Penetration Test Research Articles

Region partitioning of articular cartilage with streaming-potential-based parameters and indentation maps

Articular cartilage exhibits site-specific tissue inhomogeneity, for which the tissue properties may continuously vary across the articular surface. To facilitate practical applications such as studying site-specific cartilage degeneration, the inhomogeneity may be approximated with several distinct region-wise variations, with one set of tissue properties for one region. A clustering method was previously developed to partition such regions using cartilage indentation-relaxation and thickness mapping instead of simply using surface geometry. In the present study, a quantitative parameter based on streaming potential measurement was introduced as an additional feature to assess the applicability of the methodology with independent datasets. Experimental data were collected from 24 sets of femoral condyles, extracted from fresh porcine stifle joints, through streaming potential mapping, automated indentation, and needle penetration tests. K-means clustering and Elbow method were used to find optimal region partitions. Consistent with previous findings, three regions were suggested for either lateral or medial condyle regardless of left or right joint. The region shapes were approximately triangular or trapezoidal, which was similar to what was found previously. Streaming potentials were confirmed to be region-dependent, but not significantly different among joints. The cartilage was significantly thicker in the medial than lateral condyles. The region areas were consistent among joints, and comparable to that found in a previous study. The present study demonstrated the capability of region partitioning methods with different variables, which may facilitate new applications whenever site-specific tissue properties must be considered.

Use of Waste Rubber Material as Crumb on Road Construction

Abstract: The growth rate of vehicles is the backbone of economic development of any country. India is the second fast growing automobile industry in the world. In today’s era, solid waste management is the thrust area. On the other side, the traffic intensity is also increasing. As a result, amount of waste tyres is also increasing. The increasing consumption of waste tire has generated many problems such as increasing landfill space, environmental pollution and causing health hazards. Parallel to this is the increasing of roads construction because of heavy traffic on roads. This study reviews to the use of crumb rubber (waste tires in powder form) in bitumen using the wet process. The study focuses on the crumb rubber as a replacement to the total weight of bitumen. The design or life span for all highways and urban roads is 10 – 20 years. Unfortunately, damages or distresses on pavements are still occurring before reaching the maximum period of the designed road serviceability. Among the major influencing factor that is contributing to this distress is the repeated heavy traffic loading on the road surfaces. Moreover, the use of waste crumb rubber in road construction as a pavement surface has a better skid resistance, fatigue crack resistance and increased rut resistance. The review includes physical tests that are used to determine the physical properties of bitumen and modified crumb rubber mix. The physical tests involve penetration test, softening point test, and ductility test. The expectations from the study are to develop bitumen with waste crumb rubber that would minimize the costs of bitumen and providing better physical properties compared to the convention bitumen based on the tests that was conducted.

Securing the digital supply chain: Cybersecurity best practices for logistics and shipping companies

The review investigates the pressing need for robust cybersecurity measures within the logistics and shipping sector, where the digital supply chain is vulnerable to a myriad of cyber threats. The paper delves into the specific challenges faced by logistics companies, including the interconnectedness of global supply chains, reliance on digital technologies for operations, and the high value of goods in transit. It explores the multifaceted nature of cyber risks, encompassing threats such as ransomware, phishing attacks, data breaches, and supply chain disruptions, which can have far-reaching consequences for business continuity and reputation. Through a detailed analysis, the study elucidates cybersecurity best practices tailored to the logistics and shipping industry, encompassing both technical solutions and organizational policies. These include implementing robust authentication and access controls, encrypting sensitive data in transit and at rest, establishing secure communication channels, and conducting regular vulnerability assessments and penetration testing. Furthermore, the paper emphasizes the importance of fostering a culture of cybersecurity awareness among employees through comprehensive training programs and incident response drills. It also discusses the role of regulatory compliance frameworks such as GDPR, CCPA, and industry-specific standards like ISO 27001 in guiding cybersecurity efforts and ensuring adherence to best practices. By providing actionable recommendations and insights garnered from real-world case studies, the study equips logistics and shipping companies with the knowledge and tools needed to bolster their cybersecurity defenses, safeguard critical assets, and maintain trust in the digital supply chain ecosystem.

