Traffic watermarking is a powerful traffic-analysis technique and remains a practical threat to anonymous communication systems, yet its real-world feasibility is still poorly understood. Prior schemes are typically evaluated in narrow, single-scenario setups with ad hoc implementations and implicit assumptions about flow structure, coordination between embedder and detector, and how perturbations survive network noise. This makes results hard to reproduce, compare across contexts, and stress-test against modern network protocol features. This paper introduces Waterfall, a capsule-based framework for implementing and evaluating watermarking techniques on real traffic. Waterfall captures common semantic foundations of watermarking and exposes them via a unified capsule abstraction with a programmable API and declarative configuration. We implement Waterfall and validate it by reproducing and stress-testing representative schemes from the literature, and by deriving self-adaptive variants that tune their behavior using live traffic measurements. We further use Waterfall to analyze Tor's deployed Conflux protocol and show that its traffic-splitting design can unintentionally amplify watermark detectability. Finally, we demonstrate that Waterfall is also suitable for implementing and evaluating defenses against watermarking attacks.

Network topology verification in Software-Defined Networks (SDN) poses a significant challenge, as vulnerabilities can allow attackers to deceive the controller and manipulate the data plane into incorrect topologies, thereby endangering the entire network's security. Current solutions fail to guarantee both security and efficiency in the verification process, often resulting in damaging user traffic. With the aim of solving joint objectives, in this paper, we introduce PathSafe, a novel tool constructed on top of the existing controller frameworks designed for secure path verification in SDN environments. It enables the verification of all available paths between two points in the network and ensures a secure process. Our approach requires a data plane component for real-time packet monitoring at line speed and a control plane verification step. Our research demonstrates that PathSafe effectively mitigates security risks in compromised switches and host scenarios. Alongside a theoretical exploration of this challenge, we present a proof of concept implemented in P4, a common language for programmable data planes. Results obtained in Mininet underscore the practical applicability of PathSafe that, compared to alternatives, reduces overhead in the verification process while maintaining a limited execution time.

Implantable Medical Devices (IMDs) are widely deployed today and often use wireless communication. Establishing a secure communication channel to these devices is challenging in practice. To address this issue, researchers have proposed IMD key exchange protocols, particularly ones that leverage an Out-Of-Band (OOB) channel such as audio, vibration and physiological signals. While these solutions have advantages over traditional key exchange, they are often proposed in an ad-hoc manner and lack a systematic evaluation of their security, usability and deployability properties. In this paper, we provide an in-depth analysis of existing OOB-based solutions for IMDs and, based on our findings, propose a novel IMD key exchange protocol that includes a new class of OOB channel based on human bodily motions. We implement prototypes and validate our designs through a user study (N = 24). The results demonstrate the feasibility of our approach and its unique features, establishing a new direction in the context of IMD security.

The cloud computing landscape has evolved sig-nificantly in recent years, embracing various sandboxes to meet the diverse demands of modern cloud applications. These sandboxes encompass container-based technologies like Docker and gVisor, microVM-based solutions like Fire-cracker, and security-centric sandboxes relying on Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV. However, the practice of placing multiple tenants on shared physical hardware raises security and privacy concerns, most notably side-channel attacks. In this paper, we investigate the possibility of fingerprinting containers through CPU frequency reporting sensors in Intel and AMD CPUs. One key enabler of our attack is that the current CPU frequency information can be accessed by user-space attackers. We demonstrate that Docker images exhibit a unique frequency signature, enabling the distinction of different containers with up to 84.5 % accuracy even when multiple containers are running simultaneously in different cores. Additionally, we assess the effectiveness of our attack when performed against several sandboxes deployed in cloud environments, including Google's gVisor, AWS’ Firecracker, and TEE-based platforms like Gramine (utilizing Intel SGX) and AMD SEV. Our empirical results show that these attacks can also be carried out successfully against all of these sandboxes in less than 40 seconds, with an accuracy of over 70 % in all cases. Finally, we propose a noise injection-based countermeasure to mitigate the proposed attack on cloud environments.

