In modern large-scale systems comprising multiple heterogeneous devices, the introduction of swarm attestation schemes aims to alleviate the scalability and efficiency issues of traditional single-Prover and single-Verifier attestation. In this paper, we propose PRIVÉ, a privacy-preserving, scalable, and accountable swarm attestation scheme that addresses the limitations of existing solutions. Specifically, we eliminate the assumption of a trusted Verifier, which is not always applicable in real-world scenarios, as the need for the devices to share identifiable information with the Verifier may lead to the expansion of the attack landscape. To this end, we have designed an enhanced variant of the Direct Anonymous Attestation (DAA) protocol, offering traceability and linkability whenever needed. This enables PRIVÉ to achieve anonymous, privacy-preserving attestation while also providing the capability to trace a failed attestation back to the compromised device. To the best of our knowledge, this paper presents the first Universally Composable (UC) security model for swarm attestation accompanied by mathematical UC security proofs, as well as experimental benchmarking results that highlight the efficiency and scalability of the proposed scheme.

Anonymous credentials (ACs) are secure digital versions of credentials that allow selective proof of possession of encoded attributes without revealing additional information. Attributes can include basic personal details (e.g., passport, medical records) and also claims about existing attributes (e.g., age > 18), which can be revealed without disclosing any concrete information. However, embedding all possible claims in a credential is impractical. To address this, we propose verifiers defining policies as high-level programs executed by holders on their credentials. We also propose making the proofs designated verifier to prevent the misuse or leakage of sensitive information by dishonest verifiers to any unwanted third party.

To detect runtime attacks against programs running on a remote computing platform, Control-Flow Attestation (CFA) lets a (trusted) verifier determine the legality of the program’s execution path, as recorded and reported by the remote platform (prover). However, besides complicating scalability due to verifier complexity, this assumption regarding the verifier’s trustworthiness renders existing CFA schemes prone to privacy breaches and implementation disclosure attacks under “honest-but-curious” adversaries. Thus, to suppress sensitive details from the verifier, we propose to have the prover outsource the verification of the attested execution path to an intermediate worker of which the verifier only learns the result. However, since a worker might be dishonest about the outcome of the verification, we propose a purely cryptographical solution of transforming the verification of the attested execution path into a verifiable computational task that can be reliably outsourced to a worker without relying on any trusted execution environment. Specifically, we propose to express a program-agnostic execution path verification task inside an arithmetic circuit whose correct execution can be verified by untrusted verifiers in zero knowledge.

Recent advances in Decentralized Digital Identity solutions, revolving around the use of Verifiable Credentials towards identity sovereignty, are centered around Identity Wallets for ensuring that identity data control remains with the user. However, such schemes still lack the capabilities to provide higher Level of Assurance (LoA) guarantees, for identity verification, which restricts their full potential. In this paper, we design and showcase DOOR; a library that enables Identity Wallets to leverage hardware Roots-of-Trust (RoT) for binding user authentication factors to HW-based keys, thus, allowing for both proof of (User) identity and (Wallet) integrity, bringing them in alignment with emerging regulations and standards that require higher LoA for services (e.g. eIDAS). At the same time, we make sure that privacy-enhancing properties like selective-disclosure are fully supported in order to make the Wallet compliant with privacy regulations (e.g. GDPR). To achieve all the above, we have designed an enhanced variant of Attribute-based Direct Anonymous Attestation (DAA-A) crypto protocol for offering anonymity, unlinkability, and unforgeability, while being the first to offer strong guarantees on the Wallet’s integrity when constructing attribute attestations. We formally prove the security properties of DOOR, offered by the underlying crypto primitives used to enable selective disclosure of attributes, by describing their construction while also benchmarking their computational footprint and comparing them with other widespread cryptographic mechanisms (adopted by the standards) in terms of performance, size of the associated verifiable presentations while safeguarding user anonymous authentication and unlinkability.

In the race toward next-generation systems of systems, the adoption of edge and cloud computing is escalating to deliver the underpinning end-to-end services. To safeguard the increasing attack landscape, remote attestation lets a verifier reason about the state of an untrusted remote prover. However, for most schemes, verifiability is only established under the omniscient and trusted verifier assumption, where a verifier knows the prover’s trusted states, and the prover must reveal evidence about its current state. This assumption severely challenges upscaling, inherently limits eligible verifiers, and naturally prohibits adoption in public-facing security-critical networks. To meet current zero trust paradigms, we propose a general ZEro-Knowledge pRoof of cOnformance (ZEKRO) scheme, which considers mutually distrusting participants and enables a prover to convince an untrusted verifier about its state’s correctness in zero-knowledge, i.e., without revealing anything about its state.

