro-Precision,Macro-Recall and Macro-F1-score of which were 86.67%,88.97%,87.50%and 87.37%.Based on this,the Needle Penetration Test-based GSCV-XGBoost(NPT-GSCV-XGBoost)was proposed.After comparative analysis and discussion,NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability.展开更多
The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challe...The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.展开更多
Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been st...Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.展开更多
Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail ...Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.展开更多
MICP(Microbially induced calcite precipitation),an environmentally friendly soil improvement technique,has great potential in ocean engineering due to its ability to promote the precipitation of calcium carbonate thro...MICP(Microbially induced calcite precipitation),an environmentally friendly soil improvement technique,has great potential in ocean engineering due to its ability to promote the precipitation of calcium carbonate through microbial activity to enhance the engineering properties of geomaterials.In this study,piezocone penetration test(CPTU)is used to evaluate the effectiveness of MICP treatment in calcareous sand.The change of physical properties(relative density D and total unit weight)of MICP treated calcareous sand is investigated by conducting CPTU on the geomaterials prepared in a series of mini calibration chambers(25 cm×50 cm).Results indicate that CPTU(tip stress,sleeve friction,and porewater pressure)measurements can be used to interpret the physical characteristics of calcareous sand treated with MICP under seawater conditions.Additionally,a relationship between CPTU measurements,physical parameters(relative density D,and total unit weight y)of MICP treated calcareous sand is proposed and calibrated.The findings of the research extend the implementation of in-situ testing techniques such as CPTU towards physical property evaluation of bio-treated geomaterials in ocean environment,and demonstrate the potential of scaling up MICP techniques for broader engineeringapplication.展开更多
Ground improvement has been used on many construction sites to densify granular materials, in other word, to improve soil properties and reduce potential settlement. This work presents a case study of ground improveme...Ground improvement has been used on many construction sites to densify granular materials, in other word, to improve soil properties and reduce potential settlement. This work presents a case study of ground improvement using rapid impact compaction （RIC）. The research site comprises the construction of workshop and depots as part of railway development project at Batu Gajah-Ipoh, Malaysia. In-situ testing results show that the subsurface soil comprises mainly of sand and silty sand through the investigated depth extended to 10 m. Groundwater is approximately 0.5 m below the ground surface. Evaluation of improvement was based on the results of pre- and post-improvement cone penetration test （CPT）. Interpretation software has been used to infer soil properties. Load test was conducted to estimate soil settlement. It is found that the technique succeeds in improving soil properties namely the relative density increases from 45% to 70%, the friction angle of soil is increased by an average of 3°, and the soil settlement is reduced by 50%： The technique succeeds in improving soil properties to approximately 5.0 m in depth depending on soil uniformity with depth.展开更多
In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d c...In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t （CPT） resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts： a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g （GEP） m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel.展开更多
Cone penetration test(CPT)is an appropriate technique for quickly determining the geotechnical properties of lunar soil,which is valuable for in situ lunar exploration.Utilizing a typical coupling method recently deve...Cone penetration test(CPT)is an appropriate technique for quickly determining the geotechnical properties of lunar soil,which is valuable for in situ lunar exploration.Utilizing a typical coupling method recently developed by the authors,a finite element method(FEM)-discrete element method(DEM)coupled model of CPTs is obtained.A series of CPTs in lunar soil are simulated to qualitatively reveal the flow of particles and the development of resistance throughout the penetration process.In addition,the effects of major factors,such as penetration velocity,penetration depth,cone tip angle,and the low gravity on the Moon surface are investigated.展开更多
