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Journal articles on the topic 'AI in Penetration Testing'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Khedkar, Samruddhi S., and Prof. D. G. Ingale. "Automated Penetration Testing." International Journal of Ingenious Research, Invention and Development (IJIRID) 5, no. 3 (2024): 299–305. https://doi.org/10.5281/zenodo.13988309.

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Automated penetration testing has emerged as a critical tool in the cyber security landscape, offering the ability to rapidly identify and address vulnerabilities within increasingly complex network infrastructures. This seminar report explores the development and application of automated penetration testing tools, which streamline traditional, labor-intensive processes by leveraging automation to conduct thorough security assessments. The report delves into various automated tools and frameworks, such as Metasploit, Burp Suite, and OpenVAS, and examines their methodologies, capabilities, and

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Alaryani,, Meera, Shamsa Alremeithi, Fatima Al Ali, and Richard Ikuesan. "PentHack: AI-Enabled Penetration Testing Platform for Knowledge Development." European Conference on Cyber Warfare and Security 23, no. 1 (2024): 27–36. http://dx.doi.org/10.34190/eccws.23.1.2493.

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The process of conducting and executing penetration testing within the pedagogical paradigm often requires complex and arduous processes. This is especially daunting for beginners who often struggle with the complexities of penetration processes: reconnaissance, enumeration, and system hacking. Research works to address this complexity leverage industry tools that have proven to work for industry-related training, however, they fail to support pedagogical learning in higher education systems. To address this limitation, this study proposed the development of an academic-focused penetration tes

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Patil, Manaswi, Devaki Thakare, Arzoo Bhure, Shweta Kaundanyapure, and Dr Ankit Mune. "An AI-Based Approach for Automating Penetration Testing." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5019–28. http://dx.doi.org/10.22214/ijraset.2024.61113.

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Abstract: Cyber penetration testing (pen-testing) is important in revealing possible weaknesses and breaches in network systems that can ultimately help in curbing cybercrimes. Nevertheless, even with the current drive to mechanize pen- testing, there are still a number of challenges which include incomplete frameworks and low precision in automation methods. This paper aims at addressing them by suggesting hybrid AI-based automation framework specifically for Pen- Testing through integration of smart algorithms and automated tools. As indicated by recent studies, it goes further into proposin

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Thapaliya, Suman, and Saroj Dhital. "AI-Augmented Penetration Testing: A New Frontier in Ethical Hacking." International Journal of Atharva 3, no. 2 (2025): 28–37. https://doi.org/10.3126/ija.v3i2.80099.

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The accelerating sophistication of cyber threats has outpaced the capabilities of traditional, manual penetration testing approaches. This paper proposes an AI-augmented penetration testing framework that leverages machine learning and reinforcement learning to enhance the efficiency, scalability, and adaptability of ethical hacking efforts. We detail the integration of AI in key phases of the penetration testing lifecycle, including automated reconnaissance via NLP-based parsing of open-source intelligence, vulnerability prediction through supervised learning models trained on historical expl

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Sandeep, Phanireddy. "Advancing Security: Penetration Testing in Web Applications Powered by Artificial Intelligence." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 6, no. 4 (2020): 1–7. https://doi.org/10.5281/zenodo.14787314.

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Artificial Intelligence brings web applications to a new level of personalization and decision-making. These systems are new and complex but expose them to unique security challenges. Penetration testing (pen-testing) techniques specific to the penetration testing of AI-powered web applications are explored. It describes testing methodologies and available frameworks and tools and discusses the scope of pen-testing to improve security for AI systems and underlying web infrastructures. Illustrative diagrams complement the key insights to explain them.

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Siva Krishna Jampani. "Revolutionizing Penetration Testing: AI-Powered Automation for Enterprise Security." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1562–69. https://doi.org/10.32628/cseit241061201.

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This article explores the transformative impact of artificial intelligence and machine learning technologies on enterprise security through automated penetration testing frameworks. The article presents a comprehensive article analysis of an AI-powered penetration testing system, examining its architecture, implementation methodology, and performance metrics in real-world enterprise environments. The article findings demonstrate significant improvements in testing efficiency, with automated systems achieving superior vulnerability detection rates while substantially reducing testing time and r

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Hoffmann, Joerg. "Simulated Penetration Testing: From "Dijkstra" to "Turing Test++"." Proceedings of the International Conference on Automated Planning and Scheduling 25 (April 8, 2015): 364–72. http://dx.doi.org/10.1609/icaps.v25i1.13684.

