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检索条件"任意字段=Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy"

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NEUTRON: A Graph-based Pipeline for Zero-trust Network Architectures 22

NEUTRON: A Graph-based Pipeline for Zero-trust Network Archi...

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acm conference on data and application security and privacy (CODASPY)

作者： Katsis, Charalampos Cicala, Fabrizio Thomsen, Dan Ringo, Nathan Bertino, Elisa Purdue Univ W Lafayette IN 47907 USA SIFT LLC Minneapolis MN USA

ISBN: (纸本)9781450392204

The Zero-Trust Architecture (ZTA) security paradigm deploys comprehensive user- and resource-aware defenses both at the network's perimeter and inside the network. However, deploying a ZTA approach requires specifying and managing a large, network spanning set of fine-grained security policies, which will increase administrators' workloads and increase the chance of errors. This paper presents the design and prototype implementation of the NEUTRON policy framework, which provides an automated end-to-end policy pipeline, specification, management, testing, and deployment. NEUTRON uses a flexible, graph-based approach to specify and share complex, fine-grained network security policies. NEUTRON provides a software structure so that policy patterns may be easily shared between organizations, reducing the burden of creating the policy. Administrators assemble the software for their site, and the NEUTRON policy generator creates the entire network-wide security policy. Treating the security policy like software also allows new approaches to policy verification and policy change impact analysis. Thus we designed the security Policy Regression Tool (SPRT), which uses our novel Ruleset Aggregation Algorithm to perform scalable verification of the network-wide security policy across the network model. Moreover, our graph-based framework allows for efficient computation and visualization of the policy change impact.

关键词： security policy enforcement testing access control network security zero trust architecture least privilege policy

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Leveraging Synthetic data and PU Learning For Phishing Email Detection 22

Leveraging Synthetic Data and PU Learning For Phishing Email...

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acm conference on data and application security and privacy (CODASPY)

作者： Qachfar, Fatima Zahra Verma, Rakesh M. Mukherjee, Arjun Univ Houston Houston TX 77004 USA

ISBN: (纸本)9781450392204

Imbalanced data classification has always been one of the most challenging problems in data science especially in the cybersecurity field, where we observe an out-of-balance proportion between benign and phishing examples in security datasets. Even though there are many phishing detection methods in literature, most of them neglect the imbalanced nature of phishing email datasets. In this paper, we examine the imbalanced property by varying legitimate to phishing class ratios. We generate new synthetic instances using a generative adversarial network model for long sentences (Leak-GAN) to balance out the training process and ameliorate its impact on classification. These synthetic instances are labeled by positive-unlabeled learning and added to the initial imbalanced training set. The resulting dataset is given to the Bidirectional Encoder Representations from Transformers (BERT) model for sequence classification. We compare several state-of-the-art methods from the literature against our approach, which achieves a high performance throughout all the imbalanced ratios reaching an F1-score of 99.6% for the most extreme imbalanced ratio and an F1-score of 99.8% for balanced cases.

关键词： Imbalanced data generative adversarial network positive-unlabeled learning sequence classification semi-supervised learning

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Securing Smart Grids Through an Incentive Mechanism for Blockchain-Based data Sharing 22

Securing Smart Grids Through an Incentive Mechanism for Bloc...

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acm conference on data and application security and privacy (CODASPY)

作者： Reijsbergen, Daniel Maw, Aung Tien Tuan Anh Dinh Li, Wen-Tai Yuen, Chau Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9781450392204

Smart grids leverage the data collected from smart meters to make important operational decisions. However, they are vulnerable to False data Injection (FDI) attacks in which an attacker manipulates meter data to disrupt the grid operations. Existing works on FDI are based on a simple threat model in which a single grid operator has access to all the data, and only some meters can be compromised. Our goal is to secure smart grids against FDI under a realistic threat model. To this end, we present a threat model in which there are multiple operators, each with a partial view of the grid, and each can be fully compromised. An effective defense against FDI in this setting is to share data between the operators. However, the main challenge here is to incentivize data sharing. We address this by proposing an incentive mechanism that rewards operators for uploading data, but penalizes them if the data is missing or anomalous. We derive formal conditions under which our incentive mechanism is provably secure against operators who withhold or distort measurement data for profit. We then implement the data sharing solution on a private blockchain, introducing several optimizations that overcome the inherent performance limitations of the blockchain. Finally, we conduct an experimental evaluation that demonstrates that our implementation has practical performance.

关键词： data security false data injection smart grids blockchains

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Why You've Got Mail: Evaluating Inbox privacy Implications of Email Marketing Practices in Online Apps and Services 25

Why You've Got Mail: Evaluating Inbox Privacy Implications o...