This was a cohort study of in vitro fertilization(IVF)subjects at the University of Utah,Salt Lake City(UT,USA)utilizing partner sperm.Cycles where both the hamster egg penetration test(HEPT)and semen analysis were pe...This was a cohort study of in vitro fertilization(IVF)subjects at the University of Utah,Salt Lake City(UT,USA)utilizing partner sperm.Cycles where both the hamster egg penetration test(HEPT)and semen analysis were performed within 2 years prior to IVF cycles were stratified into four groups based on a normal or an abnormal HEPT and morphology.The mean conventional and intracytoplasmic sperm injection(ICSI)fertilization rates were calculated in each group.We performed a univariate analysis on the primary outcome comparing clinically interesting subjects.We performed a cost-effectiveness analysis of a policy of HEPT versus universal ICSI in couples with an abnormal morphology.Among patients with a normal HEPT,there was no difference in the mean conventional fertilization rates between those with a normal and an abnormal morphology.There was no difference in the mean conventional fertilization rates between subjects with a normal morphology without a hamster test and those with a normal HEPT without a morphology assessment.In 1000 simulated cycles with an abnormal morphology,a policy of HEPT was cost saving compared to universal ICSI,yet produced similar fertilization rates.The HEPT is similar to the World Health Organization edition 5(WHO-5)morphology in predicting successful conventional fertilization while allowing decreased utilization of ICSI.A policy of HEPT for males with abnormal morphology saves cost in selecting couples for a fertilization method.展开更多
Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineer...Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineering practice,soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests,e.g.cone penetration tests(CPTs),due to the restrictions of time,cost and access to subsurface space.In these cases,liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method,leading to remarkable statistical uncertainty in liquefaction assessment.This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment.To tackle this issue,this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a selfadaptive and data-driven manner.The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling(BCS).Both simulated and real CPT data are used to demonstrate the proposed method.Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.展开更多
文摘Penetration testing plays a critical role in ensuring security in an increasingly interconnected world. Despite advancements in technology leading to smaller, more portable devices, penetration testing remains reliant on traditional laptops and computers, which, while portable, lack true ultra-portability. This paper explores the potential impact of developing a dedicated, ultra-portable, low-cost device for on-the-go penetration testing. Such a device could replicate the core functionalities of advanced penetration testing tools, including those found in Kali Linux, within a compact form factor that fits easily into a pocket. By offering the convenience and portability akin to a smartphone, this innovative device could redefine the way penetration testers operate, enabling them to carry essential tools wherever they go and ensuring they are always prepared to conduct security assessments efficiently. This approach aims to revolutionize penetration testing by merging high functionality with unparalleled portability.
文摘Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.
文摘In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.
基金supported by te Key Research Project of Zhejiang Lab(No.2021PB0AV02)。
文摘Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical application.In this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive scale.CLAP employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large networks.It also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between them.Experimental results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 hosts.Additionally,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.
基金supported by the National Natural Science Foundation of China(Grant Nos.42272328 and 52108356).
文摘Cone penetration testing(CPT)and its variant with pore pressure measurements(CPTu)are versatile tools that have been traditionally used for in situ geotechnical site investigations.These investigations are among the most challenging yet indispensable tasks,providing a crucial reference for infrastructure planning,design and construction.However,data obtained through the CPT/CPTu testing often exhibit significant variability,even at closely spaced test points.This variability is primarily attributed to the complex mineral compositions and sedimentary process of the Quaternary sediments.Problems induced by the scattering data include the difficulties in estimating the shear strength of the sediments and determining the appropriate bearing stratum for pile foundations.In this paper,the conventional interpretation methods of the CPT/CPTu data are enhanced with sedimentary facies knowledge.The geotechnical investigation mainly involves 42 CPTu tests(39 essential data sets available)and 4 boring samples.Sediment types are interpreted from the CPTu data and calibrated by the nearby boring samples.Sedimentary facies are derived from the interpreted sequence stratigraphy,for which the interpretation skills are summarized in the form of characteristic curves of the CPTu data.Scattering distribution of the sediment types and their mechanical parameters are well explained by the sedimentary facies.The sediments are then categorized into a few groups by their sedimentary facies,resulting in reduced uncertainties and scattering in terms of shear strength.Bearing stratum of pile foundations is also suggested based on the sedimentary regulations.
基金funded by the National Science Fund for Distinguished Young Scholars(Grant No.42225206)the National Key R&D Program of China(Grant No.2020YFC1807200)the National Natural Science Foundation of China(Grant No.42072299).