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Penetration testing (pentesting) is a well established method for identifying security weaknesses, by conducting friendly attacks. Simulated pentesting automates this process, through designing a model of the system at hand, and using model-based attack planning to generate the attacks. Classical planning variants of this idea are being used commercially by the pentesting industry since 2010. Such models can pinpoint potentially dangerous combinations of known vulnerabilities, but ignore the incomplete knowledge characteristic of hacking from the attacker's point of view. Yet, ideally, the sim

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Kumar CSA, Ayush. "Ethical Hacking and Penetration Testing." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02790.

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The rise of digital infrastructure has led to an increase in cyber threats, making cybersecurity a critical concern for organizations. Ethical hacking and penetration testing have become essential tools in identifying vulnerabilities and preventing potential security breaches. Ethical hacking involves authorized professionals simulating cyberattacks to uncover security weaknesses, while penetration testing is a structured approach to evaluating system defenses through controlled attack scenarios. This study explores ethical hacking methodologies, including black-box, white-box, and gray-box te

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Chamberlain, David, and Ellis Casey. "Capture the Flag with ChatGPT: Security Testing with AI ChatBots." International Conference on Cyber Warfare and Security 19, no. 1 (2024): 43–54. http://dx.doi.org/10.34190/iccws.19.1.2171.

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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 lear

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Kozlovska, Mariia, Andrian Piskozub, and Volodymyr Khoma. "Artificial intelligence in penetration testing: leveraging AI for advanced vulnerability detection and exploitation." Advances in Cyber-Physical Systems 10, no. 1 (2025): 65–70. https://doi.org/10.23939/acps2025.01.065.

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The article examines the ways artificial intelligence is influencing the penetration testing procedure. As technology advances and cyber threats grow more common, conventional testing methods are insufficient. Artificial intelligence aids in automating processes like vulnerability detection and real-world attack simulation, leading to quicker, more precise results with reduced dependence on human input. Machine learning is a game-changer in identifying hidden security flaws by analyzing past attacks and abnormal patterns. Tools mentioned in the article are revolutionizing vulnerability detecti

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Çaylı, Osman. "AI-Enhanced Cybersecurity Vulnerability-Based Prevention, Defense, and Mitigation using Generative AI." Orclever Proceedings of Research and Development 5, no. 1 (2024): 655–67. https://doi.org/10.56038/oprd.v5i1.616.

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The rapid evolution of cyberattacks, driven by increasingly sophisticated techniques and the proliferation of readily available AI tools, presents significant challenges for organizations worldwide. Traditional cybersecurity approaches often prove insufficient in addressing the speed, adaptability, and complexity of modern threats. The VULTURE project directly tackles these challenges by proposing a revolutionary AI-powered cybersecurity platform that leverages the capabilities of generative AI (GenAI) and large language models (LLMs) to enhance vulnerability prediction, automate penetration t

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Dr., Anitha S., and D. Pravishka. "Intelligent Automation Framework for Penetration Testing and Security Assessment." Research and Reviews: Advancement in Cyber Security 2, no. 3 (2025): 39–45. https://doi.org/10.5281/zenodo.15583371.

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<em>As cyber threats grow in complexity and frequency, the need for efficient, continuous security assessment is critical. Traditional penetration testing is often time-consuming, manual, and resource-intensive. This paper presents a modular Automated Penetration Testing Framework that automates key phases namely reconnaissance, scanning, vulnerability detection, exploitation, and reporting using open-source tools and rule-based logic. The framework reduces human error, improves test consistency, and supports real-time reporting, making it ideal for dynamic and resource-constrained environment

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Pratama, Derry, Naufal Suryanto, Andro Aprila Adiputra, et al. "CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher." Sensors 24, no. 21 (2024): 6878. http://dx.doi.org/10.3390/s24216878.

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Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a special

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Reddy Bhimanapati, Vijay Bhasker, Shalu Jain, and Pandi Kirupa Gopalakrishna Pandian. "Security Testing for Mobile Applications Using AI and ML Algorithms." Journal of Quantum Science and Technology 1, no. 2 (2024): 44–58. http://dx.doi.org/10.36676/jqst.v1.i2.15.