Cryptographic primitives are of paramount importance to guarantee security properties in communication networks. The associated computational complexity of cryptography standards makes it prohibitive to execute these primitives at line rate in the network core. Existing implementations of cryptographic MAC algorithms in$\mathbf{P 4}$for programmable switches impose a severe performance penalty due to packet recirculation, which may not be tolerable at those network speeds. In this paper, we propose the first data plane design in$\mathbf{P 4}$of the Chaskey algorithm, a widely used secure and lightweight cryptographic MAC algorithm, tailored for the PISA switch architecture. Our P4Chaskey is the first solution to compute MACs using 128-bit keys without packet recirculation, guaranteeing line rate Terabit speeds. As state-of-the-art solutions require recirculations for the same key size (reducing throughput performance) or offer weaker security (smaller keys), P4CHASKEY is now, to our knowledge, the most efficient MAC design for the target switch architecture.

The computing continuum combines computational resources and services from edge to cloud, promising enhanced efficiency and resilience with respect to the traditional siloed-based approach. This study presents the REAR (Resource Advertisement and Reservation) protocol, which tackles the complexities of managing resources within this continuum. REAR establishes standardized interfaces to enable interoperability, enhances resource allocation efficiency, and maintains security measures for workload execution. The paper details the protocol’s design, key components, operational workflows, and potential uses, contributing to the optimization of resource use across the computing continuum.

Distributed SDN (Software-Defined Networking) controllers have rapidly become an integral element ofWide Area Networks (WAN), particularly within SD-WAN, providing scalability and fault-tolerance for expansive network infrastructures. However, the architecture of these controllers introduces new potential attack surfaces that have thus far received inadequate attention. In response to these concerns, we introduceAmbusher, a testing tool designed to discover vulnerabilities within protocols used in distributed SDN controllers.Ambusherachieves this by leveragingprotocol state fuzzing, which systematically finds attack scenarios based on an inferred state machine. Since learning states from a cluster is complicated,Ambusherproposes a novel methodology that extracts a single and relatively simple state machine, achieving efficient state-based fuzzing. Our evaluation ofAmbusher, conducted on a real SD-WAN deployment spanning two campus networks and one enterprise network, illustrates its ability to uncover 6 potential vulnerabilities in the widely used distributed controller platform.

During the last few years, Implantable Medical Devices (IMDs) have evolved considerably. IMD manufacturers are now starting to rely on standard wireless technologies for connectivity. Moreover, there is an evolution towards open systems where the IMD can be remotely monitored or reconfigured through personal commercial-off-the-shelf devices such as smartphones or tablets. Nevertheless, a major problem that still remains unsolved today is the secure establishment of cryptographic keys between the IMD and such personal devices. Researchers have already proposed various solutions, most notably by relying on an additional external device. Unfortunately, these proposed approaches are either insecure, difficult to realise in practice, or are unsuitable for the latest generation of IMDs. Motivated by this, we present HAT, a secure and practical solution to provide fine-grained and dynamic access control for the next generation of IMDs, while offering full control and transparency to the patient. The main idea behind HAT is to shift the access control responsibilities from the IMD to an external device under the user's control, such as a smartphone, acting as the IMD's Key Distribution Center. We show that HAT only introduces minimal energy and memory overhead and formally prove its security using Verifpal.

Software-defined wide area network (SD-WAN) has emerged as a new paradigm for steering a large-scale network flexibly by adopting distributed software-defined network (SDN) controllers. The key to building a logically centralized but physically distributed control-plane is running diverse cluster management protocols to achieve consistency through an exchange of control traffic. Meanwhile, we observe that the control traffic exposes unique time-series patterns and directional relationships due to the operational structure even though the traffic is encrypted, and this pattern can disclose confidential information such as control-plane topology and protocol dependencies, which can be exploited for severe attacks. With this insight, we propose a new SD-WAN fingerprinting system, called Heimdallr. It analyzes periodical and operational patterns of SD-WAN cluster management protocols and the context of flow directions from the collected control traffic utilizing a deep learning-based approach, so that it can classify the cluster management protocols automatically from miscellaneous control traffic datasets. Our evaluation, which is performed in a realistic SD-WAN environment consisting of geographically distant three campus networks and one enterprise network shows that Heimdallr can classify SD-WAN control traffic with ≥ 93%, identify individual protocols with ≥ 80% macro F-1 scores, and finally can infer control-plane topology with ≥ 70% similarity.

We propose and implement a Privacy-preserving Federated Learning ( PPFL ) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. Challenged by the limited memory size of current TEEs, we leverage greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of our implementation shows that PPFL can significantly improve privacy while incurring small system overheads at the client-side. In particular, PPFL can successfully defend the trained model against data reconstruction, property inference, and membership inference attacks. Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54×) and a similar amount of network traffic (1.002×) compared to the standard federated learning of a complete model. This is achieved while only introducing up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in PPFL's client-side.

