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Though the basic concept of a ledger that anyone can view and verify has been around for quite some time, today’s blockchains bring much more to the table including a way to incentivize users. The coins given to the miner or validator were the first source of such incentive to make sure they fulfilled their duties. This thesis draws inspiration from other peer efforts and uses this same incentive to achieve certain goals. Primarily one where users are incentivised to discuss their opinions and find scientific or logical backing for their standpoint. While traditional chains form a consensus on a version of financial "truth", the same can be applied to ideological truths too. To achieve this, creating a modified or scaled proof of stake consensus mechanism is explored in this work. This new consensus mechanism is a Reputation Scaled - Proof of Stake. This reputation can be built over time by voting for the winning side consistently or by sticking to one’s beliefs strongly. The thesis hopes to bridge the gap in current consensus algorithms and incentivize critical reasoning.
Die vorliegende Arbeit beschäftigt sich mit der Nutzung von Reinforcement Learning in der Informationsbeschaffungs-Phase eines Penetration Tests. Es werden Kernprobleme in den bisherigen Ansätzen anderer das Thema betreffender wissenschaftlicher Arbeiten analysiert und praktische Lösungsansätze für diese bisherigen Hindernisse vorgestellt und implementiert. Die Arbeit zeigt damit eine beispielhafte Implementierung eines Reinforcement Learning Agenten zur Automatisierung der Informationsbeschaffungs-Phase eines Penetration Tests und stellt Lösungen für existierende Probleme in diesem Bereich dar.
Eingebettet wird diese wissenschaftliche Arbeit in die Anforderungen der Herrenknecht AG hinsichtlich der Absicherung des Tunnelbohrmaschinen-Netzwerks. Dabei werden praktische Ergebnisse des eigen entwickelten Reinforcement Learning Modells im Tunnelbohrmaschinen-Test-Netzwerk der Herrenknecht AG vorgestellt.
This thesis focuses on the development and implementation of a Datagram Transport Layer Security (DTLS) communication framework within the ns-3 network simulator, specifically targeting the LoRaWAN model network. The primary aim is to analyse the behaviour and performance of DTLS protocols across different network conditions within a LoRaWAN context. The key aspects of this work include the following.
Utilization of ns-3: This thesis leverages ns-3’s capabilities as a powerful discrete event network simulator. This platform enables the emulation of diverse network environments, characterized by varying levels of latency, packet loss, and bandwidth constraints.
Emulation of Network Challenges: The framework specifically addresses unique challenges posed by certain network configurations, such as duty cycle limitations. These constraints, which limit the time allocated for data transmission by each device, are crucial in understanding the real-world performance of DTLS protocols.
Testing in Multi-client-server Scenarios: A significant feature of this framework is its ability to test DTLS performance in complex scenarios involving multiple clients and servers. This is vital for assessing the behaviour of a protocol under realistic network conditions.
Realistic Environment Simulation: By simulating challenging network conditions, such as congestion, limited bandwidth, and resource constraints, the framework provides a realistic environment for thorough evaluation. This allows for a comprehensive analysis of DTLS in terms of security, performance, and scalability.
Overall, this thesis contributes to a deeper understanding of DTLS protocols by providing a robust tool for their evaluation under various and challenging network conditions.
Linux and Linux-based operating systems have been gaining more popularity among the general users and among developers. Many big enterprises and large companies are using Linux for servers that host their websites, some even require their developers to have knowledge about Linux OS. Even in embedded systems one can find many Linux-based OS that run them. With its increasing popularity, one can deduce the need to secure such a system that many personnel rely on, be it to protect the data that it stores or to protect the integrity of the system itself, or even to protect the availability of the services it offers. Many researchers and Linux enthusiasts have been coming up with various ways to secure Linux OS, however new vulnerabilities and new bugs are always found, by malicious attackers, with every update or change, which calls for the need of more ways to secure these systems.