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proceedings of the fifteenth acm conference on data and application security and privacy

作者： Scott Seidenberger Oluwasijibomi Ajisegiri Noah Pursell Fazil Raja Anindya Maiti University of Oklahoma Norman OK USA

ISBN: (纸本)9798400714764

This study explores the widespread perception that personal data, such as email addresses, may be shared or sold without informed user consent, investigating whether these concerns are reflected in actual practices of popular online services and apps. Over the course of a year, we collected and analyzed the source, volume, frequency, and content of emails received by users after signing up for the 150 most popular online services and apps across various sectors. By examining patterns in email communications, we aim to identify consistent strategies used across industries, including potential signs of third-party data sharing. This analysis provides a critical evaluation of how email marketing tactics may intersect with data-sharing practices, with important implications for consumer privacy and regulatory oversight. Our study findings, conducted post-CCPA and GDPR, indicate that while no unknown third-party spam email was detected, internal and authorized third-party email marketing practices were pervasive, with companies frequently sending promotional and CRM emails despite opt-out preferences. The framework established in this work is designed to be scalable, allowing for continuous monitoring, and can be extended to include a more diverse set of apps and services for broader analysis, ultimately contributing to transparency in email address privacy practices.

关键词： email marketing

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Harmonizing Differential privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence 25

Harmonizing Differential Privacy Mechanisms for Federated Le...

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proceedings of the fifteenth acm conference on data and application security and privacy

作者： Shuya Feng Meisam Mohammady Hanbin Hong Shenao Yan Ashish Kundu Binghui Wang Yuan Hong University of Connecticut Storrs CT USA Iowa State University Ames IA USA Cisco Ressearch San Jose CA USA Illinois Institute of Technology Chicago IL USA

ISBN: (纸本)9798400714764

Differentially private federated learning (DP-FL) offers a compelling approach to collaborative model training by ensuring robust privacy for clients. Despite its potential, current methods face challenges in effectively balancing privacy, utility, and performance across diverse federated learning scenarios. Addressing these challenges, we introduce UDP-FL, to our knowledge the first DP-FL framework that universally harmonizes any randomization mechanism, including those considered optimal, by employing the Gaussian Moments Accountant (viz. DP-SGD). Central to UDP-FL is the 'Harmonizer,' a dynamic module engineered to intelligently select and apply the most suitable DP mechanism tailored to each client's specific privacy requirements, data sensitivities, and computational capacities. This selection process is driven by the principle of Rényi Differential privacy, which serves as a crucial mediator for aligning privacy budgets effectively. Our comprehensive evaluation of UDP-FL, benchmarked against established baseline methods, demonstrates superior performance in upholding privacy guarantees and enhancing model functionality. The framework's robustness has been rigorously tested against a broad spectrum of privacy attacks, making it one of the most thorough validations of a DP-FL framework to date.

关键词： differential privacy

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Enhanced Threat Modeling and Attack Scenario Generation for OAuth 2.0 Implementations: data/Toolset paper 25

Enhanced Threat Modeling and Attack Scenario Generation for ...

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proceedings of the fifteenth acm conference on data and application security and privacy

作者： Pieter Philippaerts Stef Verreydt Wouter Joosen KU Leuven Leuven Belgium

ISBN: (纸本)9798400714764

OAuth 2.0 is a widely adopted authorization framework enabling secure, delegated access to resources on behalf of a user. While the protocol is robust when implemented correctly, real-world deployments often exhibit vulnerabilities due to misconfigurations, incomplete mitigations, or misunderstandings of its intricacies. (Semi-)automated testing is therefore essential to identify and address these security flaws. Among available tools, OAuch offers the most comprehensive benchmark for assessing OAuth IdP implementations by identifying potential threats based on the OAuth threat model and related standards. However, OAuch has notable limitations, including an incomplete threat model, ambiguous threat classifications, and a lack of support for multi-vulnerability attack scenarios. This paper presents enhancements to OAuch that improve the tool's usability, including enriched metadata, the introduction of attack scenarios for multi-threat analyses, and a likelihood assessment to prioritize mitigation efforts.

关键词： OAuth 2.0

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proceedings of 8th International conference on Cyber security and Information Engineering, ICCSIE 2023

Proceedings of 8th International Conference on Cyber Securit...

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8th International conference on Cyber security and Information Engineering, ICCSIE 2023

ISBN: (纸本)9798400708800

The proceedings contain 55 papers. The topics discussed include: chaotic digital image encryption based on Fourier transform;structure and key technologies of nuclear power plant network security situational awareness platform;a research of improved facial expression recognition method based on CBAM and joint-normalization techniques;research on substation quantum secure communication system based on 5g private network technology;research on network intrusion detection techniques based on feature selection model and recurrent neural network;a time-bound data access control scheme based on attribute-based encryption;robust watermarking based on dual-random phase encryption against collusion attacks;research progress of location privacy protection technology in the Internet era;the application and practice of computer network security technology in electronic information engineering;and tag anti-collision algorithm for RFID in underdetermined states based on local sparse constraints.

