Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

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%.

Enhancing Mobile Security through Comprehensive Penetration Testing

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.

Penetration Tests of Multi-Bucket Foundation in Sand for Offshore Wind Turbines

Suction foundations are generally installed with negative pressures to overcome the resistance of soils and complete the penetration,but excessive negative pressures are also avoided to cause seepage damages.In this paper,the model test method was used to analyze the movement characteristics of multi-bucket foundations in the process of sinking in sand,and the common calculation methods of sinking resistances are verified.The critical negative pressure corresponding to the seepage failure of foundation was determined under the action of increasing negative pressure step by step and the characteristics of soil failure were studied.The calculation formula of critical suction in sand was verified in application,and according to the test results,the value of seepage coefficient was modified,which provides an example for the study of suction foundation in sand soils.

Study on strength properties and soil behaviour type classification of Huanghe River Delta silts based on variable rate piezocone penetration test

Fine-grained silt is widely distributed in the Huanghe River Delta(HRD)in China,and the sedimentary structure is complex,meaning that the clay content in the silt is variable.The piezocone penetration test(CPTu)is the most widely approved in situ test method.It can be used to invert soil properties and interpret soil behavior.To analyse the strength properties of surface sediments in the HRD,this paper evaluated the friction angle and its inversion formula through the CPTu penetration test and monotonic simple shear test and other soil unit experiments.The evaluation showed that the empirical formula proposed by Kulhawy and Mayne had better prediction and inversion effect.The HRD silts with clay contents of 9.2%,21.4%and 30.3%were selected as samples for the CPTu variable rate penetration test.The results show as follows.(1)The effects of the clay content on the tip resistance and the pore pressure of silt under different penetration rates were summarized.The tip resistance Q_t is strongly dependent on the clay content of the silt,the B_(q)value of the silt tends to 0 and is not significantly affected by the change of the CPTu penetration rate.(2)Five soil behavior type classification charts and three soil behavior type indexes based on CPTu data were evaluated.The results show that the soil behavior type classification chart based on soil behavior type index ISBT,the Robertson 2010 behavior type classification chart are more suitable for the silty soil in the HRD.

Obtaining 2D Soil Resistance Profiles from the Integration of Electrical Resistivity Data and Standard Penetration Test (SPT) and Light Dynamic Penetrometer (DPL) Resistance Tests—Applications in Mass Movements Studies

In Brazil and various regions globally, the initiation of landslides is frequently associated with rainfall;yet the spatial arrangement of geological structures and stratification considerably influences landslide occurrences. The multifaceted nature of these influences makes the surveillance of mass movements a highly intricate task, requiring an understanding of numerous interdependent variables. Recent years have seen an emergence in scholarly research aimed at integrating geophysical and geotechnical methodologies. The conjoint examination of geophysical and geotechnical data offers an enhanced perspective into subsurface structures. Within this work, a methodology is proposed for the synchronous analysis of electrical resistivity geophysical data and geotechnical data, specifically those extracted from the Light Dynamic Penetrometer (DPL) and Standard Penetration Test (SPT). This study involved a linear fitting process to correlate resistivity with N10/SPT N-values from DPL/SPT soundings, culminating in a 2D profile of N10/SPT N-values predicated on electrical profiles. The findings of this research furnish invaluable insights into slope stability by allowing for a two-dimensional representation of penetration resistance properties. Through the synthesis of geophysical and geotechnical data, this project aims to augment the comprehension of subsurface conditions, with potential implications for refining landslide risk evaluations. This endeavor offers insight into the formulation of more effective and precise slope management protocols and disaster prevention strategies.

FEM-DEM coupled modeling of cone penetration tests in lunar soil

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.

Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

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.

Adaptive sampling strategy for characterizing spatial distribution of soil liquefaction potential using cone penetration test

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.

Seasonal influence on cone penetration test: An unsaturated soil site example

Interpretation of electric cone penetration test(CPT) based pore water pressure measurement(CPTu) is well established for soils with behavior that follows classical soil mechanics. The literature on the interpretation of these tests performed on unsaturated tropical soils is limited, and little is known about the influence of soil suction on in situ test data. In this context, the CPT data are presented and discussed to illustrate the seasonal variability in an unsaturated tropical soil site. The test data show that soil suction significantly influenced CPT data up to a depth of 4 m at the study site. It shows the importance of considering seasonal variability in unsaturated soil sites caused by soil suction, which was related to water content through a soil-water retention curve(SWRC). It is also important to consider this aspect in the interpretation of CPT data from these soils.

A case study on the soil classification of the Yellow River Delta based on piezocone penetration test

Piezocone penetration test(CPTu),the preferred in-situ tool for submarine investigation,is significant for soil classification and soil depth profile prediction,which can be used to predict soil types and states.However,the accuracy of these methods needs to be validated for local conditions.To distinguish and evaluate the properties of the shallow surface sediments in Chengdao area of the Yellow River Delta,seabed CPTu tests were carried out at ten stations in this area.Nine soil classification methods based on CPTu data are applied for soil classification.The results of classification are compared with the in-situ sampling to determine whether the method can provide sufficient resolution.The methods presented by Robertson(based on soil behavior type index Ic),Olsen and Mitchell are the more consistent and compatible ones compared with other methods.Considering that silt soils have potential to liquefy under storm tide or other adverse conditions,this paper is able to screen soil classification methods suitable for the Chengdao area and help identify the areas where liquefaction or submarine landslide may occur through CPTu investigation.

