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检索条件"任意字段=14th ACM Conference on Data and Application Security and Privacy, CODASPY 2024"

共 54 条记录，以下是11-20 订阅

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An Overview of threats Exploring the Confusion Between Top-Level Domains and File Type Extensions 24

An Overview of Threats Exploring the Confusion Between Top-L...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Sales, Anderson Torres, Nuno Pinto, Pedro Inst Politecn Viana do Castelo Viana Do Castelo Portugal INESC TEC Porto Portugal

ISBN: (纸本)9798400704215

Cyberattacks exploit deceptions involving the Domain Name Service (DNS) to direct users to fake websites, such as typosquatting attacks, which exploit natural typographical errors, and homograph attacks, where different Unicode characters resemble the legitimate ones. the deception attacks may also exploit the confusion between DNS domain names, specifically Top-Level Domains (TLDs), and file extensions. Recently, two new TLDs were added, "zip" and "mov", sharing names with certain file types. this overlapping can be explored by malicious actors in a range of threat scenarios to compromise user security. this paper provides an overview of threats originating from the confusion between specific TLDs and file extensions, such as the recent "zip" and "mov". the threats are grouped into 6 threat scenarios that are described and discussed. this research can be part of a more comprehensive strategy that includes addressing the risks associated with these threats and designing future strategies to address the threats associated with exploiting this ambiguity.

关键词： Top-Level Domains File Extensions security threats Cyber security Cyber Attack

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CoCoT: Collaborative Contact Tracing 24

CoCoT: Collaborative Contact Tracing

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Kann, Trevor Bauer, Lujo Cunningham, Robert K. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9798400704215

Contact tracing can limit the spread of infectious diseases by notifying people of potential exposure to disease. Manual contact tracing is resource-intensive, but much of it can be automated using mobile phones, which are ubiquitous and can detect and record nearby contacts. Two major problems arise with automated contact tracing (ACT): preventing abuse for mass surveillance and accurately determining contacts. For example, the most widely adopted solution-Google and Apple's Exposure Notification (GAEN)-protects user privacy but suffers from inaccurate distance measurements that result in poor risk assessments. We propose to use collaboration among nearby devices to increase distance estimation accuracy, and therefore risk assessment accuracy, while minimizing the loss of user privacy. Our protocol, CoCoT, extends GAEN, a proximity-based, distributed ACT protocol, by adding an additional broadcast to share locally-derived distance estimates. To evaluate CoCoT, we develop a method for merging phone sensor datasets with human interaction datasets to approximate realistic scenarios and test our protocol. CoCoT improves distance estimate accuracy by 28% over the current best distance estimators and we analytically show impact on privacy, security, and battery consumption are minimal.

关键词： Automatic Contact Tracing Distributed Optimization Network Localization Smartphone privacy

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Examining Cryptography and Randomness Failures in Open-Source Cellular Cores 24

Examining Cryptography and Randomness Failures in Open-Sourc...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： English, K. Virgil Bennett, Nathaniel thorn, Seaver Butler, Kevin R. B. Enck, William Traynor, Patrick North Carolina State Univ Raleigh NC 27695 USA Univ Florida Gainesville FL USA

ISBN: (纸本)9798400704215

Industry is increasingly adopting private 5G networks to securely manage their wireless devices in retail, manufacturing, natural resources, and healthcare. As with most technology sectors, open-source software is well poised to form the foundation of deployments, whether it is deployed directly or as part of well-maintained proprietary offerings. this paper seeks to examine the use of cryptography and secure randomness in open-source cellular cores. We design a set of 13 CodeQL static program analysis rules for cores written in both C/C++ and Go and apply them to 7 open-source cellular cores implementing 4G and 5G functionality. We identify two significant security vulnerabilities, including predictable generation of TMSIs and improper verification of TLS certificates, with each vulnerability affecting multiple cores. In identifying these flaws, we hope to correct implementations to fix downstream deployments and derivative proprietary projects.