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Spend Your Budget Wisely: Towards an Intelligent Distribution of the privacy Budget in Differentially Private Text Rewriting 25

Spend Your Budget Wisely: Towards an Intelligent Distributio...

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proceedings of the fifteenth acm conference on data and application security and privacy

作者： Stephen Meisenbacher Chaeeun Joy Lee Florian Matthes School of Computation Information and Technology Technical University of Munich Garching Germany

ISBN: (纸本)9798400714764

The task of Differentially Private Text Rewriting is a class of text privatization techniques in which (sensitive) input textual documents are rewritten under Differential privacy (DP) guarantees. The motivation behind such methods is to hide both explicit and implicit identifiers that could be contained in text, while still retaining the semantic meaning of the original text, thus preserving utility. Recent years have seen an uptick in research output in this field, offering a diverse array of word-, sentence-, and document-level DP rewriting methods. Common to these methods is the selection of a privacy budget (i.e., the ε parameter), which governs the degree to which a text is privatized. One major limitation of previous works, stemming directly from the unique structure of language itself, is the lack of consideration of where the privacy budget should be allocated, as not all aspects of language, and therefore text, are equally sensitive or personal. In this work, we are the first to address this shortcoming, asking the question of how a given privacy budget can be intelligently and sensibly distributed amongst a target document. We construct and evaluate a toolkit of linguistics- and NLP-based methods used to allocate a privacy budget to constituent tokens in a text document. In a series of privacy and utility experiments, we empirically demonstrate that given the same privacy budget, intelligent distribution leads to higher privacy levels and more positive trade-offs than a naive distribution of epsilon. Our work highlights the intricacies of text privatization with DP, and furthermore, it calls for further work on finding more efficient ways to maximize the privatization benefits offered by DP in text rewriting.

关键词： data privacy

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TerrARA: Automated security Threat Modeling for Infrastructure as Code 25

TerrARA: Automated Security Threat Modeling for Infrastructu...

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proceedings of the fifteenth acm conference on data and application security and privacy

作者： Anh-Duy Tran Laurens Sion Koen Yskout Wouter Joosen DistriNet KU Leuven Leuven Belgium

ISBN: (纸本)9798400714764

The emergence of DevOps is accompanied by an increased use of Infrastructure as Code (IaC) to specify and manage deployment configurations, infrastructure, and associated resources. Terraform is one such IaC solution. However, improper configurations can lead to serious security threats. This paper introduces an approach, implemented as TerrARA, that provides a systematic and structured way for automatically eliciting security threats based on Terraform configuration files. Specifically, TerrARA: (1) automates the construction of an abstract model-an enriched data Flow Diagram (DFD)-from Terraform configuration files for Amazon Web Services (AWS), and it can be extended to other resources and cloud providers via profiles; (2) encodes cloud computing threat patterns, which are utilized by the SPARTA threat modeling engine to automatically identify security threats; and (3) demonstrates its capability in accurately extracting DFDs from Terraform projects and eliciting relevant cloud computing security threats, achieving high accuracy and reasonable performance compared to existing tools and approaches like StartLeft and GPT-4o. By integrating it into CI/CD pipelines, the automated reconstruction and analysis enable continuous security assessments that systematically incorporate cloud infrastructure artifacts into the threat modeling process.

关键词： data flow diagram

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SmartFusion2 SoC as a security Module for the IoT world 22

SmartFusion2 SoC as a Security Module for the IoT world

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19th acm International conference on Computing Frontiers (CF)

作者： Rodrigues, Alexandre Resende, Joao Carlos Chaves, Ricardo Univ Lisbon INESC ID IST Lisbon Portugal

ISBN: (纸本)9781450393386

Dedicated computational devices such as HSMs and FPGAs are frequently used to provide data security and privacy. However, these options have several drawbacks, particularly when considering IoT environments. HSMs offer high-grade services but are costly and lack application flexibility, while FPGAs, in general, are cheaper and adaptable, but lack security services and protection. Herein, the SmartFusion2 SoC FPGA, a security-oriented system, is evaluated as a possible low-cost and flexible platform for security modules for the IoT. This work analyzes the several security services of the SmartFusion2 SoC, their advantages, and possible trade-offs. To demonstrate the SoC viability as a security module and/or a more adaptable HSM alternative, several case study applications are considered and analyzed to elaborate on the potential, limitations, and mitigations of the latter.

关键词： SmartFusion2 SoC security module Low cost Flexible

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