Relevant bibliographies by topics / AI in Penetration Testing

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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  6. Reports

Academic literature on the topic 'AI in Penetration Testing'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "AI in Penetration Testing"

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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 limitations. By analyzing both the advantages and disadvantages of automation in penetration testing, this report highlights the efficiency gains in vulnerability detection and the potential pitfalls, such as false positives and the limited scope of predefined attack scenarios. The integration of artificial intelligence and machine learning into automated testing is also discussed, emphasizing its role in enhancing the adaptability and accuracy of these tools. While automation significantly improves the speed and scalability of penetration testing, it is not a replacement for human expertise. The conclusion underscores the importance of combining automated tools with manual testing to ensure comprehensive security coverage, especially as cyber threats continue to evolve.
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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 testing learning platform. The proposed approach integrates large language models (LLM) into the penetration testing lifecycle through a user-friendly GUI tool. The tool addresses the void in beginner-friendly ethical hacking tools by offering a stepwise guide, built-in commands and justifications, report generation, and an LLM prompt-engineered output displayed in a simple tabular format for easy reference. Furthermore, the tool provides an interactive menu for each phase of the penetration lifecycle thereby guiding users through common penetration testing commands. To cater to deeper learning needs, the tool leverages LLMs to furnish additional information on commands, empowering users with AI-generated insights. With the capability to compile a comprehensive report with all commands and logs acquired during its use, the proposed tool has the potential to reduce the time spent on research and decision-making. In addition, it streamlines the learning curve, allowing a more informed and structured approach to Pen-testing for beginners. By leveraging this platform, academics and learners can enhance their penetration testing knowledge without the complexities associated with learning penetration testing.
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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 proposing a holistic approach towards maximizing the efficiency as well as effectiveness of Pen-Testing processes. Furthermore, it also identifies the need for machine learning techniques such as reinforcement learning and deep reinforcement learning for automating Pen-Testing activities. MITRE ATT&CK Framework being utilized within the proposed model imitates real-life cyber-attacks and exploits hence facilitating automated testing across diverse target networks. Based on comparison with manual penetration testing reports, this study reviews how effective the new automated method is when compared to old ways used in manual penetration tests while providing some direction for future developments along with suggestions in the field of self- governing intrusion detection systems (IDS)
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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 exploit data, and dynamic attack path generation using reinforcement learning agents. Through empirical evaluation on simulated enterprise environments, our prototype system demonstrates improved detection of deep-seated vulnerabilities and reduction in time-to-compromise metrics compared to conventional methods. We also examine the implications of adversarial machine learning, model drift, and AI explain ability within red team operations, highlighting the need for robust oversight mechanisms. The findings suggest that AI-augmented penetration testing can significantly enhance proactive threat identification and emulate advanced persistent threat (APT) behavior, offering a powerful tool for defenders in a rapidly evolving threat landscape.
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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 resource requirements compared to traditional manual approaches. The article highlights how machine learning models, particularly deep neural networks and ensemble approaches, enable continuous, adaptive security assessment capabilities that effectively identify and respond to emerging threats. Through empirical analysis, we document substantial reductions in false positive rates and marked improvements in scalability across diverse enterprise architectures. The article also addresses critical considerations regarding ethical implications, compliance requirements, and integration challenges with existing security infrastructure. The article results indicate that AI-powered penetration testing represents a significant advancement in enterprise security, offering organizations more robust, efficient, and cost-effective means of protecting their digital assets against evolving cyber threats. This article research contributes to the growing body of knowledge in automated security assessment and provides valuable insights for organizations seeking to enhance their security posture through advanced technologies.
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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 simulation should conduct its attacks the same way a real attacker would. Hence the ultimate goal is much more ambitious: to realistically simulate a human hacker. This is a grand vision indeed; e.g., the classical Turing Test can be viewed as a sub-problem. Taking a more practical perspective, the simulated pentesting model space spans a broad range of sequential decision making problems. Analyzing prior work in AI and other relevant areas, we derive a systematization of this model space, highlighting a multitude of interesting challenges to AI sequential decision making research.
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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 testing, and industry standards such as the Open Web Application Security Project (OWASP) and the Open Source Security Testing Methodology Manual (OSSTMM). Legal and ethical considerations surrounding ethical hacking are discussed, emphasizing compliance with cybersecurity regulations, responsible disclosure, and professional ethics. The research also examines challenges faced by ethical hackers, including evolving cyber threats, advanced attack techniques, and the growing complexity of IT environments. Emerging technologies such as artificial intelligence and automation in penetration testing are analyzed for their potential impact on cybersecurity. Real-world case studies highlight the effectiveness of ethical hacking in preventing cyberattacks and improving security postures across various industries. This study underscores the importance of integrating ethical hacking and penetration testing into cybersecurity frameworks to proactively mitigate risks, enhance digital resilience, and ensure compliance with security standards. Keywords—Ethical Hacking, Penetration Testing, Cybersecurity Vulnerabilities, AI in Security Testing, Legal and Ethical Considerations
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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 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.
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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 detection, traffic monitoring, and attack simulations. These tools have better key performance metrics, such as scan time, false positive rate, detection accuracy, mean time to detect, zero-day threats/month, compared to traditional penetration testing tools.
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Dissertations / Theses on the topic "AI in Penetration Testing"

