arXiv subjects

Andrey Brito

Publications and source records attributed to Andrey Brito.

14 recordsLinked to original sources

arXiv2025

A Comprehensive Study on the Impact of Vulnerable Dependencies on Open-Source Software

Open-source libraries are widely used by software developers to speed up the development of products, however, they can introduce security vulnerabilities, leading to incidents like Log4Shell. With the expanding usage of open-source libraries, it becomes even more imperative to comprehend and address these dependency vulnerabilities. The use of Software Composition Analysis (SCA) tools does greatly help here as they provide a deep insight on what dependencies are used in a project, enhancing the security and integrity in the software supply chain. In order to learn how wide spread vulnerabilities are and how quickly they are being fixed, we conducted a study on over 1k open-source software projects with about 50k releases comprising several languages such as Java, Python, Rust, Go, Ruby, PHP, and JavaScript. Our objective is to investigate the severity, persistence, and distribution of these vulnerabilities, as well as their correlation with project metrics such as team and contributors size, activity and release cycles. In order to perform such analysis, we crawled over 1k projects from github including their version history ranging from 2013 to 2023 using VODA, our SCA tool. Using our approach, we can provide information such as library versions, dependency depth, and known vulnerabilities, and how they evolved over the software development cycle. Being larger and more diverse than datasets used in earlier works and studies, ours provides better insights and generalizability of the gained results. The data collected answers several research questions about the dependency depth and the average time a vulnerability persists. Among other findings, we observed that for most programming languages, vulnerable dependencies are transitive, and a critical vulnerability persists in average for over a year before being fixed.

cs.SE↗

arXiv2025

TICAL: Trusted and Integrity-protected Compilation of AppLications

During the past few years, we have witnessed various efforts to provide confidentiality and integrity for applications running in untrusted environments such as public clouds. In most of these approaches, hardware extensions such as Intel SGX, TDX, AMD SEV, etc., are leveraged to provide encryption and integrity protection on process or VM level. Although all of these approaches increase the trust in the application at runtime, an often overlooked aspect is the integrity and confidentiality protection at build time, which is equally important as maliciously injected code during compilation can compromise the entire application and system. In this paper, we present Tical, a practical framework for trusted compilation that provides integrity protection and confidentiality in build pipelines from source code to the final executable. Our approach harnesses TEEs as runtime protection but enriches TEEs with file system shielding and an immutable audit log with version history to provide accountability. This way, we can ensure that the compiler chain can only access trusted files and intermediate output, such as object files produced by trusted processes. Our evaluation using micro- and macro-benchmarks shows that Tical can protect the confidentiality and integrity of whole CI/CD pipelines with an acceptable performance overhead.

cs.CR↗

arXiv2024

CRISP: Confidentiality, Rollback, and Integrity Storage Protection for Confidential Cloud-Native Computing

Trusted execution environments (TEEs) protect the integrity and confidentiality of running code and its associated data. Nevertheless, TEEs' integrity protection does not extend to the state saved on disk. Furthermore, modern cloud-native applications heavily rely on orchestration (e.g., through systems such as Kubernetes) and, thus, have their services frequently restarted. During restarts, attackers can revert the state of confidential services to a previous version that may aid their malicious intent. This paper presents CRISP, a rollback protection mechanism that uses an existing runtime for Intel SGX and transparently prevents rollback. Our approach can constrain the attack window to a fixed and short period or give developers the tools to avoid the vulnerability window altogether. Finally, experiments show that applying CRISP in a critical stateful cloud-native application may incur a resource increase but only a minor performance penalty.

cs.CR↗

arXiv2023

A Last-Level Defense for Application Integrity and Confidentiality

Our objective is to protect the integrity and confidentiality of applications operating in untrusted environments. Trusted Execution Environments (TEEs) are not a panacea. Hardware TEEs fail to protect applications against Sybil, Fork and Rollback Attacks and, consequently, fail to preserve the consistency and integrity of applications. We introduce a novel system, LLD, that enforces the integrity and consistency of applications in a transparent and scalable fashion. Our solution augments TEEs with instantiation control and rollback protection. Instantiation control, enforced with TEE-supported leases, mitigates Sybil/Fork Attacks without incurring the high costs of solving crypto-puzzles. Our rollback detection mechanism does not need excessive replication, nor does it sacrifice durability. We show that implementing these functionalities in the LLD runtime automatically protects applications and services such as a popular DBMS.