This Thesis explores the possibility and feasibility of another way to secure Linux OS, specifically securing the terminal of such OS, by altering the commands of the terminal, getting in the way of attackers that have gained terminal access and delaying, giving more time for the response teams and for forensics to stop the attack, minimize the damage, restore operations, and to identify collect and store evidence of the cyber-attack. This research will discuss the advantages and disadvantages of various security measures and compare and contrast with the method suggested in this research.
This research is significant because it paints a better picture of what the state of the art of Linux and Linux-based operating systems security looks like, and it addresses the concerns of security enthusiasts, while exploring new uncharted area of security that have been looked at as a not so significant part of protecting the OSes out of concern of the various limitations and problems it entails. This research will address these concerns while exploring few ways to solve them, as well as addressing the ideal areas and situations in which the proposed method can be used, and when would such method be more of a burden than help if used.
Truth is the first causality of war”, is a very often used statement. What rather intrigues the mind is what causes the causality of truth. If one dives deeper, one may also wonder why is this so-called truth the first target in a war. Who all see the truth before it dies. These questions rarely get answered as the media and general public tends to focus more on the human and economic losses in a war or war like situation. What many fail to realize is that these truthful pieces of information are critical to how a situation further develops. One correct information may change the course of the whole war saving millions and one mis-information may do the opposite.
Since its inception, some studies have been conducted to propose and develop new applications for OSINT in various fields. In addition to OSINT, Artificial Intelligence is a worldwide trend that is being used in conjunction witThe question here is, what is this information. Who transmits this and how? What is the source. Although, there has been an extensive use of the information provided by the secret services of any nation, which have come handy to many, another kind of information system is using the one that is publicly available, but in different pieces. This kind of information may come from people posting on social media, some publicly available records and much more. The key part in this publicly available information is that these are just pieces of information available across the globe from various different sources. This could be seen as small pieces of a puzzle that need to be put together to see the bigger picture. This is where OSINT comes in place.
h other areas (AI). AI is the branch of computer science that is in charge of developing intelligent systems. In terms of contribution, this work presents a 9-step systematic literature review as well as consolidated data to support future OSINT studies. It was possible to understand where the greatest concentration of publications was, which countries and continents developed the most research, and the characteristics of these publications using this information. What are the trends for the next OSINT with AI studies? What AI subfields are used with OSINT? What are the most popular keywords, and how do they relate to others over time?A timeline describing the application of OSINT is also provided. It was also clear how OSINT was used in conjunction with AI to solve problems in various areas with varying objectives. Private investigators and journalists are no longer the primary users of open-source intelligence gathering and analysis (OSINT) techniques. Approximately 80-90 percent of data analysed by intelligence agencies is now derived from publicly available sources. Furthermore, the massive expansion of the internet, particularly social media platforms, has made OSINT more accessible to civilians who simply want to trawl the Web for information on a specific individual, organisation, or product. The General Data Protection Regulation (GDPR) of the European Union was implemented in the United Kingdom in May 2018 through the new Data Protection Act, with the goal of protecting personal data from unauthorised collection, storage, and exploitation. This document presents a preliminary review of the literature on GDPR-related work.
The reviewed literature is divided into six sections: ’What is OSINT?’, ’What are the risks?’ and benefits of OSINT?’, ’What is the rationale for data protection legislation?’, ’What are the current legislative frameworks in the UK and Europe?’, ’What is the potential impact of the GDPR on OSINT?’, and ’Have the views of civilian and commercial stakeholders been sought and why is this important?’. Because OSINT tools and techniques are available to anyone, they have the unique ability to be used to hold power accountable. As a result, it is critical that new data protection legislation does not impede civilian OSINT capabilities.
In this paper we see how OSINT has played an important role in the wars across the globe in the past. We also see how OSINT is used in our everyday life. We also gain insights on how OSINT is playing a role in the current war going on between Russia and Ukraine. Furthermore, we look into some of these OSINT tools and how they work. We also consider a use case where OSINT is used as an anti terrorism tool. At the end, we also see how OSINT has evolved over the years, and what we can expect in the future as to what OSINT may look like.
