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MLHat: Deployable Machine Learning for security Defense 21

MLHat: Deployable Machine Learning for Security Defense

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27th acm SIGKDD International conference on Knowledge Discovery and data Mining (KDD)

作者： Wang, Gang Ciptadi, Arridhana Ahmadzadeh, Ali UIUC Champaign IL 61820 USA TruEra Redmond WA USA Blue Hexagon San Francisco CA USA

ISBN: (纸本)9781450383325

The MLHat workshop aims to bring together academic researchers and industry practitioners to discuss the open challenges, potential solutions, and best practices to deploy machine learning at scale for security defense. The workshop will discuss related topics from both defender perspectives (white-hat) and the attacker perspectives ( black-hat). We call the workshop MLHats, to serve as a place for people who are interested in using machine learning to solve practical security problems. The workshop will focus on defining new machine learning paradigms under various security application contexts and identifying exciting new future research directions. At the same time, the workshop will also have a strong industry presence to provide insights into the challenges in deploying and maintaining machine learning models and the much-needed discussion on the capabilities that the state-of-the-arts failed to provide.

关键词： security and privacy Deployable Machine Learning Adversarial Machine Learning

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Enabling data Interoperability for Decentralized, Smart, and Connected Health applications 23

Enabling Data Interoperability for Decentralized, Smart, and...

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proceedings of the 8th acm/IEEE International conference on Connected Health: applications, Systems and Engineering Technologies

作者： Oshani Seneviratne Rensselaer Polytechnic Institute Troy NY USA

ISBN: (纸本)9798400701023

The digitization of healthcare is increasing, providing a wealth of data that can improve patient care and outcomes. However, different systems and formats often silo this data, presenting a significant challenge to achieving a connected and interoperable healthcare ecosystem. data interoperability is crucial for realizing the full potential of smart and connected health, which can improve patient outcomes and reduce costs. In this paper, we describe the key concepts and technologies enabling interoperability, including health information exchange, interoperability standards, and application programming interfaces enabling connected health applications built on blockchains. We also discuss the challenges and barriers to achieving data interoperability, such as privacy and security concerns, organizational and cultural barriers, and technical limitations. Furthermore, we highlight the benefits of data interoperability in healthcare, such as better patient outcomes, improved population health management, reduced costs, and increased efficiency. Finally, we discuss the potential future of data interoperability in healthcare, including the role of emerging privacy-preserving technologies.

关键词： interoperability

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Probabilistic data Structures in the Wild: A security Analysis of Redis 25

Probabilistic Data Structures in the Wild: A Security Analys...

引用

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

作者： Mia Fili? Jonas Hofmann Sam A. Markelon Kenneth G. Paterson Anupama Unnikrishnan ETH Zurich Zurich Switzerland ETH Zurich Zurich Germany and Technische Universität Darmstadt Darmstadt Germany University of Florida Gainesville FL USA

ISBN: (纸本)9798400714764

Redis (Remote Dictionary Server) is a general purpose, in-memory database that supports a rich array of functionality, including various Probabilistic data Structures (PDS), such as Bloom filters, Cuckoo filters, as well as cardinality and frequency estimators. These PDS typically perform well in the average case. However, given that Redis is intended to be used across a diverse array of applications, it is crucial to evaluate how these PDS perform under worst-case scenarios, i.e., when faced with adversarial inputs. We offer a comprehensive analysis to address this question. We begin by carefully documenting the different PDS implementations in Redis, explaining how they deviate from those PDS as described in the literature. Then we show that these deviations enable a total of 10 novel attacks that are more severe than the corresponding attacks for generic versions of the PDS. We highlight the critical role of Redis' decision to use non-cryptographic hash functions in the severity of these attacks. We conclude by discussing countermeasures to the attacks

关键词： PDS attack countermeasures

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A dataset for Evaluating LLMs Vulnerability Repair Performance in Android applications: data/Toolset paper 25

A Dataset for Evaluating LLMs Vulnerability Repair Performan...