文摘Conventional empirical equations for estimating undrained shear strength(s_(u))from piezocone penetration test(CPTu)data,without incorporating soil physical properties,often lack the accuracy and robustness required for geotechnical site investigations.This study introduces a hybrid virus colony search(VCS)algorithm that integrates the standard VCS algorithm with a mutation-based search mechanism to develop high-performance XGBoost learning models to address this limitation.A dataset of 372 seismic CPTu and corresponding soil physical properties data from 26 geotechnical projects in Jiangs_(u)Province,China,was collected for model development.Comparative evaluations demonstrate that the proposed hybrid VCS-XGBoost model exhibits s_(u)perior performance compared to standard meta-heuristic algorithm-based XGBoost models.The res_(u)lts highlight that the consideration of soil physical properties significantly improves the predictive accuracy of s_(u),emphasizing the importance of considering additional soil information beyond CPTu data for accurate s_(u)estimation.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024M760734)National Science Fund for Distinguished Young Scholars(Grant No.42225206)the National Natural Science Foundation of China(Grant Nos.41877231 and 42072299).
文摘With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement curve of uplift piles is crucial for evaluating their uplift bearing characteristics,which facilitates the risk evaluation,design,and construction of large infrastructural supports.In this study,a load-displacement curve model based on piezocone penetration test(CPTU)data is proposed via the load transfer method.Experimental tests are conducted to analyze the uplift bearing characteristics and establish a correlation between the proposed model and CPTU data.The results of the proposed load-displacement curve are compared with the results from numerical simulations and those calculated by previous methods.The results show that the proposed curves appropriately evaluated the uplift bearing characteristics and improved the accuracy in comparison with previous methods.
基金funding of this research by the German Research Foundation(Deutsche Forschungsgemeinschaft,DFG)in the framework of Research Training Group GRK 2462:Processes in natural and technical Particle-Fluid-Systems at Hamburg University of Technology(TUHH).
文摘The water drop penetration time(WDPT)test consists of placing water drops on a material's surface in order to evaluate how long it takes to penetrate the pores.It is used to evaluate the hydrophobicity of materials.This study aims at investigating in more detail the soil-water interaction during the test,exposing its mechanism.For that,a model soil named Hamburg Sand was coated with a hydrophobic fluoropolymer and then a WDPT test was performed while computed tomography(CT)images were taken.Tomography experiments were performed at the P07 high-energy materials science(HEMS)beamline,operated by Helmholtz–Zentrum Hereon,at the storage ring PETRA III at the Deutsches Elektronen-Synchrotron(DESY)in Hamburg.Using synchrotron radiation,a tomogram can be obtained in about 10 min,way less time than regular laboratory X-ray sources usually owned by universities.The faster imaging enables the observation of the drop penetration during time and thus provides insight into the dynamics of the process.After that,digital discrete image correlation is performed to track the displacement of the grains throughout time.From the results one can observe that,as the drop is absorbed at the material's surface,the grains directly around the droplet base are dragged to the liquid-air interface around the drop,revealing grain kinematics during capillary interactions of the penetrating liquid and sand grains.
基金funded by the National Natural Science Foundation of China(Grant No.12072371)Jiangsu Natural Science Foundation(Grant No.BK20221528)。
文摘To explore the penetration resistance of calcareous sand media,penetration tests have been conducted in the velocity range of 200-1000 m/s using conical-nosed projectiles with a diameter of 14.5 mm.Further,a pseudo fluid penetration model applicable to the penetration of rigid projectiles in sand media is established according to the approximate flow of compacted sand in the adjacent zone of penetration.The correlation between the impedance function of projectile-target interaction and the internal friction features of pseudo fluid is clarified,and the effects of sand density,cone angle of nose-shaped projectile,and dynamic hardness on the penetration depth are investigated.The results verify the feasibility,wide applicability,and much lower error(with respect to the experimental data)of the proposed model as compared to the Slepyan hydrodynamic model.