Conceptualization and implementation of automated optimization methods for private 5G networks
(2023)
Today’s companies are adjusting to the new connectivity realities. New applications require more bandwidth, lower latency, and higher reliability as industries become more distributed and autonomous. Private 5th Generation (5G) networks known as 5G Non-Public Networks (5G-NPN), is a novel 3rd Generation Partnership Project (3GPP)- based 5G network that can deliver seamless and dedicated wireless access for a particular industrial use case by providing the mentioned application’s requirements. To meet these requirements, several radio-related aspects and network parameters should be considered. In many cases, the behavior of the link connection may vary based on wireless conditions, available network resources, and User Equipment (UE) requirements. Furthermore, Optimizing these networks can be a complex task due to the large number of network parameters and KPIs that need to be considered. For these reasons, traditional solutions and static network configuration are not affordable or simply impossible. Despite the existence of papers in the literature that address several optimization methods for cellular networks in industrial scenarios, more insight into these existing but complex or unknown methods is needed.
In this thesis, a series of optimization methods were implemented to deliver an optimal configuration solution for a 5G private network. To facilitate this implementation, a testing system was implemented. This system enables remote control over the UE and 5G network, establishment of a test environment, extraction of relevant KPI reports from both UE and network sides, assessment of test results and KPIs, and effective utilization of the optimization and sampling techniques.
The research highlights the advantageous aspects of automated testing by using OFAT, Simulated Annealing, and Random Forest Regressor methods. With OFAT, as a common sampling method, a sensitivity analysis and an impact of each single parameter variation on the performance of the network were revealed. With Simulated Annealing, an optimal solution with MSE of roughly 10 was revealed. And, in the Random Forest Regressor, it was seen that this method presented a significant advantage over the simulated annealing method by providing substantial benefits in time efficiency due to its machine- learning capability. Additionally, it was seen that by providing a larger dataset or using some other machine-learning techniques, the solution might be more accurate.
As e-commerce platforms have grown in popularity, new difficulties have emerged, such as the growing use of bots—automated programs—to engage with e-commerce websites. Even though some algorithms are helpful, others are malicious and can seriously hurt e-commerce platforms by making fictitious purchases, posting fictitious evaluations, and gaining control of user accounts. Therefore, the development of more effective and precise bot identification systems is urgently needed to stop such actions. This thesis proposes a methodology for detecting bots in E-commerce using machine learning algorithms such as K-nearest neighbors, Decision Tree, Random Forest, Support Vector Machine, and Neural Network. The purpose of the research is to assess and contrast the output of these machine learning methods. The suggested approach will be based on data that is readily accessible to the public, and the study’s focus will be on the research of bots in e-commerce.
The purpose of the study is to provide an overview of bots in e-commerce, as well as information on the different kinds and traits of bots, as well as current research on bots in e-commerce and associated work on bot detection in e-commerce. The research also seeks to create a more precise and effective bot detection system as well as find critical factors in detecting bots in e-commerce.
This research is significant because it sheds light on the increasing issue of bots in e-commerce and the requirement for more effective bot detection systems. The suggested approach for using machine learning algorithms to identify bots in ecommerce can give e-commerce platforms a more precise and effective bot detection system to stop malicious bot activities. The study’s results can also be used to create a more effective bot detection system and pinpoint key elements in detecting bots in e-commerce.
The Internet of Things is spreading significantly in every sector, including the household, a variety of industries, healthcare, and emergency services, with the goal of assisting all of those infrastructures by providing intelligent means of service delivery. An Internet of Vulnerabilities (IoV) has emerged as a result of the pervasiveness of the Internet of Things (IoT), which has led to a rise in the use of applications and devices connected to the IoT in our day-to-day lives. The manufacture of IoT devices are growing at a rapid pace, but security and privacy concerns are not being taken into consideration. These intelligent Internet of Things devices are especially vulnerable to a variety of attacks, both on the hardware and software levels, which leaves them exposed to the possibility of use cases. This master’s thesis provides a comprehensive overview of the Internet of Things (IoT) with regard to security and privacy in the area of applications, security architecture frameworks, a taxonomy of various cyberattacks based on various architecture models, such as three-layer, four-layer, and five-layer. The fundamental purpose of this thesis is to provide recommendations for alternate mitigation strategies and corrective actions by using a holistic rather than a layer-by-layer approach. We discussed the most effective solutions to the problems of privacy and safety that are associated with the Internet of Things (IoT) and presented them in the form of research questions. In addition to that, we investigated a number of further possible directions for the development of this research.