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Nilsson, Robin. "Penetration testing of Android applications." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280290.

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The market of Android applications is huge, and in 2019, Google Play users worldwide downloaded 84.3 billion mobile applications. With such a big user base, any security issues could have big negative impacts. That is why penetration testing of Android applications is important and it is also why Google has a bug bounty program where people can submit vulnerability reports on their most downloaded applications. The aim of the project was to assess the security of Android applications from the Google Play Security Reward Program by performing penetration tests on the applications. A threat model of Android applications was made where potential threats were identified. A choice was made to focus on the Spotify Application for Android where threats were given ratings based on risks associated with them in the context of the Spotify Application. Penetration tests were made where testing depth was determined by the ratings associated with the attacks.The results of the tests showed that the Spotify Application is secure, and no test showed any real possibility of exploiting the application. The perhaps biggest potential exploit found is a Denial of Service attack that can be made through a malicious application interacting with the Spotify application. The result doesn’t guarantee that the application isn’t penetrable and further testing is needed to give the result more reliability. The methods used in the project can however act as a template for further research into both Spotify and other Android applications.<br>Marknaden för Android applikationer är enorm och 2019 laddade Google Play användare ner 84.3 miljarder mobil-applikationer. Med en så stor användarbas kan potentiella säkerhetsproblem få stora negativa konsekvenser. Det är därför penetrationstest är viktiga och varför Google har ett bug bounty program där folk kan skicka in sårbarhetsrapporter för deras mest nedladdade applikationer. Målet med projektet är att bedöma säkerheten hos Android applikationer från Google Play Security Reward Program genom utförande av penetrationstester på applikationerna. En hotmodell över Android applikationer skapades, där potentiella hot identifierades. Ett val att fokusera på Spotify för Android gjordes, där hot gavs rankingar baserat på riskerna associerade med dem i kontexten av Spotify applikationen. Penetrationstest gjordes med testdjup avgjort av rankingarna associerade med attackerna.Resultatet av testen visade att Spotify applikationen var säker, och inga test visade på några riktiga utnyttjningsmöjligheter av applikationen. Den kanske största utnyttjningsmöjligheten som hittades var en Denial of Service-attack som kunde göras genom en illvillig applikation som interagerar med Spotify applikationen. Resultaten garanterar inte att applikationen inte är penetrerbar och fortsatt testande behövs för att ge resultatet mer trovärdighet. Metoderna som användes i projektet kan i alla fall agera som en mall för fortsatt undersökning av både Spotify såväl som andra Android applikationer.
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McCallum, Adrian Bruce. "Cone penetration testing in polar snow." Thesis, University of Cambridge, 2012. https://www.repository.cam.ac.uk/handle/1810/244073.

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Innovative Cone Penetration Testing (CPT) using adapted commercial CPT equipment was conducted in Antarctica in early 2010 in an attempt to assess the strength of polar snow; additionally, application of CPT data was considered, particularly in estimating surface bearing capacity. Almost 100 CPT tests were carried out and both qualitative and quantitative analysis of data was undertaken. Additional supporting testing in- cluded snow density assessment, snow strength assessment, extrapolation of CPT data via Ground Penetrating Radar (GPR) and preliminary mini-cone penetrometer testing in Greenland. Analysis of results revealed that assessing the strength of polar snow via CPT is affected by numerous factors including penetration rate, cone size/shape and snow material properties, particularly compaction of the snow undergoing penetration. A density-dependant relationship between CPT resistance and snow shear strength was established, and methods for estimating surface bearing capacity directly from CPT in homogeneous and layered polar snow were proposed. This work applied existing technology in a new material and shows that CPT can be used efficiently in polar environs to provide estimates of snow shear strength and surface bearing capacity, to depths of 10 m or more.
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Lu, Yuebin. "Deep penetration microindentation testing of polymers." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0019/NQ58147.pdf.