关键词： cellular core security cryptography misuse static analysis

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Coherent Multi-Table data Synthesis for Tabular and Time-Series data with GANs 24

Coherent Multi-Table Data Synthesis for Tabular and Time-Ser...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Elliker, Clement Tonnelier, Emeric Shabou, Aymen Grp Credit Agricole DataLab Grp Paris France

ISBN: (纸本)9798400704215

As the usage of user-private-data is increasingly monitored by regulatory institutions for security purposes, its transfer becomes more constrained. Synthetic data has recently emerged as a viable alternative to prevent the disclosure of user-protected information that complies with data sharing regulations. Both public and private sectors commonly use a combination of tabular and time-series tables that often contains user-related sensitive information. they are usually intrinsically interlinked as they describe the users and their behaviors over different perimeters. Moreover, it contains both numerical and categorical features, adding complexity to the anonymization task. State of the art generative methods, specialized either in tabular or time-series data, are able to generate high quality synthetic data. However, if each table is generated independently, it becomes impossible to link them. As a result, the usability of such synthetic data is impacted. To address this issue, we not only propose a coherent multi-table generative model that uses Generative Adversarial Networks (GANs) to sample both tabular and time-series tables, but also a conditional time-series generative model that handles both numerical and categorical features. Additionally, many experiments are conducted to analyse the inner modules of our model and evaluate it on an in-house private dataset in order to prove the viability of the synthetic data generated for machine learning tasks.

关键词： synthetic data generative ai gan tabular data time-series data deep learning data privacy

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security Policy Generation and Verification through Large Language Models: A proposal 24

Security Policy Generation and Verification through Large La...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Martinelli, Fabio Mercaldo, Francesco Petrillo, Luca Santone, Antonella CNR IIT Pisa Italy Univ Molise Campobasso Italy CNR IIT Campobasso Italy IMT Sch Adv Studies Lucca Lucca Italy CNR IIT Lucca Italy

ISBN: (纸本)9798400704215

the ability to manage and enforce security policies in a rapidly changing digital environment is vital. When enforcing and verifying policies on complex systems, it can be challenging for them to be understood by humans. Furthermore, updating access control rules or configurations that are translated from natural language documents might take considerable time and eventually cause errors and damage. It is crucial for the rules and configurations to be modified with care in order to prevent such mistakes. the aim of the following proposal is the design of a method to convert security policies written in natural language into a machine-understandable format that can easily be enforced, verified as well and understood by machines. To this end, we propose using advanced policy languages like XacmL and Rego to translate security policies into formats that are automatically enforceable and verifiable by the system. In addition, this transformation will facilitate the implementation of security policies within modern application architectures including micro-services and cloud-native environments. therefore, under its auspices, a framework that eases policy enforcement and minimizes human errors while ensuring conformity to safety standards set out shall be born out of this research proposal.

关键词： security policy machine learning testing

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CCSM: Building Cross-Cluster security Models for Edge-Core Environments Involving Multiple Kubernetes Clusters 24

CCSM: Building Cross-Cluster Security Models for Edge-Core E...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： GholipourChoubeh, Mahmood Kermabon-Bobinnec, Hugo Majumdar, Suryadipta Jarraya, Yosr Wang, Lingyu Nour, Boubakr Pourzandi, Makan Concordia Univ Montreal PQ Canada Ericsson Secur Res Montreal PQ Canada

ISBN: (纸本)9798400704215

With the emergence of 5G networks and their large scale applications such as IoT and autonomous vehicles, telecom operators are increasingly offloading the computation closer to customers (i.e., on the edge). Such edge-core environments usually involve multiple Kubernetes clusters potentially owned by different providers. Confidentiality concerns could prevent those providers from sharing data freely with each other, which makes it challenging to perform common security tasks such as security verification across different clusters. In this work, we propose a solution for building cross-cluster security models to enable various security analyses, while preserving confidentiality for each cluster. We design a six-step methodology to model both the cross-cluster communication and cross-cluster event dependency, and we apply those models to different security use cases. We implement our solution based on a 5G edge-core environment that involves multiple Kubernetes clusters, and our experimental results demonstrate its efficiency (e.g., less than 8 seconds of processing time for a model with 3,600 edges and nodes) and accuracy (e.g., more than 96% for cross-cluster event prediction).

关键词： Cloud Computing security Kubernetes 5G security Model

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Towards Accurate and Stronger Local Differential privacy for Federated Learning with Staircase Randomized Response 24

Towards Accurate and Stronger Local Differential Privacy for...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Varun, Matta Feng, Shuya Wang, Han Sural, Shamik Hong, Yuan Indian Inst Technol Kharagpur India Univ Connecticut Storrs CT USA Univ Kansas Lawrence KS USA

ISBN: (纸本)9798400704215

Federated Learning (FL), a privacy-preserving training approach, has proven to be effective, yet its vulnerability to attacks that extract information from model weights is widely recognized. To address such privacy concerns, Local Differential privacy (LDP) has been applied to FL: perturbing the weights trained for the local model by each client. However, besides high utility loss on the randomized model weights, we identify a new inference attack to the existing LDP method, that can reconstruct the original value from the noisy values with high confidence. To mitigate these issues, in this paper, we propose the Staircase Randomized Response (SRR)-FL framework, which assigns higher probabilities to weights closer to the true weight, reducing the distance between the true and perturbed data. this minimizes the noise for maintaining the same LDP guarantee, leading to better utility. Compared to existing LDP mechanisms (e.g., Generalized Randomized Response) on the FL, SRR-FL can further provide a more accurate privacy-preserving training model, and enhance the robustness against the inference attack while ensuring the same LDP guarantee. Furthermore, we also use the parameter shuffling method for privacy amplification. the efficacy of SRR-FL has been validated on widely used datasets MNIST, Medical-MNIST and CIFAR-10, demonstrating remarkable performance.

