arXiv subjects

Alysson Bessani

Publications and source records attributed to Alysson Bessani.

At least 19 recordsLinked to original sources

arXiv2025

MVP-ORAM: a Wait-free Concurrent ORAM for Confidential BFT Storage

It is well known that encryption alone is not enough to protect data privacy. Access patterns, revealed when operations are performed, can also be leveraged in inference attacks. Oblivious RAM (ORAM) hides access patterns by making client requests oblivious. However, existing protocols are still limited in supporting concurrent clients and Byzantine fault tolerance (BFT). We present MVP-ORAM, the first wait-free ORAM protocol that supports concurrent fail-prone clients. In contrast to previous works, MVP-ORAM avoids using trusted proxies, which require additional security assumptions, and concurrency control mechanisms based on inter-client communication or distributed locks, which limit overall throughput and the capability of tolerating faulty clients. Instead, MVP-ORAM enables clients to perform concurrent requests and merge conflicting updates as they happen, satisfying wait-freedom, i.e., clients make progress independently of the performance or failures of other clients. Since wait and collision freedom are fundamentally contradictory goals that cannot be achieved simultaneously in an asynchronous concurrent ORAM service, we define a weaker notion of obliviousness that depends on the application workload and number of concurrent clients, and prove MVP-ORAM is secure in practical scenarios where clients perform skewed block accesses. By being wait-free, MVP-ORAM can be seamlessly integrated into existing confidential BFT data stores, creating the first BFT ORAM construction. We implement MVP-ORAM on top of a confidential BFT data store and show our prototype can process hundreds of 4KB accesses per second in modern clouds.

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arXiv2025

QScale: Probabilistic Chained Consensus for Moderate-Scale Systems

Existing distributed ledger protocols either incur a high communication complexity and are thus suited to systems with a small number of processes (e.g., PBFT), or rely on committee-sampling-based approaches that only work for a very large number of processes (e.g., Algorand). Neither of these lines of work is well-suited for moderate-scale distributed ledgers ranging from a few hundred to a thousand processes, which are common in production (e.g, Redbelly, Sui). The goal of this work is to design a distributed ledger with sub-linear communication complexity per process, sub-quadratic total communication complexity, and low latency for finalizing a block into the ledger, such that it can be used for moderate-scale systems. We propose QScale, a protocol in which every process incurs only $\widetilde{O}(\kappa \sqrt{n})$ communication complexity per-block in expectation, $\widetilde{O}(n\kappa)$ total communication complexity per-block in expectation, and a best-case latency of $O(\kappa)$ rounds while ensuring safety and liveness with overwhelming probability, with $\kappa$ being a small security parameter.

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arXiv2025

False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems

Cyber Threat Intelligence (CTI) has emerged as a vital complementary approach that operates in the early phases of the cyber threat lifecycle. CTI involves collecting, processing, and analyzing threat data to provide a more accurate and rapid understanding of cyber threats. Due to the large volume of data, automation through Machine Learning (ML) and Natural Language Processing (NLP) models is essential for effective CTI extraction. These automated systems leverage Open Source Intelligence (OSINT) from sources like social networks, forums, and blogs to identify Indicators of Compromise (IoCs). Although prior research has focused on adversarial attacks on specific ML models, this study expands the scope by investigating vulnerabilities within various components of the entire CTI pipeline and their susceptibility to adversarial attacks. These vulnerabilities arise because they ingest textual inputs from various open sources, including real and potentially fake content. We analyse three types of attacks against CTI pipelines, including evasion, flooding, and poisoning, and assess their impact on the system's information selection capabilities. Specifically, on fake text generation, the work demonstrates how adversarial text generation techniques can create fake cybersecurity and cybersecurity-like text that misleads classifiers, degrades performance, and disrupts system functionality. The focus is primarily on the evasion attack, as it precedes and enables flooding and poisoning attacks within the CTI pipeline.

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arXiv2024

Chasing the Speed of Light: Low-Latency Planetary-Scale Adaptive Byzantine Consensus

Blockchain technology sparked renewed interest in planetary-scale Byzantine fault-tolerant (BFT) state machine replication (SMR). While recent works predominantly focused on improving the scalability and throughput of these protocols, few of them addressed latency. We present Mercury, a novel transformation to autonomously optimize the latency of quorum-based BFT consensus. Mercury employs a dual resilience threshold that enables faster transaction ordering when the system contains few faulty replicas. Mercury allows forming compact quorums that substantially accelerate consensus using a smaller resilience threshold. Nevertheless, Mercury upholds standard SMR safety and liveness guarantees with optimal resilience, thanks to its judicious use of a dual operation mode and BFT forensics techniques. Our experiments spread tens of replicas across continents and reveal that Mercury can order transactions with finality in less than 0.4 seconds, half the time of a PBFT-like protocol (optimal in terms of number of communication steps and resilience) in the same network. Furthermore, Mercury matches the latency of running its base protocol on theoretically optimal internet links (transmitting at 67% of the speed of light).