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Mobile apps have revolutionized the digital world, making mobile devices essential to billions of users' everyday lives. This growth in mobile use has also increased security concerns to mobile apps, from data breaches to malicious software assaults. Traditional security testing methodologies, although useful, sometimes fail to address these attackers' sophistication and evolution. This study examines the use of AI and ML algorithms in mobile application security testing to improve vulnerability discovery, analysis, and mitigation.AI and ML algorithms use massive volumes of data and real-time

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Ghanem, Mohamed C., and Thomas M. Chen. "Reinforcement Learning for Efficient Network Penetration Testing." Information 11, no. 1 (2019): 6. http://dx.doi.org/10.3390/info11010006.

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Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The propo

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Jha, Tapan Kumar. "Modern Web Application Penetration Testing Tools: A Comparative Analysis." International Journal of Recent Research and Review XVII, no. 4 (2024): 171–85. https://doi.org/10.62233/ijrrr26.

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Web applications increasingly serve as critical infrastructure, yet remain disproportionately vulnerable to cyber-attacks. This paper presents a comparative analysis of modern penetration testing (VAPT) tools—both open-source and commercial—with a focus on detection efficacy, coverage of the OWASP Top 10, false-positive rates, performance, usability, and cost. A selection of tools (Skipfish, OWASP ZAP, Burp Suite Pro, W3af, Qualys WAS, and Fortify WebInspect) are reviewed through data drawn from recent peer-reviewed studies, benchmarks on standardized testbeds like bWAPP, and industry reports.

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Xiao, Jin, Buhong Wang, Ruochen Dong, Zhengyang Zhao, and Bofu Zhao. "SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods." Aerospace 12, no. 5 (2025): 431. https://doi.org/10.3390/aerospace12050431.

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Satellite networks face escalating cybersecurity threats from evolving attack vectors and systemic complexities. This paper proposes SatGuard, a novel framework integrating a three-dimensional penetration testing methodology and a nonlinear risk assessment mechanism tailored for satellite security. To address limitations of conventional tools in handling satellite-specific vulnerabilities, SatGuard employs large language models (LLMs) like GPT-4 and DeepSeek-R1. By leveraging their contextual reasoning and code-generation abilities, SatGuard enables semi-automated vulnerability analysis and ex

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Damilola Oluwaseun Ogundipe, Sodiq Odetunde Babatunde, and Emmanuel Adeyemi Abaku. "AI AND PRODUCT MANAGEMENT: A THEORETICAL OVERVIEW FROM IDEA TO MARKET." International Journal of Management & Entrepreneurship Research 6, no. 3 (2024): 950–69. http://dx.doi.org/10.51594/ijmer.v6i3.965.

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Artificial Intelligence (AI) has emerged as a transformative force in the realm of product management, offering a theoretical framework that reshapes the journey from ideation to market penetration. This abstract provides a comprehensive overview of the theoretical underpinnings and practical applications of AI in product management, delineating its pivotal role across various stages of the product lifecycle. The ideation phase marks the inception of product development, where AI serves as a catalyst for innovation, augmenting creativity through advanced algorithms and data-driven insights. Ma

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Shatson Pamba Fasco. "Neuromorphic computing for real-time adaptive penetration testing: analysis of human intuition in AI-dominated work space." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2038–51. https://doi.org/10.30574/wjarr.2024.24.3.3860.

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This research investigates the revolutionary integration of neuromorphic computing with human intuition in the domain of real-time adaptive penetration testing, addressing the critical challenges facing modern cybersecurity in AI-dominated workspaces. The study presents a novel approach that combines the parallel processing capabilities of neuromorphic architectures with the nuanced decision-making abilities of human security experts, resulting in a hybrid system that significantly enhances threat detection and response capabilities. Our research implements a sophisticated neuromorphic computi

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Shatson, Pamba Fasco. "Neuromorphic computing for real-time adaptive penetration testing: analysis of human intuition in AI-dominated work space." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2038–51. https://doi.org/10.5281/zenodo.15209149.