This paper analyzes the vulnerability space arising in Trusted Execution Environments (TEEs) when interfacing a trusted enclave application with untrusted, potentially malicious code. Considerable research and industry effort has gone into developing TEE runtime libraries with the purpose of transparently shielding enclave application code from an adversarial environment. However, our analysis reveals that shielding requirements are generally not well-understood in real-world TEE runtime implementations. We expose several sanitization vulnerabilities at the level of the Application Binary Interface (ABI) and the Application Programming Interface (API) that can lead to exploitable memory safety and side-channel vulnerabilities in the compiled enclave. Mitigation of these vulnerabilities is not as simple as ensuring that pointers are outside enclave memory. In fact, we demonstrate that state-of-the-art mitigation techniques such as Intel's edger8r, Microsoft's "deep copy marshalling", or even memory-safe languages like Rust fail to fully eliminate this attack surface. Our analysis reveals 35 enclave interface sanitization vulnerabilities in 8 major open-source shielding frameworks for Intel SGX, RISC-V, and Sancus TEEs. We practically exploit these vulnerabilities in several attack scenarios to leak secret keys from the enclave or enable remote code reuse. We have responsibly disclosed our findings, leading to 5 designated CVE records and numerous security patches in the vulnerable open-source projects, including the Intel SGX-SDK, Microsoft Open Enclave, Google Asylo, and the Rust compiler.

Software-Defined Networking (SDN) is a novel network approach that has revolutionised existent network architectures by decoupling the control plane from the data plane. Researchers have shown that SDN networks are highly vulnerable to security attacks. For instance, adversaries can tamper with the controller's network topology view to hijack the hosts' location or create fake inter-switch links. These attacks can be launched for various purposes, ranging from impersonating hosts to bypassing middleboxes or intercepting network traffic. Several countermeasures have been proposed to mitigate topology attacks but to date there has been no comprehensive analysis of the level of security they offer. A critical analysis is thus an important step towards better understanding the possible limitations of the existing solutions and building stronger defences against topology attacks.

With the increasing capabilities of wearable sensors and implantable medical devices, new opportunities arise to diagnose, control and treat several chronic conditions. Unfortunately, these advancements also open new attack vectors, making security an essential requirement for the further adoption of these devices. Researchers have already developed security solutions tailored to their unique requirements and constraints. However, a fundamental yet unsolved problem is how to securely and efficiently establish and manage cryptographic keys. One of the most promising approaches is the use of patient's physiological signals for key establishment.

Implantable medical devices (IMDs) typically rely on proprietary protocols to wirelessly communicate with external device programmers. In this paper, we fully reverse engineer the proprietary protocol between a device programmer and a widely used commercial neurostimulator from one of the leading IMD manufacturers. For the reverse engineering, we follow a black-box approach and use inexpensive hardware equipment. We document the message format and the protocol state-machine, and show that the transmissions sent over the air are neither encrypted nor authenticated. Furthermore, we conduct several software radio-based attacks that could compromise the safety and privacy of patients, and investigate the feasibility of performing these attacks in real scenarios.

Physical-layer fingerprinting investigates how features extracted from radio signals can be used to uniquely identify devices. This paper proposes and analyses a novel methodology to fingerprint LoRa devices, which is inspired by recent advances in supervised machine learning and zero-shot image classification. Contrary to previous works, our methodology does not rely on localized and low-dimensional features, such as those extracted from the signal transient or preamble, but uses the entire signal. We have performed our experiments using 22 LoRa devices with 3 different chipsets. Our results show that identical chipsets can be distinguished with 59% to 99% accuracy per symbol, whereas chipsets from different vendors can be fingerprinted with 99% to 100% accuracy per symbol. The fingerprinting can be performed using only inexpensive commercial off-the-shelf software defined radios, and a low sample rate of 1 Msps. Finally, we release all datasets and code pertaining to these experiments to the public domain.

This paper analyses the security and privacy properties of a widely used insulin pump and its peripherals. We eavesdrop the wireless channel using Commercial Off-The-Shelf (COTS) software-based radios to intercept the messages sent between these devices; fully reverse-engineer the wireless communication protocol using a black-box approach; and document the message format and the protocol state-machine in use. The upshot is that no standard cryptographic mechanisms are applied and hence the system is shown to be completely vulnerable to replay and message injection attacks. Furthermore, sensitive patient health-related information is sent unencrypted over the wireless channel.