Security Analysis of Learning Management System Using Penetration Testing with ISSAF Framework

Security Analysis of Learning Management System Using Penetration Testing with ISSAF Framework

Detecting Brute Force Attacks on SSH and FTP Protocol Using Machine Learning: A Survey

The significance of detecting network traffic anomalies in cybersecurity cannot be overstated, especially given the increasing frequency and complexity of computer network attacks. As new Internet-related technologies emerge, so do more intricate attacks. One particularly daunting challenge is represented by dictionary-based brute-force attacks, which require effective real- time detection and mitigation methods. In this paper, we investigate Secure Shell or Secure Socket Shell, is a network protocol that gives users, particularly system administrators, a secure way to access a computer over an unsecured network(SSH)and File Transfer Protocol is a standard network protocol used for the transfer of files from one host to another over a TCP-based network, such as the Internet (FTP) brute-force attack detection by using Our research focuses on using the machine learning approach to detect SSH and FTP brute-force attacks. A reasonably thorough investigation of machine learners' efficacy in identifying brute force assaults on SSH and FTP is made possible by employing several classifiers. Brute-force assaults are a popular and risky method of obtaining usernames and passwords. Applying ethical hacking is an excellent technique to examine the effects of a brute-force assault. This article discusses many defense strategies and approaches to using brute-force assaults. The pros and cons of several defense strategies are enumerated, along with information on which kind of assault is easiest to identify. we made use of machine learning classifiers: Naive Bayes, Random Forest, Logistic Regression, we determined that the Random Forest algorithm achieved the highest level with an accuracy the contribution lies in demonstrating the feasibility of training and evaluating basic Random Forest models with two independent variables to classify CSE-CIC-IDS2018 dataset.

Mitigating cybersecurity risks in radiology—is it time to unmask vulnerabilities and fortify cyber defenses with ethical hacking?

Mitigating cybersecurity risks in radiology—is it time to unmask vulnerabilities and fortify cyber defenses with ethical hacking?

Evaluation of using hardening soil model for predicting wall deflections caused by deep excavation: A case study at the Ho Chi Minh metro line 1, Vietnam

The goal of this study is to assess the application of the Hardening soil model in predicting the deformation of retaining walls of excavations in 2D and 3D finite element analysis at the Ho Chi Minh Metro project. Designed as the deepest underground station in the first metro line built in Ho Chi Minh City (HCMC), Opera House station is located in an area with a dense building zone and close to historical buildings. A summary of the input soil properties is provided using data from site investigations, in-situ tests, and laboratory tests. By numerical simulation using the Hardening soil model, the parameters of the soil stiffness modulus value are verified based on the Standard Penetration Test (SPT), and Pressuremeter test (PMT). The obtained results of the numerical analysis by 2D and 3D finite element methods, and field observations indicate that applying the Hardening soil model with soil stiffness modulus obtained in situ tests gives reasonable results on the displacement of the retaining wall at the final phase. The relationship between the SPT value and the stiffness modulus of HCMC sand is a function of depth. This correlation is obtained through the comparison of wall deformation between the simulation and monitoring at the construction site. The results of the difference between 2D and 3D finite element analysis also are discussed in this study.

Experimental study on behaviors of lithium-ion cells experiencing internal short circuit and thermal runaway under nail penetration abuse condition

This work compared the macro-phenomenon, voltage response, surface temperature, mass loss and material characteristics of LiCoO2 (LCO), LiMn2O4 (LMO), Li(NixCoyMnz)O2 (NCM), LiFePO4 (LFP) cells with the same capacity under penetration tests. The tests provided an in-depth study on behaviors and properties of internal short circuit (ISC) and thermal runaway (TR). From the results, a feature was discovered that the parameters of different types of cells showed different tendencies when penetrated with a series of diameter nails, determined by cathode materials. The LFP cell experienced a slight ISC. Temperature rise rate and maximum temperature increased with larger diameter of nails. The NCM, LCO and LMO cells experienced a severe ISC. The maximum temperature of cells increased, however, the temperature rise rate reduced and macro-phenomena delayed and weakened with larger diameter of nails. For different type of cells, the order of ISC sensitivity was as follows: LCO > LMO > NCM > LFP. As state of charge (SOC) increased, the cells had a higher risk of TR, evidenced by higher surge current and temperature rise rate. The results of nail penetration tests at 100 % SOC indicated that the hazard of TR was arranged in the order of LCO > NCM > LMO > LFP. Due to the fragmentation of secondary particles during the extrusion of nail, the cathode composed of secondary particles suffered more damage than composed of primary particles. The research can provide support for the safety design of cells.