cs.CR↗

arXiv2023

Triad: Trusted Timestamps in Untrusted Environments

We aim to provide trusted time measurement mechanisms to applications and cloud infrastructure deployed in environments that could harbor potential adversaries, including the hardware infrastructure provider. Despite Trusted Execution Environments (TEEs) providing multiple security functionalities, timestamps from the Operating System are not covered. Nevertheless, some services require time for validating permissions or ordering events. To address that need, we introduce Triad, a trusted timestamp dispatcher of time readings. The solution provides trusted timestamps enforced by mutually supportive enclave-based clock servers that create a continuous trusted timeline. We leverage enclave properties such as forced exits and CPU-based counters to mitigate attacks on the server's timestamp counters. Triad produces trusted, confidential, monotonically-increasing timestamps with bounded error and desirable, non-trivial properties. Our implementation relies on Intel SGX and SCONE, allowing transparent usage. We evaluate Triad's error and behavior in multiple dimensions.

cs.CR↗

arXiv2020

TEEMon: A continuous performance monitoring framework for TEEs

Trusted Execution Environments (TEEs), such as Intel Software Guard eXtensions (SGX), are considered as a promising approach to resolve security challenges in clouds. TEEs protect the confidentiality and integrity of application code and data even against privileged attackers with root and physical access by providing an isolated secure memory area, i.e., enclaves. The security guarantees are provided by the CPU, thus even if system software is compromised, the attacker can never access the enclave's content. While this approach ensures strong security guarantees for applications, it also introduces a considerable runtime overhead in part by the limited availability of protected memory (enclave page cache). Currently, only a limited number of performance measurement tools for TEE-based applications exist and none offer performance monitoring and analysis during runtime. This paper presents TEEMon, the first continuous performance monitoring and analysis tool for TEE-based applications. TEEMon provides not only fine-grained performance metrics during runtime, but also assists the analysis of identifying causes of performance bottlenecks, e.g., excessive system calls. Our approach smoothly integrates with existing open-source tools (e.g., Prometheus or Grafana) towards a holistic monitoring solution, particularly optimized for systems deployed through Docker containers or Kubernetes and offers several dedicated metrics and visualizations. Our evaluation shows that TEEMon's overhead ranges from 5% to 17%.

cs.CR↗

arXiv2019

ABEONA: an Architecture for Energy-Aware Task Migrations from the Edge to the Cloud

This paper presents our preliminary results with ABEONA, an edge-to-cloud architecture that allows migrating tasks from low-energy, resource-constrained devices on the edge up to the cloud. Our preliminary results on artificial and real world datasets show that it is possible to execute workloads in a more efficient manner energy-wise by scaling horizontally at the edge, without negatively affecting the execution runtime.

cs.DC↗

arXiv2019

Designing for Pragmatists and Fundamentalists: Privacy Concerns and Attitudes on the Internet of Things

Internet of Things (IoT) systems have aroused enthusiasm and concerns. Enthusiasm comes from their utilities in people daily life, and concerns may be associated with privacy issues. By using two IoT systems as case-studies, we examine users' privacy beliefs, concerns and attitudes. We focus on four major dimensions: the collection of personal data, the inference of new information, the exchange of information to third parties, and the risk-utility trade-off posed by the features of the system. Altogether, 113 Brazilian individuals answered a survey about such dimensions. Although their perceptions seem to be dependent on the context, there are recurrent patterns. Our results suggest that IoT users can be classified into unconcerned, fundamentalists and pragmatists. Most of them exhibit a pragmatist profile and believe in privacy as a right guaranteed by law. One of the most privacy concerning aspect is the exchange of personal information to third parties. Individuals' perceived risk is negatively correlated with their perceived utility in the features of the system. We discuss practical implications of these results and suggest heuristics to cope with privacy concerns when designing IoT systems.