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This research investigates the revolutionary integration of neuromorphic computing with human intuition in the domain of real-time adaptive penetration testing, addressing the critical challenges facing modern cybersecurity in AI-dominated workspaces. The study presents a novel approach that combines the parallel processing capabilities of neuromorphic architectures with the nuanced decision-making abilities of human security experts, resulting in a hybrid system that significantly enhances threat detection and response capabilities. Our research implements a sophisticated neuromorphic computi

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Safhi, Amine el Mahdi, Gilberto Cidreira Keserle, and Stéphanie C. Blanchard. "AI-Driven Non-Destructive Testing Insights." Encyclopedia 4, no. 4 (2024): 1760–69. http://dx.doi.org/10.3390/encyclopedia4040116.

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Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This paper provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect d

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Jha, Tapan Kumar, and Riddhi Soral. "Virtual Penetration Testing (VPT): A Next-Gen Approach To Web Application Security." International Journal of Recent Research and Review XVIII, no. 1 (2025): 281–98. https://doi.org/10.62233/ijrrr25.

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Web applications have become fundamental components of the modern digital ecosystem, facilitating communication, commerce, and data exchange. However, their growing complexity and interconnectivity have made them prime targets for cyber-attacks. Traditional penetration testing methods, although effective, are often manual, time-consuming, and inconsistent. In response, Virtual Penetration Testing (VPT) has emerged as a next-generation solution that leverages automation, artificial intelligence (AI), and model-driven engineering to perform continuous, scalable, and efficient security assessment

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Tolkachova, Anastasiia, and Andriian Piskozub. "METHODS FOR TESTING THE SECURITY OF WEB APPLICATIONS." Cybersecurity: Education, Science, Technique 2, no. 26 (2024): 115–22. https://doi.org/10.28925/2663-4023.2024.26.668.

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Penetration testing is a key method of dynamic security assessment of computer networks, infrastructure, web and mobile applications aimed at identifying and exploiting vulnerabilities by simulating possible attacks by intruders. Traditionally, this process is carried out manually, requiring highly skilled cybersecurity professionals and considerable time to prepare, execute attacks, analyse the results and generate reports. However, with the growing complexity and number of cyber threats, there is a need for automated tools that can speed up the testing process while increasing its efficiency

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Jeong, Sanghyeok, Jooheon Park, Jinyoung Yoon, and Jaesung Kim. "AI-Based Nondestructive Quality Evaluation of Friction Stir Welded Joints in Aluminum Parts for Electric Vehicles." Journal of Welding and Joining 43, no. 1 (2025): 30–38. https://doi.org/10.5781/jwj.2025.43.1.3.

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In the manufacturing of electric vehicles, aluminum is widely used to enhance lightweight properties and heat dissipation in vehicle parts. However, aluminum is prone to issues such as internal pore formation and reduced strength compared to the base material after welding. Therefore, it is crucial to select an appropriate welding process based on the vehicle part's purpose, thickness, geometry and quality requirements. Friction Stir Welding (FSW) has become increasingly popular due to its effectiveness in reducing defects such as porosity, cracks, and thermal distortion. Various Non-Destructi

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Al-Saudi, Mustafa Salim Mohammed, and Kassem Hamze. "Artificial Intelligence-Driven Penetration Testing for Wireless Networks: Enhancing Security Vulnerability Detection Using CNN Models." International Research Journal of Innovations in Engineering and Technology 08, no. 05 (2024): 232–37. http://dx.doi.org/10.47001/irjiet/2024.805034.

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Adware, spyware, viruses, and other forms of malware are serious risks to people, companies, governments, and military activities. Advanced methods for vulnerability detection are required since traditional security measures frequently fall short in the face of complex and dynamic cyberthreats. In order to increase accuracy, adaptability, and scalability in discovering security vulnerabilities, this study investigates the incorporation of artificial intelligence (AI) in the design of a wireless network penetration testing system, utilizing machine learning. The dataset used was BoTNeTIoT-L01,

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Alazba, A. A., Mohamed A. Mattar, Ahmed El-Shafei, Farid Radwan, Mahmoud Ezzeldin, and Nasser Alrdyan. "Comparative Analysis of ANN, GEP, and Water Advance Power Function for Predicting Infiltrated Water Volume in Furrow of Permeable Surface." Water 17, no. 9 (2025): 1304. https://doi.org/10.3390/w17091304.