As lightweight embedded devices become increasingly ubiquitous and connected, they present a disturbing target for adversaries circumventing the gates of cryptography. We consider the challenge of exfiltrating and locating cryptographic keys from the run-time environment of software-based services when their software layout and data structures in memory are unknown. We detail an attack that can, without affecting the system’s operation, exfiltrate keys in use promptly by leveraging the strong causality between transceivers and keyed cryptosystems (authentication, authorization, and encryption). We then propose how to effectively and efficiently reduce the key material’s search space from a batch of stackshots (stack extractions) by leveraging the stack’s innate composition, which, to the best of our knowledge, is the first method to systematically infer and reduce the search space of semi-arbitrary keys. We instantiate and evaluate our attack against MSP430 micro-controllers.

Vehicular networks rely on Public Key Infrastructure (PKIs) to generate long-term and short-term pseudonyms that protect vehicle's privacy. Instead of relying on a complex and centralized ecosystem of PKI entities, a more scalable solution is to rely on Direct Anonymous Attestation (DAA) and the use of Trusted Computing elements. In particular, revocation based on DAA is very attractive in terms of efficiency and privacy: it does not require the use of Certificate Revocation Lists (CRLs) and revocation authorities can exclude misbehaving participants from a V2X system without resolving (i.e. learning) their long-term identity. In this paper, we present a novel revocation protocol based on the use of DAA and showcase a detailed design and modeling of the implementation on a real TPM platform in order to demonstrate its significant performance improvements compared to existing solutions.

The goal of CYSARM workshop is to foster collaboration among researchers and practitioners to discuss the various facets and trade-offs of cyber-security. In particular, how new technologies and algorithms might impact the cyber-security of existing or future models and systems.

The Internet of Things (IoT) is rapidly evolving, while introducing several new challenges regarding security, resilience and operational assurance. In the face of an increasing attack landscape, it is necessary to cater for the provision of efficient mechanisms to collectively detect sophisticated malware resulting in undesirable (run-time) device and network modifications. This is not an easy task considering the dynamic and heterogeneous nature of IoT environments; i.e., different operating systems, varied connected networks and a wide gamut of underlying protocols and devices. Malicious IoT nodes or gateways can potentially lead to the compromise of the whole IoT network infrastructure. On the other hand, the SDN control plane has the capability to be orchestrated towards providing enhanced security services to all layers of the IoT networking stack. In this paper, we propose an SDN-enabled control plane based orchestration that leverages emerging Long Short-Term Memory (LSTM) classification models; a Deep Learning (DL) based architecture to combat malicious IoT nodes. It is a first step towards a new line of security mechanisms that enables the provision of scalable AI-based intrusion detection focusing on the operational assurance of only those specific, critical infrastructure components,thus, allowing for a much more efficient security solution. The proposed mechanism has been evaluated with current state of the art datasets (i.e., N_BaIoT 2018) using standard performance evaluation metrics. Our preliminary results show an outstanding detection accuracy (i.e., 99.9%) which significantly outperforms state-of-the-art approaches. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security does not hinder the deployment of intelligent IoT-based computing systems.

Over recent years, emphasis in secure V2X communications research has converged on the use of Vehicular Public Key Infrastructures (VPKIs) for credential management and privacy-friendly authentication services. However, despite the security and privacy guarantees offered by such solutions, there are still a number of challenges to be conquered. By reflecting on state-of-the-art PKI-based architectures, in this paper, we identify their limitations focusing on scalability, interoperability, pseudonym reusage policies and revocation mechanisms. We argue that in their current form such mechanisms cannot capture the strict security, privacy, and trust requirements of all involved stakeholders. Motivated by these weaknesses, we then proceed on proposing the use of trusted computing technologies as an enabler for more decentralized approaches where trust is shifted from the back-end infrastructure to the edge. We debate on the advantages offered and underline the specifis of such a novel approach based on the use of advanced cryptographic primitives, using Direct Anonymous Attestation (DAA) as a concrete example. Our goal is to enhance run-time security, privacy and trustworthiness of edge devices with a scalable and decentralized solution eliminating the need for federated infrastructure trust. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them.

The goal of CYSARM workshop is to foster collaboration among researchers and practitioners to discuss the various facets and trade-offs of cyber-security. In particular, how new technologies and algorithms might impact the cyber-security of existing or future models and systems.