As cyber threats continue to evolve, it is becoming increasingly important for organizations to have a Security Operations Center (SOC) in place to effectively defend against them. However, building and maintaining a SOC can be a daunting task without clear guidelines, policies, and procedures in place. Additionally, most current SOC solutions used by organizations are outdated, lack key features and integrations, and are expensive to maintain and upgrade. Moreover, proprietary solutions can lead to vendor lock-in, making it difficult to switch to a different solution in the future.
To address these challenges, this thesis proposes a comprehensive SOC framework and an open-source SOC solution that provides organizations with a flexible and cost-effective way to defend against modern cyber threats. The research methodology involved conducting a thorough literature review of existing literature and research on building and maintaining a SOC, including using SOC as a service. The data collected from the literature review was analyzed to identify common themes, challenges, and best practices for building and maintaining a SOC.
Based on the data collected, a comprehensive framework for building and maintaining a SOC was developed. The framework addresses essential areas such as the scope and purpose of the SOC, governance and leadership, staffing and skills, technologies and tools, processes and procedures, service level agreements (SLAs), and evaluation and measurement. This framework provides organizations with the necessary guidance and resources to establish and effectively operate a SOC, as well as a reference for evaluating the service provided by SOC service providers.
In addition to the SOC framework, a modern open-source SOC solution was developed, which emphasizes several key measures to help organizations defend against modern cyber threats. These measures include real-time, actionable threat intelligence, rapid and effective incident response, continuous security monitoring and alerting, automation, integration, and customization. The use of open-source technologies and a modular architecture makes the solution cost-effective, allowing organizations to scale it up or down as needed.
Overall, the proposed SOC framework and open-source SOC solution provide organizations with a comprehensive and systematic approach for building and maintaining a SOC that is aligned with the needs and objectives of the organization. The open-source SOC solution provides a flexible and cost-effective way to defend against modern cyber threats, helping organizations to effectively operate their SOC and reduce their risk of security incidents and breaches.
Licht war für die Menschheit schon immer ein Hilfsmittel zur Orientierung. Das Zusammenspiel zwischen hellen und schattierten Oberflächen macht eine räumliche Wahrnehmung erst möglich. Die Lokalisierung von Lichtquellen bietet darüber hinaus für zahlreiche Anwendungsfelder, wie beispielsweise Augmented Reality, ein großes Potential.
Das Ziel der vorliegenden Arbeit war es, ein neuronales Netzwerk zu entwickeln, welches mit Hilfe eines selbst generierten, synthetischen Datensatzes eine Lichtsetzung parametrisiert. Dafür wurden State-of-the-Art Netzwerke aus der digitalen Bildverarbeitung eingesetzt.
Zu Beginn der Arbeit mussten die Eigenschaften der Lichtsetzung extrahiert werden. Eine weitere fundamentale Anforderung war die Aufbereitung des Wissens von Deep Learning.
Für die Generierung des synthetischen Datensatzes wurde eigens ein Framework entwickelt, welches auf der Blender Engine basiert.
Anschließend wurden die generierten Bilder und Metadaten in einem abgewandelten VGG16- und ResNet50-Netz trainiert, validiert und evaluiert.
Eine gewonnene Erkenntnis ist, dass sich künstlich generierte Daten eignen um ein neuronales Netz zu trainieren. Des Weiteren konnte gezeigt werden, dass sich mit Hilfe von Deep Learning Lichtsetzungsparameter extrahieren lassen.
Eine weiterführende Forschungsaufgabe könnte mit dem vorgeschlagenen Ansatzdie Lichtinszenierung von Augmented Reality Anwendungen verbessern.