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The present investigation utilized artificial neural networks (ANN) and gene expression programming (GEP) in comparison with the two-point method (TPM) to develop a generalized solution for predicting infiltrated water volume (∀Z) across various soil types under furrow conditions. This work assesses infiltration behavior with respect to experimental data from several temporal contexts. Data distribution and model performance are evaluated via descriptive statistics and correlation tests. Artificial intelligence (AI) models (ANN and GEP) trained and evaluated utilizing input variables—inflow ra

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.GANESH, Mr. "Intelligence Technique for Fixing Vulnerabilities in Mobile Applications." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46379.

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ABSTRACT - The increasing reliance on mobile applications for both personal and business purposes have led to a rise in security vulnerabilities, posing significant risks to users and organizations alike. As mobile applications evolve in complexity, so do the methods employed by attackers to exploit weaknesses. Traditional security practices often fall short in addressing the unique challenges of mobile platforms. This paper explores advanced intelligence techniques for identifying, analyzing, and mitigating vulnerabilities in mobile applications. Using different types of algorithms such as Ga

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Adekunle, Temitope Samson, Oluwaseyi Omotayo Alabi, Morolake Oladayo Lawrence, Godwin Nse Ebong, Grace Oluwamayowa Ajiboye, and Temitope Abiodun Bamisaye. "The Use of AI to Analyze Social Media Attacks for Predictive Analytics." Journal of Computing Theories and Applications 2, no. 2 (2024): 169–78. http://dx.doi.org/10.62411/jcta.10120.

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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 compile

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Adekunle, Temitope, Morolake Lawrence, Oluwaseyi Alabi, Godwin Ebong, Grace Ajiboye, and Temitope Bamisaye. "The Use of AI to Analyze Social Media Attacks for Predictive Analytics." American Journal of Operations Management and Information Systems 9, no. 1 (2024): 17–24. http://dx.doi.org/10.11648/j.ajomis.20240901.12.

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Social engineering, on the other hand, presents weaknesses that are difficult to directly quantify in penetration testing. The majority of expert social engineers utilize phishing and adware tactics to convince victims to provide information voluntarily. Social Engineering (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 a variety of posts, including

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Elkatatny, Salaheldin. "Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models." Sensors 20, no. 12 (2020): 3506. http://dx.doi.org/10.3390/s20123506.

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Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective of this paper is to evaluate the ability of the optimized adaptive neuro-f

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Alsuhaibani, Eyad. "Nondestructive Testing of Externally Bonded FRP Concrete Structures: A Comprehensive Review." Polymers 17, no. 9 (2025): 1284. https://doi.org/10.3390/polym17091284.

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The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo

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Mahdi, Doaa S., Emad A. Al-Khdheeawi, and Yujie Yuan. "AI-Based Estimation of Poisson’s Ratio for Carbonate Formations Using Drilling Parameters in a Southern Iraqi Oil Field." Journal of Petroleum Research and Studies 15, no. 2 (2025): 44–59. https://doi.org/10.52716/jprs.v15i2.1072.

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Wellbore instability is a significant issue encountered during drilling operations. The mechanical properties of the formation are among the many factors that affect wellbore instability. Poisson's ratio is one of these mechanical properties and is a key factor in mechanical earth modeling (GEM). It is extremely important to minimize risks in drilling and production operations like sand output, collapse, tight holes, and pipe sticking. Poisson's ratio estimation contributes to optimizing hydro-carbon recovery and making important choices for a suitable field development plan. Poisson's ratio (

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Ghule, Pallavi Shivaji. "Use of Nanotechnology in Cosmetics and Cosmeceuticals." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 160–67. http://dx.doi.org/10.22214/ijraset.2024.64488.

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Nanotechnology has revolutionized the cosmetic and cosmeceutical industries by enhancing product efficacy and enabling innovative formulations. This review paper delves into the various applications of nanotechnology in skincare and cosmetic products, highlighting its ability to improve the delivery of active ingredients, provide enhanced protection, and increase the bioavailability of beneficial compounds. Nanomaterials such as liposomes, nanoemulsions, nanocapsules, and solid lipid nanoparticles are now extensively utilized for their superior skin penetration capabilities, ensuring targeted

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A.Shaji, George, and S.Sagayarajan. "Securing Cloud Application Infrastructure: Understanding the Penetration Testing Challenges of IaaS, PaaS, and SaaS Environments." Partners Universal International Research Journal (PUIRJ) ISSN: 2583-5602 02, no. 01 (2023): 24–34. https://doi.org/10.5281/zenodo.7723187.