cs.HC↗

arXiv2019

PubSub-SGX: Exploiting Trusted Execution Environments for Privacy-Preserving Publish/Subscribe Systems

This paper presents PUBSUB-SGX, a content-based publish-subscribe system that exploits trusted execution environments (TEEs), such as Intel SGX, to guarantee confidentiality and integrity of data as well as anonymity and privacy of publishers and subscribers. We describe the technical details of our Python implementation, as well as the required system support introduced to deploy our system in a container-based runtime. Our evaluation results show that our approach is sound, while at the same time highlighting the performance and scalability trade-offs. In particular, by supporting just-in-time compilation inside of TEEs, Python programs inside of TEEs are in general faster than when executed natively using standard CPython.

cs.DC↗

arXiv2019

Security and Privacy Preserving Data Aggregation in Cloud Computing

Smart metering is an essential feature of smart grids, allowing residential customers to monitor and reduce electricity costs. Devices called smart meters allows residential customers to monitor and reduce electricity costs, promoting energy saving, demand management, and energy efficiency. However, monitoring a households' energy consumption through smart meters poses serious privacy threats, and have thus become a major privacy issue. Hence, a significant amount of research has appeared recently with the purpose of providing methods and mechanisms to reconcile smart metering technologies and privacy requirements. However, most current approaches fall short in meeting one of several of the requirements for privacy preserving smart metering systems. In this paper we show how Intel SGX technology can be used to provide a simple and general solution for the smart metering privacy problem that meets all these requirements in a satisfactory way. Moreover, we present also an implementation of the proposed architecture as well as a series of experiments that have been carried out in order to assess how the proposed solution performs in comparison to a second implementation of the architecture that completely disregards privacy issues.

cs.CR↗

arXiv2018

Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction.

cs.LG↗

arXiv2018

Secure and Privacy-Aware Data Dissemination for Cloud-Based Applications

In this paper we propose a data dissemination platform that supports data security and different privacy levels even when the platform and the data are hosted by untrusted infrastructures. The proposed system aims at enabling an application ecosystem that uses off-the-shelf trusted platforms (in this case, Intel SGX), so that users may allow or disallow third parties to access the live data stream with a specific sensitivity-level. Moreover, this approach does not require users to manage the encryption keys directly. Our experiments show that such an approach is indeed practical for medium scale systems, where participants disseminate small volumes of data at a time, such as in smart grids and IoT environments.

cs.DC↗

arXiv2017

DynSGX: A Privacy Preserving Toolset for Dynamically Loading Functions into Intel(R) SGX Enclaves

Intel(R) Software Guard eXtensions (SGX) is a hardware-based technology for ensuring security of sensitive data from disclosure or modification that enables user-level applications to allocate protected areas of memory called enclaves. Such memory areas are cryptographically protected even from code running with higher privilege levels. This memory protection can be used to develop secure and dependable applications, but the technology has some limitations: ($i$) the code of an enclave is visible at load time, ($ii$) libraries used by the code must be statically linked, and ($iii$) the protected memory size is limited, demanding page swapping to be done when this limit is exceeded. We present DynSGX, a privacy preserving tool that enables users and developers to dynamically load and unload code to be executed inside SGX enclaves. Such a technology makes possible that developers use public cloud infrastructures to run applications based on sensitive code and data. Moreover, we present a series of experiments that assess how applications dynamically loaded by DynSGX perform in comparison to statically linked applications that disregard privacy of the enclave code at load time.

cs.CR↗

arXiv2013

Operating the Cloud from Inside Out

Virtual machine images and instances (VMs) in cloud computing centres are typically designed as isolation containers for applications, databases and networking functions. In order to build complex distributed applications, multiple virtual machines must be connected, orchestrated and combined with platform and infrastructure services from the hosting environment. There are several reasons why sometimes it is beneficial to introduce a new layer, Cloud-in-a-VM, which acts as a portable management interface to a cluster of VMs. We reason about the benefits and present our Cloud-in-a-VM implementation called Nested Cloud which allows consumers to become light-weight cloud operators on demand and reap multiple advantages, including fully utilised resource allocations. The practical usefulness and the performance of the intermediate cloud stack VM are evaluated in a marketplace scenario.

cs.DC↗