Susceptibility of Legionella gormanii Membrane-Derived Phospholipids to the Peptide Action of Antimicrobial LL-37-Langmuir Monolayer Studies.

LL-37 is the only member of the cathelicidin-type host defense peptide family in humans. It exhibits broad-spectrum bactericidal activity, which represents a distinctive advantage for future therapeutic targets. The presence of choline in the growth medium for bacteria changes the composition and physicochemical properties of their membranes, which affects LL-37's activity as an antimicrobial agent. In this study, the effect of the LL-37 peptide on the phospholipid monolayers at the liquid-air interface imitating the membranes of Legionella gormanii bacteria was determined. The Langmuir monolayer technique was employed to prepare model membranes composed of individual classes of phospholipids-phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), cardiolipin (CL)-isolated from L. gormanii bacteria supplemented or non-supplemented with exogenous choline. Compression isotherms were obtained for the monolayers with or without the addition of the peptide to the subphase. Then, penetration tests were carried out for the phospholipid monolayers compressed to a surface pressure of 30 mN/m, followed by the insertion of the peptide into the subphase. Changes in the mean molecular area were observed over time. Our findings demonstrate the diversified effect of LL-37 on the phospholipid monolayers, depending on the bacteria growth conditions. The substantial changes in membrane properties due to its interactions with LL-37 enable us to propose a feasible mechanism of peptide action at a molecular level. This can be associated with the stable incorporation of the peptide inside the monolayer or with the disruption of the membrane leading to the removal (desorption) of molecules into the subphase. Understanding the role of antimicrobial peptides is crucial for the design and development of new strategies and routes for combating resistance to conventional antibiotics.

Effect of temperature rise on the mechanical behaviour of deep-sea clay surrounding oil and gas pipelines

The mechanical behaviour of deep-sea clay is crucial for oil and gas pipeline safety and is traditionally assessed through in situ penetration testing, which is usually conducted in a low-temperature environment (usually at temperatures lower than 4 °C). However, the temperature of deep-sea clay surrounding high-temperature pipelines can reach approximately 20 °C. To date, the influence of temperature variation on the mechanical properties of deep-sea clay, especially strain-softening behaviour, has been largely overlooked, posing a safety risk to practical engineering. Consequently, this study first establishes a temperature-controlled cyclic penetration testing platform. Then, an enhanced mini-spherical penetrometer is employed to investigate the undrained strength and strain-softening behaviour of undisturbed deep-sea clay samples at 4 °C, 10 °C and 20 °C. In particular, an improved framework is proposed to quantitatively characterize the mechanical behaviour. Finally, a strain-softening law considering temperature variations is established and rigorously validated through in situ tests. The findings underscore several significant observations: (a) deep-sea clay in its intact state exhibits a heightened sensitivity to temperature variations compared to that in its remoulded state; (b) temperature variations influence the strain-softening parameters of deep-sea clay, resulting in distinct behaviours; and (c) the proposed strain-softening law considering temperature variations is verified to be more logical.

Destructive and non-destructive testings of 304 austenitic stainless steel produced by investment casting method

ABSTRACT In this study, the properties and structures of 304 austenitic stainless steels produced by investment casting methods and stages through destructive and non-destructive (NDT) testing methods are examined. First, the model mould was created from recycled wax containing 5–25% water, 15–35% filler and 0.5% ceramic for the production of a water pipe armature by investment casting method. Then, the ceramic coated mould was sintered at 1350°C, and 304 austenitic stainless steel was melted in the induction furnace at 1150°C at a speed of 1.36 kg/min and poured into this mould. The hardness value of the 304 austenitic stainless steel water pipe armature was obtained as 86.16 HRB and the tensile strength value was 518.76 MPa. As a result of examination with an optical metal microscope, ferrite and austenite structures were observed. Linear and round defects were detected in the liquid penetrant test, and third level (RT-3) shrinkage-delta defects were detected in the radiography test. These results showed that the armature part was acceptable in terms of mechanical strength, corrosion and leakage.