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Cloud computing is rapidly becoming the go-to platform for businesses of all sizes, from start-ups to large enterprises. With this shift comes a responsibility to ensure that cloud applications are secure and can protect data from malicious actors. Joint responsibility model of cloud security says that while service providers have to make sure their services are secure, businesses that use those services must also take steps to maintain their own level of security. Identity as well as access management is a shared responsibility model that offloads some of the application security responsibili

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Anikin, Nikita, Anastasiia Dik, Diana Yambrovskaya, Ksenia Mikhaylova, Taras Markvashev, and Elena Petrenko. "The Role of Interactive Technologies in Optimizing the Process of Patient Treatment: Prospects and Challenges for Health Care." Journal of Complementary Medicine Research 14, no. 3 (2023): 106. http://dx.doi.org/10.5455/jcmr.2023.14.03.19.

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The introduction of medical digital technologies can provide better access and flexibility of healthcare for the population. It includes the availability of open information about health, treatment, complications, and recent progress in biomedical research. Nowadays, even in low-income countries, diagnostic and medical services are becoming more accessible. However, many issues related to digital health technologies remain unresolved, including reliability, security, testing, and ethical issues.The penetration of AI currently affects almost all areas of work of medical organizations and govern

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Omidian, Hossein, and Sumana Dey Chowdhury. "Multifunctional Hydrogel Microneedles (HMNs) in Drug Delivery and Diagnostics." Gels 11, no. 3 (2025): 206. https://doi.org/10.3390/gels11030206.

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Hydrogel microneedles (HMNs) have emerged as a transformative platform for minimally invasive drug delivery and biosensing, offering enhanced bioavailability, controlled drug release, and real-time biomarker detection. By leveraging swelling hydrogels, nanomaterial integration, and stimuli-responsive properties, HMNs provide precision medicine capabilities across diverse therapeutic and diagnostic applications. However, challenges remain in mechanical stability, as hydrogel-based MNs must balance flexibility with sufficient strength for skin penetration. Drug retention and controlled release r

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Vladimer Tsimakuridze, Vladimer Tsimakuridze. "Possibilities of Practical Use of Artificial Intelligence in Marketing Processes." Economics 106, no. 3-5 (2024): 28–34. http://dx.doi.org/10.36962/ecs106/3-5/2024-28.

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This article discusses the possibilities of practical use of artificial intelligence in marketing processes. It is unambiguous, that marketing has entered a new era of development (marketing control) and day by day, the scale of changes brought by digital transformation becomes visible to the field of marketing, in terms of challenges and changes. No one can definitively say whether human creativity will be replaced by artificial intelligence, especially since artificial intelligence, in terms of content generation, is actively used by leading companies. For instance, it is significant that ve

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Kumar, Sahil, Rabia Afzaal, Riffat Bibi, et al. "SYNERGIZING AI, IOT, AND NANOTECHNOLOGY FOR INNOVATIVE MEDICAL DIAGNOSTICS AND THERAPEUTIC SOLUTIONS." Journal of Neonatal Surgery 14, no. 20S (2025): 334–45. https://doi.org/10.63682/jns.v14i20s.5019.

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Background: The three technologies mentioned above, Artificial Intelligence (AI), the Internet of Things (IoT), and Nanotechnology, are still believed to transform healthcare diagnosis and therapies in the future. These technologies promise to improve accuracy, workflow, and patient-centered practices, but barriers to implementing them persist: privacy, regulation, and technical skills. Objective: This research thus sought to establish the level of awareness and understanding of the professionals concerning AI, IoT, & nanotechnology in Healthcare; The level of readiness of these intelligen

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Al-Omari, Ahmad H., Ahmad Ghazi Alshanty, Ayoub Alsarhan, and Saifullah A. Omari. "OTP security in wallet systems: A vulnerability assessment." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 2356–71. https://doi.org/10.53894/ijirss.v8i2.5691.