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arXiv2024

Knowledge Connectivity Requirements for Solving BFT Consensus with Unknown Participants and Fault Threshold (Extended Version)

Consensus stands as a fundamental building block for constructing reliable and fault-tolerant distributed services. The increasing demand for high-performance and scalable blockchain protocols has brought attention to solving consensus in scenarios where each participant joins the system knowing only a subset of participants. In such scenarios, the participants' initial knowledge about the existence of other participants can collectively be represented by a directed graph known as knowledge connectivity graph. The Byzantine Fault Tolerant Consensus with Unknown Participants (BFT-CUP) problem aims to solve consensus in those scenarios by identifying the necessary and sufficient conditions that the knowledge connectivity graphs must satisfy when a fault threshold is provided to all participants. This work extends BFT-CUP by eliminating the requirement to provide the fault threshold to the participants. We indeed address the problem of solving BFT consensus in settings where each participant initially knows a subset of participants, and although a fault threshold exists, no participant is provided with this information -- referred to as BFT Consensus with Unknown Participants and Fault Threshold (BFT-CUPFT). With this aim, we first demonstrate that the conditions for knowledge connectivity graphs identified by BFT-CUP are insufficient to solve BFT-CUPFT. Accordingly, we introduce a new type of knowledge connectivity graphs by determining the necessary and sufficient conditions they must satisfy to solve BFT-CUPFT. Furthermore, we design a protocol for solving BFT-CUPFT.

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arXiv2024

Probabilistic Byzantine Fault Tolerance (Extended Version)

Consensus is a fundamental building block for constructing reliable and fault-tolerant distributed services. Many Byzantine fault-tolerant consensus protocols designed for partially synchronous systems adopt a pessimistic approach when dealing with adversaries, ensuring safety in a deterministic way even under the worst-case scenarios that adversaries can create. Following this approach typically results in either an increase in the message complexity (e.g., PBFT) or an increase in the number of communication steps (e.g., HotStuff). In practice, however, adversaries are not as powerful as the ones assumed by these protocols. Furthermore, it might suffice to ensure safety and liveness properties with high probability. In order to accommodate more realistic and optimistic adversaries and improve the scalability of the BFT consensus, we propose ProBFT (Probabilistic Byzantine Fault Tolerance). ProBFT is a leader-based probabilistic consensus protocol with a message complexity of $O(n\sqrt{n})$ and an optimal number of communication steps that tolerates Byzantine faults in permissioned partially synchronous systems. It is built on top of well-known primitives, such as probabilistic Byzantine quorums and verifiable random functions. ProBFT guarantees safety and liveness with high probabilities even with faulty leaders, as long as a supermajority of replicas is correct, and using only a fraction of messages employed in PBFT (e.g., $20\%$). We provide a detailed description of ProBFT's protocol and its analysis.

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arXiv2024

Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness

Knowledge sharing about emerging threats is crucial in the rapidly advancing field of cybersecurity and forms the foundation of Cyber Threat Intelligence (CTI). In this context, Large Language Models are becoming increasingly significant in the field of cybersecurity, presenting a wide range of opportunities. This study surveys the performance of ChatGPT, GPT4all, Dolly, Stanford Alpaca, Alpaca-LoRA, Falcon, and Vicuna chatbots in binary classification and Named Entity Recognition (NER) tasks performed using Open Source INTelligence (OSINT). We utilize well-established data collected in previous research from Twitter to assess the competitiveness of these chatbots when compared to specialized models trained for those tasks. In binary classification experiments, Chatbot GPT-4 as a commercial model achieved an acceptable F1 score of 0.94, and the open-source GPT4all model achieved an F1 score of 0.90. However, concerning cybersecurity entity recognition, all evaluated chatbots have limitations and are less effective. This study demonstrates the capability of chatbots for OSINT binary classification and shows that they require further improvement in NER to effectively replace specially trained models. Our results shed light on the limitations of the LLM chatbots when compared to specialized models, and can help researchers improve chatbots technology with the objective to reduce the required effort to integrate machine learning in OSINT-based CTI tools.

