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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.
In the work at hand, we state that privacy and malleability of data are two aspects highly desired but not easy to associate. On the one hand, we are trying to shape data to make them usable and editable in an intelligible way, namely without losing their initial information. On the other hand, we are looking for effective privacy on data such that no external or non-authorized party could learn about their content. In such a way, we get overlapping requirements by pursuing different goals; it is trivial to be malleable without being secure, and vice versa. We propose four “real-world” use cases identified as scenarios where these two contradictory features are required and taking place in distinct environments. These considered backgrounds consist of firstly, cloud security auditing, then privacy of mobile network users and industry 4.0 and finally, privacy of COVID-19 tracing app users. After presenting useful background material, we propose to employ multiple approaches to design solutions to solve the use cases. We combine homomorphic encryption with searchable encryption and private information retrieval protocol to build an effective construction for the could auditing use case. As a second step, we develop an algorithm to generate the appropriate parameters to use the somewhat homomorphic encryption scheme by considering correctness, performance and security of the respective application. Finally, we propose an alternative use of Bloom filter data structure by adding an HMAC function to allow an outsourced third party to perform set relations in a private manner. By analyzing the overlapping bits occurring on Bloom filters while testing the inclusiveness or disjointness of the sets, we show how these functions maintain privacy and allow operations directly computed on the data structure. Then, we show how these constructions could be applied to the four selected use cases. Our obtained solutions have been implemented and we provide promising results that validate their efficiency and thus relevancy.
This thesis deals with the implementation of character controls and combat system of the Action Adventure 'Scout 3D'. The game development was realized with the game engine Unity 3D. In the first part, the architecture of a typical game engine is explained. The single components are describes step by step. Then, five well-known game engines are compared and evaluated. In the next chapter, a short overview about design and architecture patterns is worked out. The features of Unity, that are used for the implementation, and Unity's animation system 'Mecanim, are described finally. The second part includes the requirement definitions for the game 'Scout COD' which define player input, different conditions that allow or disallow several activities and the behaviour of enemies. With the help of patterns the architecture of the game is designed. Then, the implementation is explained by means of code snippets.
In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is
intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case.
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.
Garbage in, Garbage out: How does ambiguity in data affect state-of-the-art pedestrian detection?
(2024)
This thesis investigates the critical role of data quality in computer vision, particularly in the realm of pedestrian detection. The proliferation of deep learning methods has emphasised the importance of large datasets for model training, while the quality of these datasets is equally crucial. Ambiguity in annotations, arising from factors like mislabelling, inaccurate bounding box geometry and annotator disagreements, poses significant challenges to the reliability and robustness of the pedestrian detection models and their evaluation. This work aims to explore the effects of ambiguous data on model performance with a focus on identifying and separating ambiguous instances, employing an ambiguity measure utilizing annotator estimations of object visibility and identity. Through accurate experimentation and analysis, trade-offs between data cleanliness and representativeness, noise removal and retention of valuable data emerged, elucidating their impact on performance metrics like the log average miss-rate, recall and precision. Furthermore, a strong correlation between ambiguity and occlusion was discovered with higher ambiguity corresponding to greater occlusion prevalence. The EuroCity Persons dataset served as the primary dataset, revealing a significant proportion of ambiguous instances with approximately 8.6% ambiguity in the training dataset and 7.3% in the validation set. Results demonstrated that removing ambiguous data improves the log average miss-rate, particularly by reducing the false positive detections. Augmentation of the training data with samples from neighbouring classes enhanced the recall but diminished precision. Error correction of wrong false positives and false negatives significantly impacts model evaluation results, as evidenced by shifts in the ECP leaderboard rankings. By systematically addressing ambiguity, this thesis lays the foundation for enhancing the reliability of computer vision systems in real-world applications, motivating the prioritisation of developing robust strategies to identify, quantify and address ambiguity.
This work addresses the conceptualization, design, and implementation of an Application Programming Interface (API) for the Common Security Advisory Framework (CSAF) 2.0, introducing another method for distributing CSAF documents in addition to two already existing methods. These don't allow the use of flexible queries as well as filtering, which makes it difficult for operators of software and hardware to use CSAF. An API is intended to simplify this process and thus advance the automation goal of CSAF.
First, it is evaluated whether the current standard allows the implementation of an API. Any conflicts are highlighted and suggestions for standard adaptations are made. Based on these results, the API is designed to meet the previously defined requirements. Subsequently, a proof of concept is successfully developed according to the design and extensively tested with specially prepared test data. Finally, the results and the necessary standard adjustments are summarized and justified.
The conceptual design and the implementation were successfully completed. However, during the implementation of the proof of concept, some routes could not be fully implemented.
In the field of network security, the detection of possible intrusions is an important task to prevent and analyse attacks. Machine learning has been adopted as a particular supporting technique over the last years. However, the majority of related published work uses post mortem log files and fails to address the required real-time capabilities of network data feature extraction and machine learning based analysis [1-5]. We introduce the network feature extractor library FEX, which is designed to allow real-time feature extraction of network data. This library incorporates 83 statistical features based on reassembled data flows. The introduced Cython implementation allows processing individual packets within 4.58 microseconds. Based on the features extracted by FEX, existing intrusion detection machine learning models were examined with respect to their real-time capabilities. An identified Decision-Tree Classifier model was thus further optimised by transpiling it into C Code. This reduced the prediction time of a single sample to 3.96 microseconds on average. Based on the feature extractor and the improved machine learning model an IDS system was implemented which supports a data throughput between 63.7 Mbit/s and 2.5 Gbit/s making it a suitable candidate for a real-time, machine-learning based IDS.
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.
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.