基金support from the National Natural Science Foundation of China(Grant No.52408356)the China Scholarship Council(CSC).
文摘The interpretation of the cone penetration test(CPT)still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers.This paper presents a CPT virtual calibration chamber using deep learning(DL)approaches,which allow for the consideration of depth-dependent cone resistance profiles through the implementation of two proposed strategies:(1)depth-resistance mapping using a multilayer perceptron(MLP)and(2)sequence-to-sequence training using a long short-term memory(LSTM)neural network.Two DL models are developed to predict cone resistance profiles(qc)under various soil states and testing conditions,where Bayesian optimization(BO)is adopted to identify the optimal hyperparameters.Subsequently,the BO-MLP and BO-LSTM networks are trained using the available data from published datasets.The results show that the models with BO can effectively improve the prediction accuracy and efficiency of neural networks compared to those without BO.The two training strategies yielded comparable results in the testing set,and both can be used to reproduce the whole cone resistance profile.An extended comparison and validation of the prediction results are carried out against numerical results obtained from a coupled Eulerian-Lagrangian(CEL)model,demonstrating a high degree of agreement between the DL and CEL models.Ultimately,to demonstrate the usability of this new virtual calibration chamber,the predicted qc is used to enhance the preceding correlations with the relative density(Dr)of the sand.The improved correlation with superior generalization has an R^(2) of 82%when considering all data,and 89.6%when examining the pure experimental data.
基金supported by the National Natural Science Foundation of China(52225402 and U1910206).
文摘Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was carried out to determinate the coal bursting liability,and the empirical criterion of coal bursting liability was proposed.Moreover,the machine learning method was applied to coal bursting liability determination.Through analyzing the elastic strain energy release and failure time,the residual elastic strain energy release rate index K_(RE)was proposed to evaluate the coal bursting liability.According to the relationship between needle penetration index(NPI),K_(RE)and the critical value of K_(RE),the Needle Penetration Test-based Empirical Classification Criterion(NPT-ECC)was obtained.In addition,four machine learning classification models were constructed.After training and testing of the models,Needle Penetration Test-based Machine Learning Classification Model(NPT-MLCM)was proposed.The research results show that the accuracy of NPT-ECC is 6.66%higher than that of China National Standard Comprehensive Evaluation(CNSCE)according to verification of the coal fragment ejection ratio F.Gridsearch cross validation-extreme gradient boosting(GSCV-XGBoost)has the best prediction performance among all the models,and accuracy,Macro-Precision,Macro-Recall and Macro-F1-score of which were 86.67%,88.97%,87.50%and 87.37%.Based on this,the Needle Penetration Test-based GSCV-XGBoost(NPT-GSCV-XGBoost)was proposed.After comparative analysis and discussion,NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2025JBXT010in part by NSFC under Grant No.62171021,in part by the Project of China State Railway Group under Grant No.N2024B004in part by ZTE IndustryUniversityInstitute Cooperation Funds under Grant No.l23L00010.
文摘The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.
基金the National Natural Science Foundation of China No.61502528.
文摘Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.
文摘Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.
基金funded by the Tsinghua Shenzhen International Graduate School Research Startup Funds[item number:01030100009].
文摘MICP(Microbially induced calcite precipitation),an environmentally friendly soil improvement technique,has great potential in ocean engineering due to its ability to promote the precipitation of calcium carbonate through microbial activity to enhance the engineering properties of geomaterials.In this study,piezocone penetration test(CPTU)is used to evaluate the effectiveness of MICP treatment in calcareous sand.The change of physical properties(relative density D and total unit weight)of MICP treated calcareous sand is investigated by conducting CPTU on the geomaterials prepared in a series of mini calibration chambers(25 cm×50 cm).Results indicate that CPTU(tip stress,sleeve friction,and porewater pressure)measurements can be used to interpret the physical characteristics of calcareous sand treated with MICP under seawater conditions.Additionally,a relationship between CPTU measurements,physical parameters(relative density D,and total unit weight y)of MICP treated calcareous sand is proposed and calibrated.The findings of the research extend the implementation of in-situ testing techniques such as CPTU towards physical property evaluation of bio-treated geomaterials in ocean environment,and demonstrate the potential of scaling up MICP techniques for broader engineeringapplication.