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Vernersson, Susanne. "Penetration Testing in a Web Application Environment." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-8934.

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As the use of web applications is increasing among a number of different industries, many companies turn to online applications to promote their services. Companies see the great advantages with web applications such as convenience, low costs and little need of additional hardware or software configuration. Meanwhile, the threats against web applications are scaling up where the attacker is not in need of much experience or knowledge to hack a poorly secured web application as the service easily can be accessed over the Internet. While common attacks such as cross-site scripting and SQL injection are still around and very much in use since a number of years, the hacker community constantly discovers new exploits making businesses in need of higher security. Penetration testing is a method used to estimate the security of a computer system, network or web application. The aim is to reveal possible vulnerabilities that could be exploited by a malicious attacker and suggest solutions to the given problem at hand. With the right security fixes, a business system can go from being a threat to its users’ sensitive data to a secure and functional platform with just a few adjustments. This thesis aims to help the IT security consultants at Combitech AB with detecting and securing the most common web application exploits that companies suffer from today. By providing Combitech with safe and easy methods to discover and fix the top security deficiencies, the restricted time spent at a client due to budget concerns can be made more efficient thanks to improvements in the internal testing methodology. The project can additionally be of interest to teachers, students and developers who want to know more about web application testing and security as well as common exploit scenarios.
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Finke, Kimberly Ann. "Piezocone penetration testing in Piedmont residual soils." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/21452.

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Hepton, Peter. "Shear wave velocity measurements during penetration testing." Thesis, Bangor University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.330070.

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Dazet, Eric Francis. "ANEX: Automated Network Exploitation through Penetration Testing." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1592.

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Cyber attacks are a growing concern in our modern world, making security evaluation a critical venture. Penetration testing, the process of attempting to compromise a computer network with controlled tests, is a proven method of evaluating a system's security measures. However, penetration tests, and preventive security analysis in general, require considerable investments in money, time, and labor, which can cause them to be overlooked. Alternatively, automated penetration testing programs are used to conduct a security evaluation with less user effort, lower cost, and in a shorter period of time than manual penetration tests. The trade-off is that automated penetration testing tools are not as effective as manual tests. They are not as flexible as manual testing, cannot discover every vulnerability, and can lead to a false sense of security. The development of better automated tools can help organizations quickly and frequently know the state of their security measures and can help improve the manual penetration testing process by accelerating repetitive tasks without sacrificing results. This thesis presents Automated Network Exploitation through Penetration Testing (ANEX), an automated penetration testing system designed to infiltrate a computer network and map paths from a compromised network machine to a specified target machine. Our goal is to provide an effective security evaluation solution with minimal user involvement that is easily deployable in an existing system. ANEX demonstrates that important security information can be gathered through automated tools based solely on free-to-use programs. ANEX can also enhance the manual penetration testing process by quickly accumulating information about each machine to develop more focused testing procedures. Our results show that we are able to successfully infiltrate multiple network levels and exploit machines not directly accessible to our testing machine with mixed success. Overall, our design shows the efficacy of utilizing automated and open-source tools for penetration testing.
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Cameron, Booth Louis, and Matay Mayrany. "IoT Penetration Testing: Hacking an Electric Scooter." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254613.

