Frank Den Hartog

Wireless networks operating in unlicensed spectrum usually have to deal with signal interference, causing performance degradation. Most studies so far have tried to minimize interference to maximize users’ satisfaction. However, interference also has a positive effect: protection against eavesdropping attacks. In this study, we include Physical Layer Security (PLS) in the game-theoretical modeling of wireless networks operating in the unlicensed spectrum in a multi-operator environment without a centralized interference management authority. We do so by defining secrecy capacity as the utility of a non-cooperative game with multiple legitimate users and eavesdroppers, and using the mixed Nash equilibrium process to find optimal transmission powers. The key difference between the use of secrecy capacity as utility, as opposed to the commonly used Shannon capacity, is that secrecy capacity includes the effect that potential eavesdroppers have on the benign signal due to the necessary inclusion of secrecy-enhancing measures. We then show for a realistic scenario in a dense deployment how the utility changes if users demand performance under the condition of secrecy instead of merely maximum performance, and how that affects their transmission power selection strategy. Our experimental results reveal how a combined power selection strategy can be effective in preventing eavesdropping attacks.

This study undertakes a comprehensive analysis of the fiscal consequences of extreme cybersecurity incidents affecting publicly traded corporations in the 21st century. As cybersecurity breaches have become increasingly prevalent and financially burdensome, organisations are compelled to continuously reassess strategies and reallocate resources to safeguard their assets, maintain operational continuity, protect data and preserve client trust. Despite the pervasive nature of these threats, there remains a notable lack of academic research on the medium- to long-term economic effects of major cybersecurity incidents on the market capitalisation of affected firms. This article seeks to address this gap, offering a detailed analysis to help corporations forecast both the immediate and longer-term financial impacts of extreme yet plausible cyber events. Through an original and comprehensive analysis, this study elucidates the significant effect of extreme cybersecurity events on the market value of publicly traded corporations between 2000 and 2021. The research incorporates a series of event studies and statistical analysis to specifically scrutinise the medium- and long-term financial consequences of cyberattacks on these corporations. Our findings reveal that the immediate costs related to remediation and compensation in the wake of cyberattacks constitute merely a segment of the overall financial loss, which is better reflected in the diminution of market valuation. Furthermore, the results underscore an escalating intolerance in the market towards companies experiencing significant cybersecurity breaches, evidenced by progressively severe losses in market valuation. This trend not only amplifies the financial vulnerability of the affected companies to market forces and competitors but also signals a shifting paradigm in market perception and response to cybersecurity failures though, managed well, market positioning has been enhanced for the select few. JEL Classification: D81

Over the last decade, Software-Defined Networking (SDN) has garnered increasing research interest for networking and security. This interest stems from the programmability and dynamicity offered by SDN, as well as the growing importance of SDN as a foundational technology of future telecommunications networks and the greater Internet. However, research into SDN security has focused disproportionately on the security of the control plane, resulting in the relative trivialization of data plane security methods and a corresponding lack of appreciation of the data plane in SDN security discourse. To remedy this, this paper provides a comprehensive review of SDN data plane security research, classified into three primary research domains and several sub-domains. The three primary research domains are as follows: security capabilities within the data plane, security of the SDN infrastructure, and dynamic routing within the data plane. Our work resulted in the identification of specific strengths and weaknesses in existing research, as well as promising future directions, based on novelty and overlap with emerging technology domains. The most striking future directions are the use of hybrid SDN architectures leveraging a programmable data plane, SDN for heterogeneous network security, and the development of trust-based methods for SDN management and security, including trust-based routing.

The Internet of Things (IoT) is rapidly reshaping smart homes with innovative technologies for everyday convenience. Although smart home technologies are becoming more popular, many homeowners lack the expertise and skills to operate them. Particularly mismanaged networking technologies often result in homeowners complaining to their Broadband Service Providers (BSPs) when there are disruptions to the performance of the smart home devices and services. Current remote management solutions used by BSPs offer limited functionality and accuracy in identifying the causes of these disruptions. Causes may include legitimate factors, such as misconfiguration, interference, and congestion, but they could also be the result of cyber attacks. This review exposes knowledge gaps that impede the development of effective troubleshooting tools (particularly related to cyber attacks) for BSPs, including network-related challenges and dataset limitations. We also discuss future research directions, emphasising scalable and data-efficient methods to reduce storage requirements, costs, and privacy concerns, which will enable BSPs to manage millions of customer networks more effectively.

The Internet of Things (IoT) has been a major part of many use cases for 5G networks. From several of these use cases, it follows that 5G should be able to support at least one million devices per km². In this paper, we explain that the 5G radio access schemes as used today cannot support such densities. This issue will have to be solved by 6G. However, this requires a fundamentally different approach to accessing the wireless medium compared to current generation networks: they are not designed to support many thousands of devices in each other’s vicinity, attempting to send/receive data simultaneously. In this paper, we present a 6G system architecture for trading wireless network resources in massive IoT scenarios, inspired by the concept of the sharing economy, and using the novel concept of spectrum programming. We simulated a truly massive IoT network and evaluated the scalability of the system when managed using our proposed 6G platform, compared to standard 5G deployments. The experiments showed how the proposed scheme can improve network resource allocation by up to 80%. This is accompanied by similarly significant improvements in interference and device energy consumption. Finally, we performed evaluations that demonstrate that the proposed platform can benefit all the stakeholders that decide to join the scheme.