Organizations striving to achieve success in the long term must have a positive brand image which will have direct implications on the business. In the face of the rising cyber threats and intense competition, maintaining a threat-free domain is an important aspect of preserving that image in today's internet world. Domain names are often near-synonyms for brand names for numerous companies. There are likely thousands of domains that try to impersonate the big companies in a bid to trap unsuspecting users, usually falling prey to attacks such as phishing or watering hole. Because domain names are important for organizations for running their business online, they are also particularly vulnerable to misuse by malicious actors. So, how can you ensure that your domain name is protected while still protecting your brand identity? Brand Monitoring, for example, may assist. The term "Brand Monitoring" applies only to keep tabs on an organization's brand performance, reception, and overall online presence through various online channels and platforms [1]. There has been a rise in the need of maintaining one's domain clear of any linkages to malicious activities as the threat environment has expanded. Since attackers are targeting domain names of organizations and luring unsuspecting users to visit malicious websites, domain monitoring becomes an important aspect. Another important aspect of brand abuse is how attackers leverage brand logos in creating fake and phishing web pages. In this Master Thesis, we try to solve the problem of classification of impersonated domains using rule-based and machine learning algorithms and automation of domain monitoring. We first use a rule-based classifier and Machine Learning algorithms to classify the domains gathered into two buckets – "Parked" and "Non-Parked". In the project's second phase, we will deploy object detection models (Scale Invariant Feature Transform - SIFT and Multi-Template Matching – MTM) to detect brand logos from the domains of interest.
Even though the internet has only been there for a short period, it has grown tremendously. To- day, a significant portion of commerce is conducted entirely online because of increased inter- net users and technological advancements in web construction. Additionally, cyberattacks and threats have expanded significantly, leading to financial losses, privacy breaches, identity theft, a decrease in customers’ confidence in online banking and e-commerce, and a decrease in brand reputation and trust. When an attacker pretends to be a genuine and trustworthy institution, they can steal private and confidential information from a victim. Aside from that, phishing has been an ongoing issue for a long time. Billions of dollars have been shed on the global economy. In recent years, there has been significant progress in the development of phishing detection and identification systems to protect against phishing attacks. Phishing detection technologies frequently produce binary results, i.e., whether a phishing attempt was made or not, with no explanation. On the other hand, phishing identification methodologies identify phishing web- pages by visually comparing webpages with predetermined authentic references and reporting phishing together with its target brand, resulting in findings that are understandable. However, technical difficulties in the field of visual analysis limit the applicability of currently available solutions, preventing them from being both effective (with high accuracy) and efficient (with little runtime overhead). Here, we evaluate existed framework called Phishpedia. This hybrid deep learning system can recognize identity logos from webpage screenshots and match logo variants of the same brand with high precision. Phishpedia provides high accuracy with low run- time. Lastly, unlike other methods, Phishpedia does not require training on any phishing sam- ples whatsoever. Phishpedia exceeds baseline identification techniques (EMD, PhishZoo, and LogoSENSE), inaccurately detecting phishing pages in lengthy testing using accurate phishing data. The effectiveness of Phishpedia was tested and compared against other standard machine learning algorithms and some state-of-the-art algorithms. The given solutions performed better than different algorithms in the given dataset, which is impressive.
Technology advancement has played a vital role in business development; however, it has opened a broad attack surface. Passwords are one of the essential concepts used in applications for authentication. Companies manage many corporate applications, so the employees must meet the password criteria, which leads to password fatigue. This thesis addressed this issue and how we can overcome this problem by theoretically implementing an IAM solution. In this, we disused MFA, SSO, biometrics, strong password policies and access control. We introduced the IAM framework that should be considered while implementing the IAM solution. Implementing an IAM solution adds an extra layer of security.