The Internet of Things (IoT) is rapidly evolving, while introducing several new challenges regarding security, resilience and operational assurance. In the face of an increasing attack landscape, it is necessary to cater for the provision of efficient mechanisms to collectively verify software- and device-integrity in order to detect run-time modifications. Towards this direction, remote attestation has been proposed as a promising defense mechanism. It allows a third party, the verifier, to ensure the integrity of a remote device, the prover. However, this family of solutions do not capture the real-time requirements of industrial IoT applications and suffer from scalability and efficiency issues. In this paper, we present a lightweight dynamic control-flow property-based attestation architecture (CFPA) that can be applied on both resource-constrained edge and cloud devices and services. It is a first step towards a new line of security mechanisms that enables the provision of control-flow attestation of only those specific, critical software components that are comparatively small, simple and limited in function, thus, allowing for a much more efficient verification. Our goal is to enhance run-time software integrity and trustworthiness with a scalable and decentralized solution eliminating the need for federated infrastructure trust. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security do not hinder the deployment of intelligent edge computing systems.

With the advancement of Internet of Things (IoT), a large number of electronic devices are connected to the Internet. These connected electronic devices acquire and transmit information, and respond to any received actions. In the medical ecosystem, hospitals can implement medical diagnosis (MD) with medical sensors, especially for remote auxiliary MD. But, in this context, patients' privacy (PP) is of paramount importance, and confidentiality of medical data is crucial. Therefore, the main challenge ahead is how to realize remote auxiliary MD while protecting confidentiality of the medical data and ensuring PP. In this article, based on somewhat homomorphic encryption (SHE) scheme addressed by Junfeng Fan and Frederik Vercauteren (FV), we provide the first instance of a new efficient SHE scheme for homomorphic evaluation over single instruction multiple data (SIMD). We also implement a new set of efficient SIMD homomorphic comparison and division schemes. Based on these findings, we implement efficient privacy preserving and SIMD homomorphic surf and multiretina-image matching schemes. Offered functionalities include SIMD homomorphic feature point detection, multiretina-image matching, and lesion detection for the encrypted retinal image of diabetic retinopathy. Finally, we provide a proof-of-concept application implementation toward remote auxiliary diagnosis systems for diabetes in order to showcase the core security and privacy pillars of our solution. In the meantime, our IoT system designed with lattice-based cryptography preserves data confidentiality under quantum computation and quantum computers.

The advancement of Internet-of-Things (IoT) edge devices with various types of sensors enables us to harness diverse information with Mobile Crowd-Sensing applications (MCS). This highly dynamic setting entails the collection of ubiquitous data traces, originating from sensors carried by people, introducing new information security challenges; one of them being the preservation of data trustworthiness. What is needed in these settings is the timely analysis of these large datasets to produce accurate insights on the correctness of user reports. Existing data mining and other artificial intelligence methods are the most popular to gain hidden insights from IoT data, albeit with many challenges. In this paper, we first model the cyber trustworthiness of MCS reports in the presence of intelligent and colluding adversaries. We then rigorously assess, using real IoT datasets, the effectiveness and accuracy of well-known data mining algorithms when employed towards IoT security and privacy. By taking into account the spatio-temporal changes of the underlying phenomena, we demonstrate how concept drifts can masquerade the existence of attackers and their impact on the accuracy of both the clustering and classification processes. Our initial set of results clearly show that these unsupervised learning algorithms are prone to adversarial infection, thus, magnifying the need for further research in the field by leveraging a mix of advanced machine learning models and mathematical optimization techniques.

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of mobile crowd sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacy-preservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after an MCS architecture that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving and intelligent MCS entities (servers). In this paper, we meet these challenges and propose a comprehensive security and privacy-preserving architecture. With a full blown implementation, on real mobile devices, and experimental evaluation we demonstrate our system's efficiency, practicality, and scalability. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system offers strong security and privacy-preservation guarantees, thus, facilitating the deployment of trustworthy MCS applications.