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This study investigates One-Time Password (OTP) vulnerabilities in digital wallet systems, employing penetration testing and manual security evaluations (via Burp Suite) to assess risks across authentication, fund transfers, IVR verification, and token lifecycle management. Critical gaps including OTP bypass (e.g., header manipulation, token reuse), unauthorized beneficiary additions, brute-force attacks on weak pincodes, and expired OTP exploitation, highlight systemic flaws in server-side validation and API security. Classified using CVSS and ISO/IEC 27005:2018 frameworks, these vulnerabilit

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Fiorentini, Nicholas, and Massimo Losa. "Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data." Remote Sensing 17, no. 5 (2025): 917. https://doi.org/10.3390/rs17050917.

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Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue

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Aitkhozhayeva, Yevgeniya, Aasso Ziro, Zhanshuak Zhaibergenova, and Aliya Baltabay. "PENETRATION TESTING." BULLETIN 6, no. 376 (2018): 39–44. http://dx.doi.org/10.32014/2018.2518-1467.25.

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Hardy, Gary. "Penetration testing." Computers & Security 16, no. 6 (1997): 524. http://dx.doi.org/10.1016/s0167-4048(97)84691-3.

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Gupta, Ujjwal. "Web Penetration Testing." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 56–60. http://dx.doi.org/10.22214/ijraset.2020.5011.

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LaBarge, Ralph. "Cloud Penetration Testing." International Journal on Cloud Computing: Services and Architecture 2, no. 6 (2012): 43–62. http://dx.doi.org/10.5121/ijccsa.2012.2604.

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Arkin, B., S. Stender, and G. McGraw. "Software penetration testing." IEEE Security and Privacy Magazine 3, no. 1 (2005): 84–87. http://dx.doi.org/10.1109/msp.2005.23.

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Thompson, H. H. "Application penetration testing." IEEE Security and Privacy Magazine 3, no. 1 (2005): 66–69. http://dx.doi.org/10.1109/msp.2005.3.

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Bishop, Matt. "About Penetration Testing." IEEE Security & Privacy Magazine 5, no. 6 (2007): 84–87. http://dx.doi.org/10.1109/msp.2007.159.

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MIHALACHE, Denisa-Nicoleta. "Cloud Penetration Testing." International Journal of Information Security and Cybercrime 12, no. 2 (2023): 49–54. http://dx.doi.org/10.19107/ijisc.2023.02.06.

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The utilization of cloud computing has been growing exponentially, becoming the preferred platform for businesses of all sizes, from start-ups to large corporations. However, this shift towards cloud computing brought about the responsibility to ensure the security of cloud applications and data from malicious attacks. The joint responsibility model of cloud security requires both service providers and businesses to maintain security. Identity and access management are shared responsibility models, requiring protocols and ethical hacking to ensure data protection. Advanced security measures li

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Ralph, LaBarge1 and Thomas McGuire2. "CLOUD PENETRATION TESTING." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 2, December (2018): 01–20. https://doi.org/10.5281/zenodo.1435315.

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This paper presents the results of a series of penetration tests performed on the OpenStack Essex Cloud Management Software. Several different types of penetration tests were performed including network protocol and command line fuzzing, session hijacking and credential theft. Using these techniques exploitable vulnerabilities were discovered that could enable an attacker to gain access to restricted information contained on the OpenStack server, or to gain full administrative privileges on the server. Key recommendations to address these vulnerabilities are to use a secure protocol, such as H

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Brito, Thawane Rodrigues, David Pessoa Guedes, José Reinaldo Moreira da Silva, Edy Eime Pereira Baraúna, and Juarez Benigno Paes. "Influence of chromated copper arsenate type-C concentration on the quality of treated fence posts." Research, Society and Development 11, no. 4 (2022): e21411427088. http://dx.doi.org/10.33448/rsd-v11i4.27088.

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This study aimed to reduce the concentration of chromated copper arsenate type-C (CCA-C) in preservative treatments of Eucalyptus sp. for use in applications with ground contact. The genetic materials used were Eucalyptus cloeziana of seminal origin and the clonal hybrid (E. urophylla x E. camaldulensis), which was derived from six year old crops at commercial plantations. The basic density, moisture content, percentage of sapwood, and wood treatable volume were determined, and the anatomical structures were characterized. The genetic materials were treated with concentrations of 1.2%, 1.3%, 1

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