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arXiv2023

Vivisecting the Dissection: On the Role of Trusted Components in BFT Protocols

A recent paper by Gupta et al. (EuroSys'23) challenged the usefulness of trusted component (TC) based Byzantine fault-tolerant (BFT) protocols to lower the replica group size from $3f+1$ to $2f+1$, identifying three limitations of such protocols and proposing that TCs should be used instead to improve the performance of BFT protocols. Here, we point out flaws in both arguments and advocate that the most worthwhile use of TCs in BFT protocols is indeed to make them as resilient as crash fault-tolerant (CFT) protocols, which can tolerate up to $f$ faulty replicas using $2f+1$ replicas.

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arXiv2023

On the Minimal Knowledge Required for Solving Stellar Consensus

Byzantine Consensus is fundamental for building consistent and fault-tolerant distributed systems. In traditional quorum-based consensus protocols, quorums are defined using globally known assumptions shared among all participants. Motivated by decentralized applications on open networks, the Stellar blockchain relaxes these global assumptions by allowing each participant to define its quorums using local information. A similar model called Consensus with Unknown Participants (CUP) studies the minimal knowledge required to solve consensus in ad-hoc networks where each participant knows only a subset of other participants of the system. We prove that Stellar cannot solve consensus using the initial knowledge provided to participants in the CUP model, even though CUP can. We propose an oracle called sink detector that augments this knowledge, enabling Stellar participants to solve consensus.

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arXiv2023

How Hard is Asynchronous Weight Reassignment? (Extended Version)

The performance of distributed storage systems deployed on wide-area networks can be improved using weighted (majority) quorum systems instead of their regular variants due to the heterogeneous performance of the nodes. A significant limitation of weighted majority quorum systems lies in their dependence on static weights, which are inappropriate for systems subject to the dynamic nature of networked environments. To overcome this limitation, such quorum systems require mechanisms for reassigning weights over time according to the performance variations. We study the problem of node weight reassignment in asynchronous systems with a static set of servers and static fault threshold. We prove that solving such a problem is as hard as solving consensus, i.e., it cannot be implemented in asynchronous failure-prone distributed systems. This result is somewhat counter-intuitive, given the recent results showing that two related problems -- replica set reconfiguration and asset transfer -- can be solved in asynchronous systems. Inspired by these problems, we present two versions of the problem that contain restrictions on the weights of servers and the way they are reassigned. We propose a protocol to implement one of the restricted problems in asynchronous systems. As a case study, we construct a dynamic-weighted atomic storage based on such a protocol. We also discuss the relationship between weight reassignment and asset transfer problems and compare our dynamic-weighted atomic storage with reconfigurable atomic storage.

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arXiv2023

VEDLIoT -- Next generation accelerated AIoT systems and applications

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

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arXiv2022

VEDLIoT: Very Efficient Deep Learning in IoT

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.

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arXiv2021

Making Reads in BFT State Machine Replication Fast, Linearizable, and Live

Practical Byzantine Fault Tolerance (PBFT) is a seminal state machine replication protocol that achieves a performance comparable to non-replicated systems in realistic environments. A reason for such high performance is the set of optimizations introduced in the protocol. One of these optimizations is read-only requests, a particular type of client request which avoids running the three-step agreement protocol and allows replicas to respond directly, thus reducing the latency of reads from five to two communication steps. Given PBFT's broad influence, its design and optimizations influenced many BFT protocols and systems that followed, e.g., BFT-SMaRt. We show, for the first time, that the read-only request optimization introduced in PBFT more than 20 years ago can violate its liveness. Notably, the problem affects not only the optimized read-only operations but also standard, totally-ordered operations. We show this weakness by presenting an attack in which a malicious leader blocks correct clients and present two solutions for patching the protocol, making read-only operations fast and correct. The two solutions were implemented on BFT-SMaRt and evaluated in different scenarios, showing their effectiveness in preventing the identified attack.

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arXiv2020

AWARE: Adaptive Wide-Area Replication for Fast and Resilient Byzantine Consensus

With upcoming blockchain infrastructures, world-spanning Byzantine consensus is getting practical and necessary. In geographically distributed systems, the pace at which consensus is achieved is limited by the heterogenous latencies of connections between replicas. If deployed on a wide-area network, consensus-based systems benefit from weighted replication, an approach that utilizes extra replicas and assigns higher voting power to well connected replicas. This enables more choice in quorum formation and replicas can leverage proportionally smaller quorums to advance, thus decreasing consensus latency. However, the system needs a solution to autonomously adjust to its environment if network conditions change or faults occur. We present Adaptive Wide-Area REplication (AWARE), a mechanism which improves the geographical scalability of consensus with nodes being widely spread across the world. Essentially, AWARE is an automated and dynamic voting weight tuning and leader positioning scheme, which supports the emergence of fast quorums in the system. It employs a reliable self-monitoring process and provides a prediction model seeking to minimize the system's consensus latency. In experiments using several AWS EC2 regions, AWARE dynamically optimizes consensus latency by self-reliantly finding a fast weight configuration yielding latency gains observed by clients located across the globe.