The openness of PS systems renders them vulnerable to malicious users that can pollute the measurement collection process, in an attempt to degrade the PS system data and, overall, its usefulness. Mitigating such adversarial behavior is hard. Cryptographic protection, authentication, authorization, and access control can help but they do not fully address the problem. Reports from faulty insiders (participants with credentials) can target the process intelligently, forcing the PS system to deviate from the actual sensed phenomenon. Filtering out those faulty reports is challenging, with practically no prior knowledge on the participants' trustworthiness, dynamically changing phenomena, and possibly large numbers of compromised devices. This paper proposes SHIELD, a novel data verification framework for PS systems that can complement any security architecture. SHIELD handles available, contradicting evidence, classifies efficiently incoming reports, and effectively separates and rejects those that are faulty. As a result, the deemed correct data can accurately represent the sensed phenomena, even when 45% of the reports are faulty, intelligently selected by coordinated adversaries and targeted optimally across the system's coverage area.

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of participatory sensing (PS). The openness of such systems and the richness of user data they entail raise significant concerns for their security, privacy and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a comprehensive solution. That is, a secure and accountable PS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after a PS system that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving PS entities (servers). We address these seemingly contradicting requirements with our SPPEAR architecture. Our full blown implementation and experimental evaluation demonstrate that SPPEAR is efficient, practical, and scalable. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system is a comprehensive solution that significantly extends the state-of-the-art and can catalyze the deployment of PS applications.

ABSTRACT As the domains of pervasive computing and sensor networking are expanding, there is an ongoing trend towards assistive living and healthcare support environments that can effectively assimilate these technologies according to human needs. Most of the existing research in assistive healthcare follows a more passive approach and has focused on collecting and processing data using a static‐topology and an application‐aware infrastructure. However, with the technological advances in sensing, computation, storage, and communications, a new era is about to emerge changing the traditional view of sensor‐based assistive environments where people are passive data consumers, with one where people carry mobile sensing elements involving large volumes of data related to everyday human activities. This evolution will be driven by people‐centric sensing and will turn mobile phones into global mobile sensing devices enabling thousands new personal, social, and public sensing applications. In this paper, we discuss our vision for people‐centric sensing in assistive healthcare environments and study the security challenges it brings. This highly dynamic and mobile setting presents new challenges for information security, data privacy and ethics, caused by the ubiquitous nature of data traces originating from sensors carried by people. We aim to instigate discussion on these critical issues because people‐centric sensing will never succeed without adequate provisions on security and privacy. To that end, we discuss the latest advances in security and privacy protection strategies that hold promise in this new exciting paradigm. We hope this work will better highlight the need for privacy in people‐centric sensing applications and spawn further research in this area. Copyright © 2011 John Wiley & Sons, Ltd.

Malicious code (or malware) is defined as a software designed to execute attacks on software systems and fulfill the harmful intents of an attacker. As lightweight embedded devices become more ubiquitous and increasingly networked, they present a new and very disturbing target for malware developers. In this paper, we demonstrate how to execute malware on wireless sensor nodes that are based on the Von Neumann architecture. We achieve this by exploiting a buffer overflow vulnerability to smash the call stack and intrude a remote node over the radio channel. By breaking the malware into multiple packets, the attacker can inject arbitrarily long malicious code to the node and completely take control of it. Then we proceed to show how the malware can be crafted to become a self-replicating worm that broadcasts itself and infects the network in a hop-by-hop manner. To our knowledge, this is the first instance of a self-propagating worm that provides a detailed analysis along with instructions in order to execute arbitrary malicious code. We also provide a complete implementation of our attack, measure its effectiveness in terms of time taken for the worm to propagate to the entire sensor network and, finally, suggest possible countermeasures.

Wireless sensor networks are vulnerable to adversaries as they are frequently deployed in open and unattended environments. Preventive mechanisms can be applied to protect them from an assortment of attacks. However, more sophisticated methods, like intrusion detection systems, are needed to achieve a more autonomic and complete defense mechanism, even against attacks that have not been anticipated in advance. In this paper, we present a lightweight intrusion detection system, called LIDeA, designed for wireless sensor networks. LIDeA is based on a distributed architecture, in which nodes overhear their neighboring nodes and collaborate with each other in order to successfully detect an intrusion. We show how such a system can be implemented in TinyOS, which components and interfaces are needed, and what is the resulting overhead imposed.

One of the reasons that the research of intrusion detection in wireless sensor networks has not advanced significantly is that the concept of "intrusion" is not clear in these networks. In this paper we investigate in depth one of the most severe attacks against sensor networks, namely the sinkhole attack, and we emphasize on strategies that an attacker can follow to successfully launch such an attack. Then we propose specific detection rules that can make legitimate nodes become aware of the threat, while the attack is still taking place. Finally, we demonstrate the attack and present some implementation details that emphasize the little effort that an attacker would need to put in order to break into a realistic sensor network.