Burkhard Stiller

• Prof. Dr. rer.-nat. Dipl.-Inf.
• Director at University of Zurich UZH

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving participant privacy. Decentralized FL (DFL) eliminates reliance on a central server, mitigating the single point of failure inherent in the traditional FL paradigm, while introducing deployment challenges on resource-constrained devices. To evaluate re...

Traditional machine learning (ML) raises serious privacy concerns, while federated learning (FL) mitigates the risk of data leakage by keeping data on local devices. However, the training process of FL can still leak sensitive information, which adversaries may exploit to infer private data. One of the most prominent threats is the membership infer...

The QUIC protocol is now widely adopted by major tech companies and accounts for a significant fraction of today's Internet traffic. QUIC's multiplexing capabilities, encrypted headers, dynamic IP address changes, and encrypted parameter negotiations make the protocol not only more efficient, secure, and censorship-resistant, but also practically u...

The development of Decentralized Identities (DI) and Self-Sovereign Identities (SSI) has seen significant growth in recent years. This is accompanied by a numerous academic and commercial contributions to the development of principles, standards, and systems. While several comprehensive reviews have been produced, they predominantly focus on academ...

Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data aggregation, enhancing privacy and resilience. However, its sustainability remains underexplored, as energy consumption and carbon emissions vary across different system configurations. Understanding the environmental im...

Federated learning (FL) has garnered significant attention as a prominent privacy-preserving Machine Learning (ML) paradigm. Decentralized FL (DFL) eschews traditional FL's centralized server architecture, enhancing the system's robustness and scalability. However, these advantages of DFL also create new vulnerabilities for malicious participants t...

The integration of Federated Learning (FL) and Multi-Task Learning (MTL) has been explored to address client heterogeneity, with Federated Multi-Task Learning (FMTL) treating each client as a distinct task. However, most existing research focuses on data heterogeneity (e.g., addressing non-IID data) rather than task heterogeneity, where clients sol...

Federated Learning (FL) is widely recognized as a privacy-preserving machine learning paradigm due to its model-sharing mechanism that avoids direct data exchange. However, model training inevitably leaves exploitable traces that can be used to infer sensitive information. In Decentralized FL (DFL), the overlay topology significantly influences its...

Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing work is hindered by data privacy concerns associated with centralized data processing in RL, and the unsatisfactory time needed to le...

Mosaic warfare is a military strategy where reconnaissance missions with aerial vehicles are critical for gathering enemy information and achieving battlefield dominance. Nowadays, machine learning (ML) techniques play a pivotal role in this task by enabling precise detection of military vehicles. However, reconnaissance missions face challenges, p...

Emerging cloud-centric networks span from edge clouds to large-scale datacenters with shared infrastructure among multiple tenants and applications with high availability, isolation, fault tolerance, security, and energy efficiency demands. Live migration (LiMi) plays an increasingly critical role in these environments by enabling seamless applicat...

Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, negatively impacting its performance. Existing defense mechanisms have been designed for centralized...

Federated Learning (FL) performance is highly influenced by data distribution across clients, and non-Independent and Identically Distributed (non-IID) leads to a slower convergence of the global model and a decrease in model effectiveness. The existing algorithms for solving the non-IID problem are focused on the traditional centralized FL (CFL),...

Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been extensively studied in both centralized and decentralized settings. In contrast, Vertical Federated Le...

Machine Learning (ML) faces several challenges, including susceptibility to data leakage and the overhead associated with data storage. Decentralized Federated Learning (DFL) offers a robust solution to these issues by eliminating the need for centralized data collection, thereby enhancing data privacy. In DFL, distributed nodes collaboratively tra...

Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently and Identically Distributed (IID) data, which is unrealistic in real-world applications. In non-IID co...

Cybersecurity planning is challenging for digitized companies that want adequate protection without overspending money. Currently, the lack of investments and perverse economic incentives may increase the number of cyberattacks, which result in several economic impacts on companies worldwide. Therefore, cybersecurity planning has to consider techni...

Verifying the integrity of embedded device characteristics is required to ensure secure operation of a device. One central challenge is to securely extract and store device-specific configurations for future verification. Existing device attestation schemes suffer from notable limitations, including a lack of standardization and a failure to encomp...

Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central server for global aggregation, which exhibits limitations such as bottleneck and single point of fail...