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arXiv2020

Auditable Register Emulations

The widespread prevalence of data breaches amplifies the importance of auditing storage systems. In this work, we initiate the study of auditable storage emulations, which provide the capability for an auditor to report the previously executed reads in a register. We precisely define the notion of auditable register and its properties, and establish tight bounds and impossibility results for auditable storage emulations in the presence of faulty storage objects. Our formulation considers loggable read-write registers that securely store data using information dispersal and support fast reads. In such a scenario, given a maximum number~$f$ of faulty storage objects and a minimum number~$τ$ of data blocks required to recover a stored value, we prove that (1) auditability is impossible if $τ\leq 2f $; (2) implementing a weak form of auditability requires $τ\geq 3f+1$; and (3) a stronger form of auditability is impossible. We also show that signing read requests overcomes the lower bound of weak auditability, while totally ordering operations or using non-fast reads enables strong auditability.

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arXiv2020

From Byzantine Replication to Blockchain: Consensus is only the Beginning

The popularization of blockchains leads to a resurgence of interest in Byzantine Fault-Tolerant (BFT) state machine replication protocols. However, much of the work on this topic focuses on the underlying consensus protocols, with emphasis on their lack of scalability, leaving other subtle limitations unaddressed. These limitations are related to the effects of maintaining a durable blockchain instead of a write-ahead log and the requirement for reconfiguring the set of replicas in a decentralized way. We demonstrate these limitations using a digital coin blockchain application and BFT-SMaRt, a popular BFT replication library. We show how they can be addressed both at a conceptual level, in a protocol-agnostic way, and by implementing SMaRtChain, a blockchain platform based on BFT-SMaRt. SMaRtChain improves the performance of our digital coin application by a factor of eight when compared with a naive implementation on top of BFT-SMaRt. Moreover, SMaRtChain achieves a throughput $8\times$ and $33\times$ better than Tendermint and Hyperledger Fabric, respectively, when ensuring strong durability on its blockchain.

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arXiv2020

Smart Contracts on the Move

Blockchain systems have received much attention and promise to revolutionize many services. Yet, despite their popularity, current blockchain systems exist in isolation, that is, they cannot share information. While interoperability is crucial for blockchain to reach widespread adoption, it is difficult to achieve due to differences among existing blockchain technologies. This paper presents a technique to allow blockchain interoperability. The core idea is to provide a primitive operation to developers so that contracts and objects can switch from one blockchain to another, without breaking consistency and violating key blockchain properties. To validate our ideas, we implemented our protocol in two popular blockchain clients that use the Ethereum virtual machine. We discuss how to build applications using the proposed protocol and show examples of applications based on real use cases that can move across blockchains. To analyze the system performance we use a real trace from one of the most popular Ethereum applications and replay it in a multi-blockchain environment.

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arXiv2019

Processing Tweets for Cybersecurity Threat Awareness

Receiving timely and relevant security information is crucial for maintaining a high-security level on an IT infrastructure. This information can be extracted from Open Source Intelligence published daily by users, security organisations, and researchers. In particular, Twitter has become an information hub for obtaining cutting-edge information about many subjects, including cybersecurity. This work proposes SYNAPSE, a Twitter-based streaming threat monitor that generates a continuously updated summary of the threat landscape related to a monitored infrastructure. Its tweet-processing pipeline is composed of filtering, feature extraction, binary classification, an innovative clustering strategy, and generation of Indicators of Compromise (IoCs). A quantitative evaluation considering all tweets from 80 accounts over more than 8 months (over 195.000 tweets), shows that our approach timely and successfully finds the majority of security-related tweets concerning an example IT infrastructure (true positive rate above 90%), incorrectly selects a small number of tweets as relevant (false positive rate under 10%), and summarises the results to very few IoCs per day. A qualitative evaluation of the IoCs generated by SYNAPSE demonstrates their relevance (based on the CVSS score and the availability of patches or exploits), and timeliness (based on threat disclosure dates from NVD).

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