Encryption techniques allow storing and transferring of sensitive information securely by using encryption at rest and encryption in transit, respectively. However, when computation is performed on these sensitive data, the data needs to be decrypted first and encrypted again after performing the computations. During the computations, the sensitive data becomes vulnerable to attackers as it's in decrypted form. Homomorphic encryption, a special type of encryption technique that allows computation on encrypted data can be used to solve the above-mentioned problem. The best way to achieve maximum security with homomorphic encryption is to perform at least the homomorphic encryption and decryption on the client side (browser) of a web application by not trusting the server. At present time there are many libraries with different homomorphic schemes available for homomorphic encryption. However, there are very few to no JavaScript libraries available to perform homomorphic encryption on the client side of any web application. This thesis mainly focuses on the JavaScript implementation of client-side homomorphic encryption. The fully homomorphic encryption scheme BFV is selected for the implementation. After implementing the fully homomorphic encryption scheme based on the “py-fhe” library, tests are also carried out in order to determine the applicability (in terms of time consumption, security and correctness) of this implementation in a web application by comparing the performance and security for different test cases and different settings.
Risk-based Cybermaturity Assessment Model - Protecting the company against ransomware attacks
(2023)
Ransomware has become one of the most catastrophic attacks in the previous decade, hurting businesses of all sorts worldwide. So, no organization is safe, and most companies are reviewing their ransomware defensive solutions to avoid business and operational hazards. IT departments are using cybersecurity maturity assessment frameworks like CMMC, C2M2, CMMI, NIST, CIS, CPP, and others to analyze organization security capabilities. In addition to maturity assessment models for the process layer and human pillar, there are much research on the analysis, identification, and defense of cyber threats in product/software layers that propose state-of-the-art approaches.
This motivates a comprehensive ransomware cyber security solution. Then, a crucial question arises: “How companies can measure the security maturity of controls in a specific danger for example for Ransomware attack?” Several studies and frameworks addressed this subject.
Complexity of understanding the ransomware attack, Lack of comprehensive ransomware defense solutions and Lack of cybermaturity assessment model for ransomware defense solutions are different aspects of problem statement in this study. By considering the most important limitations to developing a ransomware defense cybermaturity assessment method, this study developed a cybermaturity assessment methodology and implemented a Toolkit to conduct cyber security self-assessment specifically for ransomware attack to provide a clearer vision for enterprises to analyze the security maturity of controls regardless of industry or size.
Durch die Fortschritte im Bereich der Quantencomputer rückt der Zeitpunkt näher, dass Quantencomputer die bestehenden mathematischen Probleme lösen können, welche in den aktuellen Public-Key-Verschlüsselungsverfahren verwendet werden. Als Reaktion darauf wurde ein Standardisierungsprozess für quantensichere Public-Key-Verschlüsselungsverfahren gestartet. Diese Arbeit analysiert diese und vergleicht sie untereinander, um Stärken und Schwächen der einzelnen Verfahren aufzuzeigen.
On a regular basis, we hear of well-known online services that have been abused or compromised as a result of data theft. Because insecure applications jeopardize users' privacy as well as the reputation of corporations and organizations, they must be effectively secured from the outset of the development process. The limited expertise and experience of involved parties, such as web developers, is frequently cited as a cause of risky programs. Consequently, they rarely have a full picture of the security-related decisions that must be made, nor do they understand how these decisions affect implementation accurately.
The selection of tools and procedures that can best assist a certain situation in order to protect an application against vulnerabilities is a critical decision. Regardless of the level of security that results from adhering to security standards, these factors inadvertently result in web applications that are insufficiently secured. JavaScript is a language that is heavily relied on as a mainstream programming language for web applications with several new JavaScript frameworks being released every year.
JavaScript is used on both the server-side in web applications development and the client-side in web browsers as well.
However, JavaScript web programming is based on a programming style in which the application developer can, and frequently must, automatically integrate various bits of code from third parties. This potent combination has resulted in a situation today where security issues are frequently exploited. These vulnerabilities can compromise an entire server if left unchecked. Even though there are numerous ad hoc security solutions for web browsers, client-side attacks are also popular. The issue is significantly worse on the server side because the security technologies available for server-side JavaScript application frameworks are nearly non-existent.
Consequently, this thesis focuses on the server-side aspect of JavaScript; the development and evaluation of robust server-side security technologies for JavaScript web applications. There is a clear need for robust security technologies and security best practices in server-side JavaScript that allow fine-grained security.