基金Projects(RG148/12AET,RG086/10AET) supported by the UMRG,MalaysiaProject(PS05812010B) supported by the Post Graduate Research Fund,Malaysia
文摘Ground improvement has been used on many construction sites to densify granular materials, in other word, to improve soil properties and reduce potential settlement. This work presents a case study of ground improvement using rapid impact compaction （RIC）. The research site comprises the construction of workshop and depots as part of railway development project at Batu Gajah-Ipoh, Malaysia. In-situ testing results show that the subsurface soil comprises mainly of sand and silty sand through the investigated depth extended to 10 m. Groundwater is approximately 0.5 m below the ground surface. Evaluation of improvement was based on the results of pre- and post-improvement cone penetration test （CPT）. Interpretation software has been used to infer soil properties. Load test was conducted to estimate soil settlement. It is found that the technique succeeds in improving soil properties namely the relative density increases from 45% to 70%, the friction angle of soil is increased by an average of 3°, and the soil settlement is reduced by 50%： The technique succeeds in improving soil properties to approximately 5.0 m in depth depending on soil uniformity with depth.
文摘In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t （CPT） resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts： a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g （GEP） m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel.
基金Project(51278451) supported by the National Natural Science Foundation of ChinaProject(LZ12E09001) supported by the Zhejiang Natural Science Foundation,China
文摘Cone penetration test(CPT)is an appropriate technique for quickly determining the geotechnical properties of lunar soil,which is valuable for in situ lunar exploration.Utilizing a typical coupling method recently developed by the authors,a finite element method(FEM)-discrete element method(DEM)coupled model of CPTs is obtained.A series of CPTs in lunar soil are simulated to qualitatively reveal the flow of particles and the development of resistance throughout the penetration process.In addition,the effects of major factors,such as penetration velocity,penetration depth,cone tip angle,and the low gravity on the Moon surface are investigated.
文摘This was a cohort study of in vitro fertilization(IVF)subjects at the University of Utah,Salt Lake City(UT,USA)utilizing partner sperm.Cycles where both the hamster egg penetration test(HEPT)and semen analysis were performed within 2 years prior to IVF cycles were stratified into four groups based on a normal or an abnormal HEPT and morphology.The mean conventional and intracytoplasmic sperm injection(ICSI)fertilization rates were calculated in each group.We performed a univariate analysis on the primary outcome comparing clinically interesting subjects.We performed a cost-effectiveness analysis of a policy of HEPT versus universal ICSI in couples with an abnormal morphology.Among patients with a normal HEPT,there was no difference in the mean conventional fertilization rates between those with a normal and an abnormal morphology.There was no difference in the mean conventional fertilization rates between subjects with a normal morphology without a hamster test and those with a normal HEPT without a morphology assessment.In 1000 simulated cycles with an abnormal morphology,a policy of HEPT was cost saving compared to universal ICSI,yet produced similar fertilization rates.The HEPT is similar to the World Health Organization edition 5(WHO-5)morphology in predicting successful conventional fertilization while allowing decreased utilization of ICSI.A policy of HEPT for males with abnormal morphology saves cost in selecting couples for a fertilization method.
基金supported by grants from the Research Grant Council of Hong Kong Special Administrative Region,China(Project Nos.CityU 11202121 and CityU 11213119).
文摘Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineering practice,soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests,e.g.cone penetration tests(CPTs),due to the restrictions of time,cost and access to subsurface space.In these cases,liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method,leading to remarkable statistical uncertainty in liquefaction assessment.This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment.To tackle this issue,this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a selfadaptive and data-driven manner.The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling(BCS).Both simulated and real CPT data are used to demonstrate the proposed method.Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.
