Explore the vast range of titles available.

Socio-economic aspects of future communication networks such as pricing models for network providers, network neutrality, and Quality of Experience (QoE) are becoming more and more important as the convergence of networks is in progress. All the above areas share a common interest: the deeper understanding of user behavior. In this paper, as a first step towards a more realistic user model, we investigate customer loyalty and its impact on the pricing competition of Internet Service Providers (ISPs) who sell Internet access to end-users. The main contribution of this paper is twofold. First, we analyze the impact of user loyalty with game-theoretical means motivated by the Bertrand game. We show how loyalty introduces a new equilibrium in a repeated game setting resulting in the cooperation of ISPs. Furthermore, we investigate the case of a differentiated customer population by introducing dual reservation values, and show how it leads to new, pure strategy Nash equilibria indicating that ISPs should make the most out of their respective loyal user base. Second, we construct two novel models for customer loyalty incorporating two important aspects of the users’ purchasing decisions: price sensitivity and inherent uncertainty. We evaluate the impact of user loyalty through these models by extensive simulations in a number of relevant scenarios. In particular, we show how the higher level of loyalty in the user population leads to larger profits for ISPs. We argue that our findings can motivate network researchers to incorporate a finer-grained user behavior model in their investigations on pricing models of network services and other socio-economic issues.

This paper explores the comparative analysis of federated learning (FL) and centralized learning (CL) models in the context of multi-class traffic flow classification for network applications, a timely study in the context of increasing privacy preservation concerns. Unlike existing literature that often omits detailed class-wise performance evaluation, and consistent data handling and feature selection approaches, our study rectifies these gaps by implementing a feed-forward neural network and assessing FL performance under both independent and identically distributed (IID) and non-independent and identically distributed (non-IID) conditions, with a particular focus on incremental training. In our cross-silo experimental setup involving five clients per round, FL models exhibit notable adaptability. Under IID conditions, the accuracy of the FL model peaked at 96.65%, demonstrating its robustness. Moreover, despite the challenges presented by non-IID environments, our FL models demonstrated significant resilience, adapting incrementally over rounds to optimize performance; in most scenarios, our FL models performed comparably to the idealistic CL model regarding multiple well-established metrics. Through a comprehensive traffic flow classification use case, this work (i) contributes to a better understanding of the capabilities and limitations of FL, offering valuable insights for the real-world deployment of FL, and (ii) provides a novel, large, carefully curated traffic flow dataset for the research community.

Cyber-warfare has become the norm in current ongoing military conflicts. Over the past decade, numerous examples have shown the extent to which nation-states become vulnerable if they do not focus on building their cyber capacities. Adding to the inherent complexity of cyberwar scenarios, a state is usually a member of one or more alliances. Alliance policies and internal struggles could shape the individual actions of member states; intuitively, this also holds for the cyber domain. In this paper, we define and study a simple Cyber Alliance Game with the objective of understanding the fundamental influence of alliances on cyber conflicts between nation-states. Specifically, we focus on the decision of whether to exploit a newly found vulnerability individually or share it with the alliance. First, we characterize the impact of vulnerability-sharing rewards on the resulting equilibrium. Second, we study the implications of the internal power structure of alliances on cyberwar outcomes and infer the expected behavior of Dictator, Veto, and Dummy players. Finally, we investigate how alliances can nudge their members via rewards and punishments to adhere to their defensive or offensive cyber policy. We believe that our results contribute to the fundamental understanding of real-world cyber-conflicts by characterizing the impact of alliances.

Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mechanism could still leak sensitive information. Hence, secure aggregation is utilized in many real-world scenarios to prevent attribution to specific participants. In this paper, we focus on the quality of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied. Specifically, through a series of image recognition experiments, we infer the relative quality ordering of participants. Moreover, we apply the inferred quality information to detect misbehaviours, to stabilize training performance, and to measure the individual contributions of participants.

Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing, contact tracing, and vaccination. However, combining these measures into an efficient holistic pandemic response instrument is even more involved than anticipated. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision making and the differing national implementations of the response mechanism. In this paper, through simple games, we show the effect of individual incentives on the decisions made with respect to mask wearing, social distancing and vaccination, and how these may result in sub-optimal outcomes. We also demonstrate the responsibility of national authorities in designing these games properly regarding data transparency, the chosen policies and their influence on the preferred outcome. We promote a mechanism design approach: it is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism; moreover, there is no one-size-fits-all solution when designing an effective solution.