Threat modeling has been successfully applied to model technical threats within information systems. However, a lack of methods focusing on non-technical assets and their representation can be observed in theory and practice. Following the voices of industry practitioners, this paper explored how to model insider threats based on business process m...

Federated learning (FL) enables participants to collaboratively train machine and deep learning models while safeguarding data privacy. However, the FL paradigm still has drawbacks that affect its trustworthiness, as malicious participants could launch adversarial attacks against the training process. Previous research has examined the robustness o...

Les maisons intelligentes utilisent des dispositifs connectés à Internet, l’intelligence artificielle, des protocoles et de multiples technologies afin de permettre à leurs résidents de surveiller à distance leurs maisons et de les gérer à l’aide d’un Smartphone. Amazon, Apple et Google, entre autres, ont lancé leurs propres dispositifs pour maison...

Moving Target Defense (MTD) is a promising approach to mitigate attacks by dynamically altering target attack surfaces. Still, selecting suitable MTD techniques for zero-day attacks is an open challenge. Reinforcement Learning (RL) could be an effective approach to optimize the MTD selection through trial and error, but the literature fails when i)...

The emergence of 6G networks will pave the way for a diverse range of services to function within virtualized multi-cloud environments in the Edge-to-Cloud Continuum. This flexible and distributed architecture presents numerous prospects for enhancing service attributes such as availability, fault tolerance, and security. One primary instrument to...

This chapter classifies communication technologies employed in the Internet of Things (IoT) as infrastructure, data, transport, discovery, messaging, and management protocol families and semantics and frameworks. Moreover, IoT networks are divided into IP and constrained networks with the latter not directly supporting the Transmission Control Prot...

Since the proposal of Bitcoin in 2009 and with the inclusion of the first transaction in its genesis block, Blockchains (BC) have been used to store arbitrary data, including texts, images, and documents. However, such data is often not easily discoverable in BCs and is embedded within their binary data structures. Thus, this paper presents the des...

Abstract: Digital marketing has transformed referral marketing, revealing limitations in traditional centralized systems such as trust, transparency, and efficiency, however, the potential advantages of decentralized systems remain underexplored. This paper investigates the feasibility of a high-volume, decentralized referral system. The approach a...

The expansion of the Internet-of-Things (IoT) paradigm is inevitable, but vulnerabilities of IoT devices to malware incidents have become an increasing concern. Recent research has shown that the integration of Reinforcement Learning with Moving Target Defense (MTD) mechanisms can enhance cybersecurity in IoT devices. Nevertheless, the numerous new...

Elections generally involve the simple tasks of counting votes and publishing the final tally to voters. Depending on the election’s scope, these processes require sophisticated methods embedded in the electorate’s various technological and societal factors (e.g., the voting culture). An election’s integrity is the pinnacle of the trust placed in t...

Digitization increases business opportunities and the risk of companies being victims of devastating cyberattacks. Therefore, managing risk exposure and cybersecurity strategies is essential for digitized companies that want to survive in competitive markets. However, understanding company-specific risks and quantifying their associated costs is no...

Ransomware has remained one of the most notorious threats in the cybersecurity field. Moving Target Defense (MTD) has been proposed as a novel paradigm for proactive defense. Although various approaches leverage MTD, few of them rely on the operating system and, specifically, the file system, thereby making them dependent on other computing devices...

Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of Artificial Intelligence (AI), sooner than later, ransomware (and malware in general) will incorporate AI techniques to intelligently and dynamically adapt its encryption...

IoT scenarios face cybersecurity concerns due to unauthorized devices that can impersonate legitimate ones by using identical software and hardware configurations. This can lead to sensitive information leaks, data poisoning, or privilege escalation. Behavioral fingerprinting and ML/DL techniques have been used in the literature to identify devices...

Cybersecurity planning is challenging for digitized companies that want adequate protection without overspending money. Currently, the lack of investments and perverse economic incentives are the root cause of cyberattacks, which results in several economic impacts on companies worldwide. Therefore, cybersecurity planning has to consider technical...

Integrated sensing and communication (ISAC) is a novel paradigm using crowdsensing spectrum sensors to help with the management of spectrum scarcity. However, well-known vulnerabilities of resource-constrained spectrum sensors and the possibility of being manipulated by users with physical access complicate their protection against spectrum sensing...