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The industry of the Internet of Things (IoT) is a burgeoning market. A wide variety of devices now come equipped with the ability to digitally communicate to a wider network and modern electric scooters are one such example of this trend towards a more connected society. With scooter ride-share companies continually expanding in urban areas worldwide these devices are posing a greater attack surface for hackers to take advantage of. In this report we utilize threat modelling to analyse the potential vulnerabilities in a popular electric scooter. Through penetration testing we demonstrate the existence of major security flaws in the device and propose ways in which manufacturers may guard against these exploits in the future.<br>Internet-of-Things (IoT) växer globalt. Många produkter kommer utrustade med förmågan att digitalt kommunicera med olika nätverk och moderna elektroniska sparkcyklar är ett exempel på denna trend som går mot ett mer uppkopplat och sammankopplat samhälle. I och med att antalet företag som tillhandahåller elsparkcykeltjänster i urbana miljöer över världen växer, så blir dessa produkter ett större mål för hackare att utnyttja. I denna rapport använder vi hotmodellering för att analysera potentiella sårbarheter i en populär elsparkcykelmodell. Genom att penetrationstesta produkten demonstrerar vi allvarliga säkerhetsfel och föreslår förhållningssätt som tillverkare kan ta hänsyn till för att undvika framtida attacker.
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Pozza, Simone. "Penetration testing negli ambienti Windows e Active Directory." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Il penetration testing ha l’obiettivo di individuare e sfruttare i punti deboli della sicurezza di un sistema informatico evitando al contempo di danneggiare il sistema stesso. Quest’attività è di particolare rilevanza per le imprese, la maggior parte delle quali fa largo impiego di sistemi operativi Windows e dei servizi per la loro gestione inclusi in Active Directory. Partendo dall’ipotesi che all’interno di una rete aziendale esista già una singola macchina di cui un attaccante ha il controllo (scenario assume breach), sono stati identificati ed esaminati i metodi e gli strumenti più efficaci per la valutazione della sicurezza negli ambienti Windows e Active Directory. Sono stati quindi sviluppati nuovi tool che automatizzano metodologie non implementate da strumenti esistenti, al fine di ridurre i tempi necessari per il penetration testing. Sia per gli strumenti analizzati che per quelli sviluppati ex novo, sono stati messi in luce i dettagli di come questi operano e i rischi del loro utilizzo all’interno di un’organizzazione. La tesi è strutturata in modo da riflettere l’ordine logico di un penetration test completo. Il capitolo 2 presenta attacchi che consentono di ottenere credenziali Windows tramite sniffing e spoofing del traffico di rete. Il capitolo 3 è dedicato al bypass delle restrizioni all’esecuzione di software sulle macchine Windows. Infine, il capitolo 4 delinea un approccio sistematico per acquisire privilegi sempre più elevati all’interno di un’istanza di Active Directory.
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Eid, Walid Khaled. "Scaling effect in cone penetration testing in sand." Diss., Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/49849.

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The Cone Penetration Test (CPT) was developed originally in Holland in the 1930’s as a device which provides a small scale model of a pile foundation. Early versions were simple cone points for which the only measurement was the thrust required to push the point through the ground. Over the past 20 years, the cone was standardized to a tip area of 10 cm², and an electrical version was produced, which allows for continuous measurement of the cone tip resistance and sleeve friction along with a computer-based data acquisition system. The electrical cone represents a significant step forward for the CPT, since it provides a continuous profile of information that can be used to identify soil type and define important engineering parameters. More recently, the CPT has shown considerable potential for calculation of settlements of footings on sand, determination of pile capacity, assessment of ground pressures, and evaluation of liquefaction potential for cohesionless soils. Along with the widening application of the CPT, new varieties of cone penetrometers have appeared, with different sizes than the standard. Smaller cones are used for instances where relatively small depths of soil need to be penetrated, and larger cones have been developed for penetrating dense and gravelly soils. With the introduction of the new cones, there has been a tendency to assume that the methods for reducing CPT data for the standard sized cone can be extrapolated to the other sizes of cones. That is, it is assumed that there are no scale effects in cones of different sizes. While this may be true, to date, little direct evidence has been produced to support this view, and the issue is an important one from two points of view: 1. The present data analysis technology is based on that primarily from testing with a standard cone. lt is important to know if any changes are needed in this approach, or if the existing methods can be used with confidence for any size cone. 2. If it can be shown that no scale factor exists, then this will allow the use of new, smaller cones in experimentation in modem calibration chambers with the knowledge that the test results are applicable for the cones that a.re more widely used in practice. The smaller cones offer several advantages in this type of work in that they facilitate the research considerably by reducing the effort involved in sample preparation, and they are less likely to produce results influenced by boundary conditions in the chamber. One of the major objectives of this research is to develop an insight into the issue of the scale factor caused by the use of different sizes of cones. This is accomplished through an experimental program conducted in a new large scale calibration chamber recently constructed at Virginia Tech. Many of the latest developments in cone penetration testing have been forthcoming from testing done in calibration chambers where a soil mass can be placed to a controlled density under known stress conditions. To conduct the experimentation of this work, it was necessary to design, fabricate, and bring to an operational stage a calibration chamber. The Virginia Tech chamber is one of the largest in the world. A significant portion of the effort involved in this thesis research was devoted to this task. In particular, attention was devoted to the development of a system for placement of a homogeneous soil mass in the chamber, and the implementation of a microcomputer-based data acquisition unit to record and process the test results. The scale effects investigation was performed using three different sizes of cone penetrometers in a test program conducted in the calibration chamber. Of the three cones, one is smaller than the standard with a tip area of 4.23 cm², one was a standard cone with a tip area of 10 cm², and one was larger than the standard cone with a tip area of 15 cm². A total of 47 tests were carried in the chamber using two different levels of confining stress and two different sand densities. The test results show that while a scale factor might exist, the degree of its influence on interpreted soil parameters for a practical problem does not appear significant.<br>Ph. D.<br>incomplete_metadata
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Books on the topic "AI in Penetration Testing"