Misaligned incentives in secure software development have long been the focus of research in the economics of security. Product liability, a powerful legal framework in other industries, has been largely ineffective for software products until recent times. However, the rapid regulatory responses to recent global cyber attacks by both the United States and the European Union, together with the (relative) success of the General Data Protection Regulation in defining both duty and standard of care for software vendors, may enable regulators to use liability to re-align incentives for the benefit of the digital society. Specifically, the recent United States National Cybersecurity Strategy suggests shifting responsibility for cyber incidents back to software vendors. In doing so, the strategy also puts forward the concept of the liability waiver: if a software company voluntarily undergoes and passes an IT security audit, its future product liability is (fully or partially) waived. In this paper, we analyze this audit scenario from the perspective of the software vendor and the auditor, respectively. From the vendor's view, this is formulated as a sequential decision problem: a vendor with a product or process needs to pass a mandatory audit to release the product onto the market; it is allowed to go through the audit repeatedly, and thus the vendor needs to determine what level of effort to put into the product following each failed test. We show that the optimal strategy for an opt-in vendor is to never quit and to exert cumulative investments in either a ``one-and-done'' or ``incremental'' manner. From the auditor's view, we examine what type of audit might be the most effective in incentivizing voluntary participation and, at the same time, a more desirable effort from the vendor. We also showed how dynamic audits can be exploited to increase the vendor's incentivizable investment.

Heterogeneity is inherently present in multiple aspects of the wired and wireless Internet. Understanding, overcoming or even exploiting this heterogeneity at different levels are fundamental goals for researchers in computer networks. This dissertation presents results on Internet access pricing, wireless community networks and congestion control. All proposed approaches share one thing in common: they intend to help a diverse set of network providers, e.g., Internet Service Providers, community wireless operators, micro-operators (end-users themselves) and mobile operators, to address the challenges stemming from the heterogeneity of their respective networks.First, I quantify the impact of customer loyalty on the pricing competition between network providers. I then propose a pricing mechanism for Internet access, which enables network providers to plan their revenues, while users can directly influence the implemented billing policy. Second, I analyze the economic interactions in wireless community networks. I show that proper incentive design and user heterogeneity facilitates the emergence of a truly global wireless community, where both users and network providers profit from the network. Third, I show the limitations of existing TCP versions in dynamic mobile environments, particularly after a sudden capacity increase. Motivated by these limitations, I propose a simple, end-to-end non-congestion detection mechanism for TCP which solves this problem effectively.

Multiple attacks have shown that in-vehicle networks have vulnerabilities which can be exploited. Securing the Controller Area Network (CAN) for modern vehicles has become a necessary task for car manufacturers. Some attacks inject potentially large amount of fake messages into the CAN network; however, such attacks are relatively easy to detect. In more sophisticated attacks, the original messages are modified, making the detection a more complex problem. In this paper, we present a novel machine learning based intrusion detection method for CAN networks. We focus on detecting message modification attacks, which do not change the timing patterns of communications. Our proposed temporal convolutional network-based solution can learn the normal behavior of CAN signals and differentiate them from malicious ones. The method is evaluated on multiple CAN-bus message IDs from two public datasets including different types of attacks. Performance results show that our lightweight approach compares favorably to the state-of-the-art unsupervised learning approach, achieving similar or better accuracy for a wide range of scenarios with a significantly lower false positive rate.

The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location data since previous techniques all suffer from the inherent sparseness and high dimensionality of location trajectories which render most techniques impractical, resulting in unrealistic traces and unscalable methods. Moreover, time information of location visits is usually dropped, or its resolution is drastically reduced. In this paper we present a novel technique for privately releasing a composite generative model and whole high-dimensional location datasets with detailed time information. To generate high-fidelity synthetic data, we leverage several peculiarities of vehicular mobility such as its language-like characteristics (\"you should know a location by the company it keeps\") or how humans plan their trips from one point to the other. We model the generator distribution of the dataset by first constructing a variational autoencoder to generate the source and destination locations, and the corresponding timing of trajectories. Next, we compute transition probabilities between locations with a feed forward network, and build a transition graph from the output of this model, which approximates the distribution of all paths between the source and destination (at a given time). Finally, a path is sampled from this distribution with a Markov Chain Monte Carlo method. The generated synthetic dataset is highly realistic, scalable, provides good utility and, nonetheless, provably private. We evaluate our model against two state-of-the-art methods and three real-life datasets demonstrating the benefits of our approach.

Infrastructure-to-Vehicle (I2V) and Vehicle-to-Infrastructure (V2I) communication is likely to be a key-enabling technology for automated driving in the future. Using externally placed sensors, the digital infrastructure can support the vehicle in perceiving surroundings that would otherwise be difficult to perceive due to, for example, high traffic density or bad weather. Conversely, by communicating on-board vehicle measurements, the environment can more accurately be perceived in locations which are not (sufficiently) covered by digital infrastructure. The security of such communication channels is an important topic, since malicious information on these channels could potentially lead to a reduction in overall safety. Collective perception contributes to raising awareness levels and an improved traffic safety. In this work, a demonstrator is introduced, where a variety of novel techniques have been deployed to showcase an overall architecture for improving vehicle and vulnerable road user safety in a connected environment. The developed concepts have been deployed at the Automotive Campus intersection in Helmond (NL), in a field testing setting.