Structured Query Language Injection Penetration Test Case Generation Based on Formal Description

Aiming to improve the Structured Query Language( SQL) injection penetration test accuracy through the formalismguided test case generation,an attack purpose based attack tree model of SQL injection is proposed,and then under the guidance of this model, the formal descriptions for the SQL injection vulnerability feature and SQL injection attack inputs are established. Moreover,according to new coverage criteria,these models are instantiated and the executable test cases are generated.Experiments show that compared with the random enumerated test case used in other works,the test case generated by our method can detect the SQL injection vulnerability more effectively. Therefore,the false negative is reduced and the test accuracy is improved.

The hamster egg penetration test may decrease intracytoplasmic sperm injection utilization while maintaining high conventional fertilization rates

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.

Comparison of Vs and SPT Soil Liquefaction Assessments of NCEER: Including Hypothesis Testing

Soil liquefaction is one of the complex research topics in geotechnical engineering and engineering geology. Especially after the 1964 Niigata earthquake (Japan) induced many soil liquefaction incidents, a variety of soil liquefaction studies were conducted and reported, including the liquefaction potential assessment methods utilizing the shear wave velocity (Vs) or SPT-N profiles (SPT: standard penetration test). This study used the Vs and SPT methods recommended by the National Center for Earthquake Engineering Research (NCEER) to examine which is more conservative according to the assessment results on 41 liquefiable soil layers at sites in two major cities in Taiwan. Statistical hypothesis testing was used to make the analysis more quantitative and objective. Based on three sets of hypothesis tests, it shows that the hypothesis—the SPT method is more conservative than the Vs method—was not rejected on a 5% level of significance.

Laboratory Test on Long-Term Deterioration of Cement Soil in Seawater Environment

Laboratory tests were conducted to study the effects of curing time, cement ratio and seawater pressure on cement soil deterioration formed at simulative marine soft clay sites. Deterioration depth was determined on the basis of characteristics of penetration resistance and penetration depth curves, and the deterioration depth of cement soil with the cement ratio of 7%, reached 31.8 mm after 720 d. Results of research indicated that deterioration extended quickly under seawater environment and the deterioration depth increased with the prolonging curing time. In addition, the water pressure could speed up deterioration. With the increase of cement content, the strength of cement soil increased obviously. At the same time, the deterioration depth decreased significantly. The concentration of calcium ion in the cement stabilized soil increased with the increase of depth, while that of magnesium ion gradually decreased. The variations were consistent with energy dispersive spectrometer(EDS)analysis results, and the calcium concentration with depth was in a good consistency with strength distribution at long term. The results showed that the deterioration became more serious with the curing time, and it was related to calcium leaching.

Assessment of rapid impact compaction in ground improvement from in-situ testing

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.

An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference

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.

Liquefaction Assessment in Golem Area, Albania, by Means of In-situ Tests

The liquefaction is a very significant phenomenon in clayey silty soils, silty sands and also sands. The high potential of liquefaction is generally recognized when these types of soils are laid under the hydrostatic water table. Low plasticity silts, silty sands and sands are found as recent alluvial deposits in the western coastal part of Albania, especially in the sandy beaches of Adriatic Sea near Durres City. The aim of this study is to evaluate the soil liquefaction potential in the area of Golem. Ten CPTUs （cone penetration test with pore pressure measurements） are carried out for the site investigation of soils. In this paper, results of the CPTU based liquefaction analysis are presented. The data of two CPTUs （10 in total） are analyzed and factor of safety was found by considering different levels of hazard and ground water. The results of liquefaction potential analysis show that the soils in the area of Golem have a high risk of liquefaction.

In Situ Tests--Predicted vs. Observed Settlements： A Comparative Case Study

This paper aims to deal with the comparison of the estimated settlements derived by in situ tests with the observed settlements in site, in order to evaluate the accuracy of settlement prediction by in situ tests, in comparison not only with site observation by topographic means, but also with the values of settlements derived by numerical analysis by means of PLAXIS 2D and 3 D. The site where are carried out the tests and periodically are observed the settlements since the beginning of construction process, is located in the Oil Product Terminal, at the industrial park of Porto Romano, Durres, Albania. The main purpose of this project was the ground improvement by using preloading method in order to prevent liquefaction process and settlements. The data used to conduct this study are taken by the site investigation done after inserting into the soil vertical drains made of columns of free--draining gravel （gravel pile drains） until 14 m depth and center-to-center spacing of 2 m, and wick drains （premanufactured） until 25 m depth and center-to-center spacing of 1.8 m. The observed settlements are periodically measured by topographic equipments. This paper will present the conclusions derived by settlement analyzes from in situ tests and site observations.

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Rolling dynamic compaction(RDC),which involves the towing of a noncircular module,is now widespread and accepted among many other soil compaction methods.However,to date,there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC.This study presents the application of artificial neural networks(ANNs) for a priori prediction of the effectiveness of RDC.The models are trained with in situ dynamic cone penetration(DCP) test data obtained from previous civil projects associated with the 4-sided impact roller.The predictions from the ANN models are in good agreement with the measured field data,as indicated by the model correlation coefficient of approximately 0.8.It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.