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Mayne, Paul W. Cone penetration testing. Transportation Research Board, National Research Council, 2007.

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Allsopp, Wil. Advanced Penetration Testing. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119367741.

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Messier, Ric. Penetration Testing Basics. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1857-0.

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Oriyano, Sean-Philip. Penetration Testing Essentials. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119419358.

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Relan, Kunal. iOS Penetration Testing. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2355-0.

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Gottardi, Guido, and Laura Tonni. Cone Penetration Testing 2022. CRC Press, 2022. http://dx.doi.org/10.1201/9781003308829.

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Gottardi, Guido, and Laura Tonni. Cone Penetration Testing 2022. CRC Press, 2022. http://dx.doi.org/10.1201/9781003329091.

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Crest. Procuring penetration testing services. IT Governance Pub., 2014.

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Chris, Hurley, ed. WarDriving & Wireless Penetration Testing. Syngress, 2007.

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Wolf, Halton, ed. Computer security and penetration testing. Thomson, 2008.

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Book chapters on the topic "AI in Penetration Testing"

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Tehranipoor, Mark, Kimia Zamiri Azar, Navid Asadizanjani, Fahim Rahman, Hadi Mardani Kamali, and Farimah Farahmandi. "SoC Security Verification Using Fuzz, Penetration, and AI Testing." In Hardware Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58687-3_4.

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Elashmawi, Walaa H., Heba Osman, Mahmoud Osama, and Nour Nader. "Development Generative AI for Cybersecurity: Evaluating Script Generation and Attack Classification in Penetration Testing." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77229-0_6.

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La, Vinh Hoa, Wissam Mallouli, Manh Dung Nguyen, et al. "Enhancing IoT Security in 6G Networks: AI-Based Intrusion Detection, Penetration Testing, and Blockchain-Based Trust Management (Work-in-Progress Paper)." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-82065-6_5.

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Haber, Morey J., and Brad Hibbert. "Penetration Testing." In Asset Attack Vectors. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3627-7_10.

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Rahalkar, Sagar Ajay. "Penetration Testing." In Certified Ethical Hacker (CEH) Foundation Guide. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2325-3_7.

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Caddy, Tom. "Penetration Testing." In Encyclopedia of Cryptography and Security. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_214.

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Weik, Martin H. "penetration testing." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_13787.

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Sheikh, Ahmed. "Penetration Testing." In Certified Ethical Hacker (CEH) Preparation Guide. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7258-9_16.

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Moallem, Abbas. "Penetration Testing." In Understanding Cybersecurity Technologies. CRC Press, 2021. http://dx.doi.org/10.1201/9781003038429-17.

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Edwards, Dr Jason. "Penetration Testing." In Critical Security Controls for Effective Cyber Defense. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0506-6_18.

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Conference papers on the topic "AI in Penetration Testing"

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Sivakoumar, R., and Samson Reyas M. P. "Next-Gen Penetration Testing: AI, Automation & Beyond." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011887.

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Salem, Maher, and Mohammad Mrian. "AI-Driven Penetration Testing: Automating Exploits with LLMs and Metasploit-A VSFTPD Case Study." In 2025 International Conference on New Trends in Computing Sciences (ICTCS). IEEE, 2025. https://doi.org/10.1109/ictcs65341.2025.10989363.

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Sun, Yue, Cong Hou, and Yuyue Su. "Research on the Penetration Testing Model in the Vertical Field Using Generative AI Technology." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10990542.

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Bianou, Stanislas G., and Rodrigue G. Batogna. "PENTEST-AI, an LLM-Powered Multi-Agents Framework for Penetration Testing Automation Leveraging Mitre Attack." In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2024. http://dx.doi.org/10.1109/csr61664.2024.10679480.