Hospital infrastructures are always in evidence in periods of crisis, such as natural disasters or pandemic events, under stress. The recent COVID-19 pandemic exposed several inefficiencies in hospital systems over a relatively long period. Among these inefficiencies are human factors, such as how to manage staff during periods of high demand, and...

With the ever-widening spread of the Internet of Things (IoT) and Edge Computing paradigms, centralized Machine and Deep Learning (ML/DL) have become challenging due to existing distributed data silos containing sensitive information. The rising concern for data privacy is promoting the development of collaborative and privacy-preserving ML/DL tech...

Cybercriminals are moving towards zero-day attacks affecting resource-constrained devices such as single-board computers (SBC). Assuming that perfect security is unrealistic, Moving Target Defense (MTD) is a promising approach to mitigate attacks by dynamically altering target attack surfaces. Still, selecting suitable MTD techniques for zero-day a...

Cybersecurity remains one of the key investments for companies that want to protect their business in a digital era. Therefore, it is essential to understand the different steps required to implement an adequate cybersecurity strategy, which can be viewed as a cybersecurity project to be developed, implemented, and operated. This article proposes S...

Given the growing increase in the number of blockchain (BC) platforms, cryptocurrencies, and tokens, non-technical individuals face a complex question when selecting a BC that meets their requirements (e.g. performance or security). In addition, current approaches that aid such a selection process present drawbacks (e.g. require specific BC knowled...

The battlefield has evolved into a mobile and dynamic scenario where soldiers and heterogeneous military equipment exchange information in real-time and wirelessly. This fact brings to reality the Internet of Battlefield Things (IoBT). Wireless communications are key enablers for the IoBT, and their management is critical due to the spectrum scarci...

Federated learning (FL) allows participants to collaboratively train machine and deep learning models while protecting data privacy. However, the FL paradigm still presents drawbacks affecting its trustworthiness since malicious participants could launch adversarial attacks against the training process. Related work has studied the robustness of ho...

Malware affecting Internet of Things (IoT) devices is rapidly growing due to the relevance of this paradigm in real-world scenarios. Specialized literature has also detected a trend towards multipurpose malware able to execute different malicious actions such as remote control, data leakage, en-cryption, or code hiding, among others. Protecting IoT...

This paper describes an approach to analyse transversal and inter-sectoral cybersecurity challenges and opportunities: dedicated risk assessment and management framework, which can be used to develop cybersecurity technology roadmaps. This multi-sector assessment framework is able to prioritise and evaluate cybersecurity risks in trans-sectoral and...

Crowdsensing platforms collect, process, transmit, and analyze spectrum data worldwide to optimize radio frequency spectrum usage. However, Internet-of-Things (IoT) spectrum sensors, performing some of the previous tasks, are exposed to software manipulation aiming to execute spectrum sensing data falsification (SSDF) attacks to compromise data int...

The field of generating movement profiles of individuals is valuable in many real-world applications (e.g., controlling disease spread or evaluating marketing engagement). Existing solutions often rely on global positioning systems (GPS) or similar systems, primarily targeted at outdoor use cases. However, the indoor tracking capabilities of curren...

Information-Centric Network (ICN) architectures, such as Named Data Networking (NDN), can improve content delivery on the Internet by deploying in-network caching techniques. Replacing the entire established Internet with a novel architecture is a non-trivial task, which is why this work develops a layered network architecture consisting of several...

Internet-of-Things (IoT), Artificial Intelligence (AI), and Blockchains (BCs) are essential techniques that are heavily researched and investigated today. This work here specifies, implements, and evaluates an IoT architecture with integrated BC and AI functionality to manage access control based on facial detection and recognition by incorporating...

Blockchains (BC) and Distributed Ledgers (DL) offer favorable properties, especially immutability and decentralization, which are suitable for voting systems’ Bulletin Boards (BB). In recent years, an influx of BC-based voting systems have been observed. Distributing trust among multiple trustees is a crucial reason to adopt BCs and DLs in voting s...

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting spectrum sensing data falsification (SSDF) attacks. However, the amount of data needed to train models and the scenario privacy concerns limit the applicability of centralized ML/DL. Federated learning (FL) addresses these drawbacks but...

The number of Cyber-Physical Systems (CPS) available in industrial environments is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a context, radio frequency spectrum sensing in industrial scenarios is one of the most interesting applications of CPS due to the scarcity of the spectrum. Despite the benefits of o...