However, more than ever, there is this requirement of reducing the associated risks without hindering the web application in its functionality.
This is the problem that will be tackled in this thesis: the development of secure security practices and robust security technologies for JavaScript web applications, specifically, on the server-side, that offer adequate security guarantees without putting too many constraints on their functionality.
As information technology continues to advance at a rapid speed around the world, new difficulties emerge. The growing number of organizational vulnerabilities is among the most important issues. Finding and mitigating vulnerabilities is critical in order to protect an organization’s environment from multiple attack vectors.
The study investigates and comprehends the complete vulnerability management process from the standpoint of the security officer job role, as well as potential improvements. Few strategies are used to achieve efficient mitigation and the de- velopment of a process for tracking and mitigating vulnerabilities. As a result, a qualitative study is conducted in which the objective is to create a proposed vulner- ability and risk management process, as well as to develop a system for analyzing and tracking vulnerabilities and presenting the vulnerabilities in a graphical dash- board format. This thesis’s data was gathered through an organized literature study as well as through the use of various web resources. We explored numerous ap- proaches to analyze the data, such as categorizing the vulnerabilities every 30, 60, and 90 days to see whether the vulnerabilities were reoccurring or new. According to our findings, tracking vulnerabilities can be advantageous for a security officer.
We come to the conclusion that if an organization has a proper vulnerability tracking system and vulnerability management process, it can aid security officers in having a better understanding of and making plans for reducing vulnerabilities. In terms of system patching and vulnerability remediation, it will also assist the security officer in identifying areas of weakness in the process. As a result, the suggested ways provide an alternate approach to managing and tracking vulnerabilities in an effective manner, although there is still a small area that needs additional analysis and research to make it even better.
Komplexe E-Commerce-Systeme müssen heutzutage immer schneller am Markt sein und sich an diesen anpassen. Dies wird durch SaaS-Services möglich, wodurch sich die Best-of-Breed-Lösungen einsetzen lassen. Der monolithische Ansatz der meisten E-Commerce-Systeme ist für diese Anwendungen nicht mehr geeignet. Abhilfe soll der Composable-Commerce-Ansatz schaffen. Für den Ansatz wird eine Integrationslösung benötigt. Ziel dieser Thesis ist es, Integrationslösungen zu evaluieren und mithilfe von Integration-Layer-Prototypen gegenüberzustellen. Es werden zwei Integrationslösungen ausgewählt, die als Prototyp implementiert werden. Für den ersten Prototypen wird Apache Camel in einem Spring-Boot-Server verwendet. Der zweite Prototyp setzt die AWS-eigenen Services für die Integration ein. Zum Schluss werden diese durch einen Last-Test auf ihre Performance geprüft.
An organized strategy to ensure the security of an organization is an information security management system. During various security crises, hazards, and breaches, this strategy aids an organization in maintaining the confidentiality, integrity, and accessibility of information. Organizations are getting ready to comply with information security management system criteria. Despite this, security concerns continue to plague ineffective controls, have poor connectivity, or cause a silo effect, which is a common cause. One of the causes is a low maturity model that is not synchronized with the organization’s business processes. For a higher level of maturity, it is best to evaluate the practices.
Different maturity models on information security and cyber security capacity, management processes, security controls, implementation level, and many more have already been developed by numerous international organizations, experts, and scholars. The present models, however, do not assess a particular organization's particular practices. The evaluation of the business process is frequently neglected because measurement requirements for models are typically more concentrated on examining specific elements. For this reason, it caused the maturity assessment to not be executed explicitly and broadly.
We developed an organizational information security maturity model, a combination of work of different maturity models currently existing. While making this model, we considered that any size or type of organization could use this model. The model considers the success elements of the information security management system when assessing the implementation's effectiveness. We employed a mixed-method strategy that included both qualitative and quantitative research. With the help of a questionnaire survey, we evaluated the previous research using a qualitative methodology. In the quantitative method, we'll figure out how mature the information security management system is now. The proposed model could be used to reduce security incidents by improving implementation gaps.