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Wang, Wei-Che. "Legal, Policy, and Compliance Issues in Using AI for Security: Using Taiwan's Cybersecurity Management Act and Penetration Testing as Examples." In 2024 16th International Conference on Cyber Conflict: Over the Horizon (CyCon). IEEE, 2024. http://dx.doi.org/10.23919/cycon62501.2024.10685586.

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B M, Chandrakala, Annapurna P. Patil, Girija R, et al. "RESPLOIT FRAMEWORK FOR PENETRATION TESTING." In First International Conference on Computer, Computation and Communication (IC3C-2025). River Publishers, 2025. https://doi.org/10.13052/rp-9788743808268a006.

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Huang, Yu, Alex Yu, Louis Liu, and Xijiang Lin. "AI Driven Testing." In 2024 IEEE International Test Conference in Asia (ITC-Asia). IEEE, 2024. http://dx.doi.org/10.1109/itc-asia62534.2024.10661318.

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Gardner, Evan, Gurmeet Singh, and Weihao Qu. "Penetration Testing Operating Systems: Exploiting Vulnerabilities." In 2024 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). IEEE, 2024. http://dx.doi.org/10.1109/ccci61916.2024.10736454.

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Badarneh, Hazem Jihad, Loai Ahmad Attiany, Mahmoud Asassfeh, et al. "The power of Network Penetration Testing." In 2024 25th International Arab Conference on Information Technology (ACIT). IEEE, 2024. https://doi.org/10.1109/acit62805.2024.10877214.

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Vadla, Karthik, K. Gayathri Teja, Shaik Amanuddin, Anjali Bhatta, and M. Rajeshwar. "Integrating Penetration Testing Tools into a Website." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724706.

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Reports on the topic "AI in Penetration Testing"

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Wiemer, G. Cone penetration testing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/297874.

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Kurfurst, P. J., and D. J. Woeller. Cone Penetration Testing of Unit B. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/132230.

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Clauss, D. B. Severe accident testing of electrical penetration assemblies. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/5265225.

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Constantine, Monica M., and Jacob W. Ulvila. Testing and Evaluating C3I Systems That Employ AI. Volume 2. Compendium of Lessons Learned from Testing AI Systems in the Army. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada266359.

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Macedo, Jorge, Paul Mayne, Sheng Dai, et al. Cone Penetration Testing for Illinois Subsurface Characterization and Geotechnical Design. Illinois Center for Transportation, 2024. http://dx.doi.org/10.36501/0197-9191/24-013.

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Abstract:
This project sets the stage for implementing a cone penetration testing (CPT) practice in the state of Illinois, providing recommendations for using and interpreting CPT soundings in subsurface investigations. This study also contributes to modernizing Illinois Department of Transportation (IDOT) policy to current CPT-related practices adopted in other departments of transportation, which is consistent with recommendations from the Federal Highway Administration. Toward the goals of this project, CPT soundings across the nine districts of the state of Illinois have been conducted, and available CPT data at IDOT have been evaluated. The generated data have been uniformly processed, generating a database of 156 CPTs distributed across the nine districts. The database also includes shear wave velocity profiles at 28 locations and pore pressure dissipation tests at 45 locations. In addition, information (editable and non-editable) provided by IDOT has been carefully examined. The provided information consisted of boreholes with standard penetration test (SPT) data and laboratory tests on collected Shelby tubes. This information has been used to develop SPT-CPT correlations that can be applied in the state of Illinois and to provide examples of interpreting seismic piezocone test (SCPTu) data in the context of laboratory testing. Due to the several independent, fast, and reliable measurements that can be conducted in a CPT sounding and the fact that they can be done cost-effectively, CPT soundings are increasingly being preferred for in situ testing, and their adoption at IDOT is a positive step forward and consistent with the best practices at other U.S. departments of transportation.
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Wilke, Rudeger, and Jia Belanger. Towards AI Based Data Classification for Decision Making During Testing. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2516874.

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Aiken, Catherine. Classifying AI Systems. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200025.

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This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.
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Rohay, V. J. Electronic cone penetration testing at the Hanford Site, 200 West Area. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10177016.

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Adelman, Leonard, Jacob W. Ulvila, and Paul E. Lehner. Testing and Evaluating C3I Systems That Employ AI. Volume 1. Handbook for Testing Expert Systems. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada266358.

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Ulvila, Jacob W., Leonard Adelman, Monica M. Constantine, Paul E. Lehner, and Elissa Gilbert. Testing and Evaluating C3I Systems That Employ AI. Volume 4. Published Articles. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada266361.

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