关键词： Local differential privacy federated learning client-level LDP

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A Unified Time Series Analytics based Intrusion Detection Framework for CAN BUS Attacks 24

A Unified Time Series Analytics based Intrusion Detection Fr...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Maliha, Maisha Bhattacharjee, Shameek Western Michigan Univ Kalamazoo MI 49008 USA

ISBN: (纸本)9798400704215

Modern smart vehicles have a Controller Area Network (CAN) that supports intra-vehicle communication between intelligent Electronic Control Units (ECUs). the CAN is known to be vulnerable to various cyber attacks. In this paper, we propose a unified framework that can detect multiple types of cyber attacks (viz., Denial of Service, Fuzzy, Impersonation) affecting the CAN. Specifically, we construct a feature by observing the timing information of CAN packets exchanged over the CAN bus network over partitioned time windows to construct a low dimensional representation of the entire CAN network as a time series latent space. then, we apply a two tier anomaly based intrusion detection model that keeps track of short term and long term memory of deviations in the initial time series latent space, to create a 'stateful latent space'. then, we learn the boundaries of the benign stateful latent space that specify the attack detection criterion. To find hyper-parameters of our proposed model, we formulate a preference based multi-objective optimization problem that optimizes security objectives tailored for a network-wide time series anomaly based intrusion detector by balancing trade-offs between false alarm count, time to detection, and missed detection rate. We use real benign and attack datasets collected from a Kia Soul vehicle to validate our framework and show how our performance outperforms existing works.

关键词： anomaly detection intrusion detection vehicle CAN bus security

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DroidDefender: An Image-based Android Antimalware Proof-of-Concept 24

DroidDefender: An Image-based Android Antimalware Proof-of-C...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Ciaramella, Giovanni Martinelli, Fabio Mercaldo, Francesco Santone, Antonella IMT Sch Adv Studies Lucca Lucca Italy CNR IIT Lucca Italy CNR IIT Pisa Italy Univ Molise Campobasso Italy CNR IIT Campobasso Italy

ISBN: (纸本)9798400704215

Malware analysis researchers are currently focused on the design and development of innovative approaches to detect zero-day malware, with particular regard to mobile environments. It is widely recognized that existing free and commercial anti-malware tools struggle to detect unknown threats, operating within the constraints of the signature-based paradigm. Among the set of techniques frequently exploited by researchers for spotting malware, machine learning, with particular regard to deep learning, is quickly gaining prominence as a highly promising method for zero-day malware detection. Unfortunately, many of the proposed methods are not effectively implemented in research prototypes and, therefore, do not truly become usable by end users. For this reason, in this paper, we present the DroidDefender proof-of-concept, an Android antimalware specifically designed and developed to detect zero-day malware. DroidDefender is based on representing an Android application as an image and employs a deep learning model. the DroidDefender research prototype is freely available for research purposes at the following URL: https://***/ Services/***?lang=en.

关键词： malware security Android testing deep learning

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Crypto'Graph: Leveraging privacy-Preserving Distributed Link Prediction for Robust Graph Learning 24

Crypto'Graph: Leveraging Privacy-Preserving Distributed Link...

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14th acm conference on data and application security and privacy (acm codaspy)

作者： Azogagh, Sofiane Birba, Zelma Aubin Gambs, Sebastien Killijian, Marc-Olivier Univ Quebec Montreal Montreal PQ Canada

ISBN: (纸本)9798400704215

Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still be willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto'Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. through the use of cryptographic primitives, Crypto'Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private graph of each party, even though they know the number of nodes they have, since they share the same graph in terms of nodes but not the same links. Crypto'Graph improves on previous works by enabling the computation of a diverse set of similarity metrics in parallel without any additional cost. the use of Crypto'Graph is illustrated for defense against graph poisoning attacks, in which potential adversarial links are identified without compromising the privacy of the graphs of individual parties. the effectiveness of Crypto'Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a node classification task using graph neural networks is demonstrated through extensive experimentation on two real-world datasets.

关键词： security privacy secure multiparty computation graph

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