Network measurments are mostly used to studynetwork topology, performance and security. The thesis uses network measurements in relation to large Internet corporations like Salesforce, Netflix and Snapchat. The thesis attempts to verify whether it is possible to predict company performance by monitoring network measurements. For the three corporations considered, the measurements provide insider information on the company, before it is made available to the public or released by the corporation itself. Network measurements can be used by managers effectively as a tool to understand and predict company perofrmance.

In recent years, many countries and administrative domains exploit control over their communication infrastructures to censor online materials. The concrete reasons behind the Internet censorship remain poorly understood due to the opaque nature of the systems. Generally, Internet censorship is to disrupt the free flow of information. It involves a series of steps to stop the dissemination of information, or prevent the access to information, for example, disrupt the link between the users and providers. These technologies bring significant inconvenience for legitimate users. The goal of the thesis is to undertake a recent study to measure the behaviour of the Great Firewall of China (GFW). Based on that, this work designs a Peer-to-Peer (P2P) circumvention system called MultiProxy which exploits the blockchain-based economical model in order to create a balanced environment for resources providing and consuming. The system also uses multi-hop messaging to protect the privacy of the request initiators. The evaluation results show that MultiProxy can evade censorship while protecting users privacy.

The Internet is a relatively new technology that the world has become immensely dependent on. It is a tool that makes it possible to simplify our lives and better our society. But as with many things, there are people who with to exploit this tool we have for their own malicious gain. One of the mechanisms that we can use for protection against these malicious actors is the intrusion detection system. Machine learning-based intrusion detection systems (IDS) have been heavily researched for a number of years now. Much of this research, though, appears to be conducted using improper methodologies and incorrect evaluation.
Such methodologies include training and testing IDSs with unrealistic data and using uninformative metrics to determine performance. In this research, we perform a case study using one such IDS. This IDS is trained and evaluated using real network traffic collected from a real-world network. Additionally, we test its performance on actual attack traffic. This research demonstrates that an IDS that is trained with unrealistic data performs nowhere near as well as is claimed by the author when trained using real network traffic. Finally, we propose CAML-IDS, a framework for the correct assessment of machine learning-based intrusion detection systems. This framework can assist future IDS research by preventing incorrect evaluation, in turn preventing the formulation of incorrect research.

Research ProblemIn today’s competitive and fast-paced nature of conducting business in the semiconductor industry, the discipline of revenue management (RM) is often mentioned. Through a dynamic pricing capability, RM enables firms to maximize profits by capitalizing on missed revenues. Moreover, RM enables customers to receive products on short notice, by allowing them to place orders with a(n) (earlier) delivery wish date, wish price and volume. Since the semiconductor industry is characterized by volatile market demand, long production times and high capital investment (Ehm & Ponsignon, 2012), the semiconductor industry is a model representation of the manufacturing industry. Despite the benefits of RM, the manufacturing industry has not yet witnessed a widely-adopted revenue management system.In order to apply revenue management in the manufacturing industry, it is primarily important to understand that the data involved from the parties participating in the revenue management process is confidential. For manufacturers, the confidential data includes the daily unallocated available-to-promise data, which is available to commit to customers for orders on short notice. On the other hand, for customers, the maximum price that they are willing to pay for such products with a shorter delivery time is confidential. Hence, due to the confidentiality involved, as well as the need for enabling a more equal collaboration model between buyers and sellers, a secure technology that involves inputs from multiple parties is required in order to facilitate the implementation of revenue management in the manufacturing industry.Multi-party computation is a promising cryptography technology which has been theoretically studied by researchers but has not yet been practically applied. This technology is a value-added tool in the use case of revenue management because it deals with multiple parties’ confidential data and performs a computation with them securely. The outcome of the algorithm presents a revised price (based on the desired price adder range provided by the manufacturer, as well as the price wishes of the customer), a volume and a delivery date. This will allow manufacturers to secure their unallocated available-to-promise data, and customers to share the confidential maximum price they are willing to pay to receive products earlier. Multi-party computation has been proposed to be applied to revenue management by the EU H2020 SafeDEED project. We further build on this concept (SafeDEED, 2018).As a consequence of multi-party computation being an up-and-coming technology, as well as revenue management being new to the business-to-business field, it is challenging to imagine what a possible implementation of their integration is...

In this study, a analysis was performed to analyze why there are differences in port configuration between IPv4 and IPv6. For this, the current state of adoption and implantation processes of IPv6 were researched. Currently, 4,8% of all dual-stack hosts have a different configuration in open ports on IPv4 and IPv6. To discover the reasoning behind these differences, thirteen interviews were performed with owners of presumed misconfiguration or network security experts. Discovered was that most of the configurations were unintentional and because of unawareness and adequate knowledge of IPv6. Furthermore, an analyzes was performed about the reasons behind the lack of knowledge and awareness and discovered that at the moment, there is no compelling reason to migrate from IPv4 to IPv6 in most western countries. The lack of knowledge and awareness could be a reason why there is a 4,8% difference in IPv4, IPv6 configuration. Furthermore, noticed was that there is no clear solution to the problem and that there is no central actor that can solve this problem.

The frequency of online banking fraud incidents has increased over the last years. A method used by different cybercriminals is the injection of malicious code into the targeted web pages. For example, attackers might inject an additional piece code into the webpage of a targeted bank asking users to enter extra personal information (e.g., the PIN of the card). By comparing attack instances of web injected code attacks from different malware families an answer will given on how cyber criminals evolve the code of financial malware that is been used in injected code attacks against financial institutions. The contribution of this thesis is to verify the current literature of the existence of code re-use among different code instances using different code similarity tools and to explore how and why the code is evolved.