A Comparative Evaluation of The Mechanical Properties of PET and Polystyrene Modified Asphaltic Concrete Containing Rice Husk Ash Filler.

The study evaluated and compared the influence of bitumen modification for sustainable as- phalt using waste plastic (Polyethylene Terephthalate, PET) and waste Polystyrene (PS) at 5–50% modification levels. Rice husk ash (RHA) and desilted sand were used as filler and fine aggregate with crushed granite as coarse aggregate. Tests conducted include; penetration, viscosity, flash point, fire point, specific gravity, ductility and marshal stability test on asphalt. For PET modi- fied-binder a decrease in penetration and ductility was observed while the specific gravity, vis- cosity, flash and fire points of the binder increased. For the PS modified-binder, the penetration, ductility, viscosity and specific gravity decreased with an increase in PS while the flash and fire point increased. Marshall Stability results showed an optimal of 20% PET modification was ade- quate for medium traffic surfaing with stability, flow, density, air void, void in mineral aggregates (VMA), and Void filled with binder (VFB) of 4875N, 3.53 mm, 2.460 g/cm3, 3.30%, 18.20%, and 81.87% respectively. For 10% PS modification content, the stability, flow, density, air void, void in mineral aggregates (VMA), and Void filled with binder (VFB) were found to be 6825N, 3.33 mm, 2.362 g/cm3, 4.52%, 18.21%, and 75.18% respectively which was found to be adequate for heavy traffic surfacing. Hence, it was concluded that the investigated waste plastics could be used in Asphalt pavement courses. If applied, these results could provide low-cost materials for paving roads while also reducing waste-related pollution and environmental issues.

A data-driven approach to optimize the design configuration of multi-sleeve cone penetrometer probe attachments

This study uses a data-driven approach to address the complexities associated with research focused multi-sleeve Cone Penetration Test (CPT) devices, particularly focusing on the multi-friction attachment (MFA) and multi-piezo-friction attachment (MPFA) CPT devices. Hindered by time-consuming assembly and susceptibility to sensor stream losses due to extensive electronic components, these advanced devices demand optimization to transform from research devices to practice-suitable devices. This study aims at optimizing the design of the multi-sleeve CPT devices using machine learning, with soil type classification performance as the primary metric for device configuration effectiveness. The research scope centers not on using machine learning for soil classification but on refining the design of multi-sleeve CPT devices. A two-phase data-driven approach is adopted, testing various feature combinations across eight machine learning models. The first phase involves identifying the most suitable model for the dataset, followed by a refinement of features to balance sensor number minimization and soil classification accuracy. The result is a proposed configuration for a multi-sleeve CPT device, simplifying the original design while maintaining robustness, thereby enhancing cost-efficiency and operational effectiveness in geotechnical practice. This work sheds light on how the integration of machine learning can guide the design optimization of geotechnical instruments.

Exploring the Efficacy and Ethical Implications of Penetration Testing in Network Security

In today's landscape, cybersecurity reigns supreme as cybercrimes surge. Businesses are on high alert, striving to shield themselves from online threats like hacking and data breaches. Various safeguards are in place to brace for such scenarios. However, penetration testing emerges as the ultimate litmus test to gauge the efficacy of these security measures. A penetration tester must meticulously select the appropriate tools, considering factors like budget, time, and scope. This study scrutinizes the effectiveness of scanning tools within a Kali Linux environment. The overarching objective of penetration testing is to ensure networks and systems remain impervious to security threats, preventing unauthorized access to resources. This paper delves into the intricacies of planning and executing a penetration test, targeting project managers or directors contemplating such an endeavor. Undoubtedly, conducting a penetration test is a complex undertaking, and organizations must weigh the suitability of this method for their specific needs.

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

Social engineering (SE) presents weaknesses that are difficult to quantify in penetration testing directly. The majority of expert social engineers utilize phishing and adware tactics to convince victims to provide information voluntarily. SE in social media has a similar structural layout to regular postings but has a malevolent intrinsic purpose. Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) was used to train a novel SE model to recognize covert SE threats in communications on social networks. The dataset includes various posts, including text, images, and videos. It was compiled over a period of several months. Then carefully curated to ensure that it is representative of the types of content that are typically posted on social media. First, using domain heuristics, the social engineering assaults detection (SEAD) pipeline is intended to weed out social posts with malevolent intent. After tokenizing each social media post into sentences, each post is examined using a sentiment analyzer to determine whether it is a training data normal or an abnormality. Subsequently, an RNN-LSTM model is trained to detect five categories of social engineering assaults, some of which may involve information-gathering signals. Thus, the proposed SEA model yielded a classification precision of 0.82 and a recall of 0.79.

Examining Ethical Hacking: A Case Study for Information Security Curriculum

Denial of Service (DoS) assaults are a crucial subject in security courses, particularly those that emphasize intrusion detection and ethical hacking methods. This work offers a case study that describes how three typical DoS attacks were implemented as part of extensive offensive hands-on lab exercises. The goal of the exercises is to provide students with the necessary skills to carry out these assaults in a lab setting on a remote network. The report also addresses the moral and legal ramifications of teaching ethical hacking and offers recommendations for improving the efficacy and integrity of information security curricula at educational institutions.

A Case Study on Advanced CPT Data Interpretation: From Stratification to Soil Parameters

The cone penetration test (CPT) is considered as one of the most reliable in-situ tests and has found numerous applications in the geotechnical engineering field. Traditional CPT interpretation includes, but are not limited to the identification of the soil stratification and the determination of soil parameters. This paper presents a case study concerning a test site located in Salzburg, Austria, in which we focus on the interpretation of CPTs from different perspectives. The manuscript is divided into three main sections dealing with three different aspects of CPT interpretation, namely stratification, ground variability and soil parameters. The first strategy introduces a machine learning based stratification identification strategy to detect soil layer boundaries from CPT measurements. A comparison with reference solutions demonstrates relative merits of this approach to classical filter algorithms based on empirical CPT classifications. The second strategy introduces an intuitive approach to evaluate the ground variability. This is achieved by calculating the level of fluctuation on the basis of CPT measurements and could be used as a data-driven decision-making tool for the improved design of CPT investigation layouts. The third strategy is embedded in an ongoing research project that aims to determine constitutive model parameters from in-situ tests using a graph-based methodology. In the present work, the developed automated parameter determination framework is applied to evaluate the soil parameters of one selected soil layer identified from the CPT interpretations. Potential lines of research in the context of CPT interpretation are explored throughout this work and may serve as valuable reference in future research.

Application of binary logistic models for identifying high pile rebound using cone penetration test data

Application of binary logistic models for identifying high pile rebound using cone penetration test data

Capture the Flag with ChatGPT: Security Testing with AI ChatBots

Penetration testing, commonly referred to as pen testing, is a process of assessing the security of a computer system or network by simulating an attack from an external or internal threat actor. One type of pen testing exercise that has become popular among cybersecurity enthusiasts is called Capture the Flag (CTF). This involves solving a series of challenges that simulate real-world hacking scenarios, with the goal of capturing a flag that represents a piece of sensitive information. Recently, there has been a growing interest in the use of natural language processing (NLP) and machine learning (ML) technologies for penetration testing and CTF exercises. One such technology that has received significant attention is ChatGPT, a large language model (LLM) trained by OpenAI based on the GPT-3.5 architecture. The use of ChatGPT in CTFs has several potential benefits for participants and organisers, including more dynamic and realistic scenarios and enhanced learning experiences, and enhance the effectiveness and realism of CTFs.. Future research can explore more sophisticated models and evaluate the effectiveness of ChatGPT in improving the performance of participants in CTFs.

Face Detection Performance Using CNNs and Bug Bonuty Program (BBP)

Face Detection Performance Using CNNs and Bug Bonuty Program (BBP)