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

作者： Elisa Braconaro Eleonora Losiouk Universita degli Studi di Padova Padua Italy

ISBN: (纸本)9798400714764

Automated Program Repair (APR) is a well-established research area that enhances software reliability and security by automatically fixing bugs, reducing manual effort, and accelerating debugging. Despite progress in publishing benchmarks to evaluate APR tools, datasets specifically targeting Android are *** address this gap, we introduce a dataset of 272 real-world violations of Google's Android security Best Practices, identified by statically analyzing 113 real-world Android apps. In addition to the faulty code, we manually crafted repairs based on Google's guidelines, covering 176 Java-based and 96 XML-based violations from Android Java classes and Manifest files, respectively. Additionally, we leveraged our novel dataset to evaluate Large Language Models (LLMs) as they are the latest promising APR tools. In particular, we evaluated GPT-4o, Gemini 1.5 Flash and Gemini in Android Studio and we found that GPT-4o outperforms Google's models, demonstrating higher accuracy and robustness across a range of violations types. Hence, with this dataset, we aim to provide valuable insights for advancing APR research and improving tools for Android security.

关键词： android vulnerabilities

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Using Venom to Flip the Coin and Peel the Onion: Measurement Tool and dataset for Studying the Bitcoin - Dark Web Synergy: data/Toolset Paper 25

Using Venom to Flip the Coin and Peel the Onion: Measurement...

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

作者： Lukas Ingmarsson Karl Duckert Karlsson Niklas Carlsson Linköping University Linköping Sweden

ISBN: (纸本)9798400714764

Bitcoin and the Dark Web present an interesting synergy that enables both legitimate anonymity and illicit activities, making it an important landscape to understand, especially as the Dark Web, with its hidden services, relies heavily on Bitcoin as a pseudonymous currency for transactions. However, a lack of scalable tools and timely datasets has limited systematic analysis of this ecosystem. To address this gap, we introduce Venom, a scalable framework for mapping Bitcoin activity on the Dark Web. Venom integrates multithreaded crawling, data extraction, and dataset generation, resulting in a comprehensive resource that allows us to easily collect snapshots of over 177,000 onion sites in roughly 24 hours. With the paper, we share both the tool and an example snapshot containing both per-site metadata and Bitcoin transaction data. Preliminary analysis reveals concentrated activity among key players and widespread content mirroring, offering new insights into the Dark Web's economic structure. Venom provides a critical resource for advancing research and monitoring in this domain.

关键词： Bitcoin

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Padding Matters - Exploring Function Detection in PE Files: data/Toolset paper 25

Padding Matters - Exploring Function Detection in PE Files: ...

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

作者： Raphael Springer Alexander Schmitz Artur Leinweber Tobias Urban Christian Dietrich Institute for Internet Security Westphalian University of Applied Sciences Gelsenkirchen Germany

ISBN: (纸本)9798400714764

Function detection is a well-known problem in binary analysis. While prior work has focused on Linux/ELF, Windows/PE binaries have only partially been considered. This paper introduces FuncPEval, a dataset for Windows x86 and x64 PE files, featuring Chromium and the Conti ransomware, along with ground truth data for 1,092,820 function starts. Utilizing FuncPEval, we evaluate five heuristics-based (Ghidra, IDA, Nucleus, ***, SMDA) and three machine-learning-based (DeepDi, RNN, XDA) function start detection tools. Among these, IDA achieves the highest F1-score (98.44%) for Chromium x64, while DeepDi closely follows (97%) but stands out as the fastest. Towards explainability, we examine the impact of padding between functions on the detection results, finding all tested tools, except ***, are susceptible to randomized padding. The randomized padding significantly diminishes the effectiveness of the RNN, XDA, and Nucleus. Among the learning-based tools, DeepDi exhibits the least sensitivity, while Nucleus is the most adversely affected among the non-learning-based tools.

关键词： binary code analysis

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GuardiaNN: Fast and Secure On-Device Inference in TrustZone Using Embedded SRAM and Cryptographic Hardware 22

GuardiaNN: Fast and Secure On-Device Inference in TrustZone ...

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23rd acm/IFIP International Middleware conference (Middleware)

作者： Choi, Jinwoo Kim, Jaeyeon Lim, Chaemin Lee, Suhyun Lee, Jinho Song, Dokyung Kim, Youngsok Yonsei Univ Seoul South Korea

ISBN: (纸本)9781450393409

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent studies execute part of a DNN in a trusted execution environment (e.g., TrustZone) to isolate DNN execution from the other processes;however, as the trusted execution environments for mobile/embedded devices provide limited memory protection, DNN data remain unencrypted in DRAM and become vulnerable to physical attacks. The devices can prevent the physical attacks by keeping DNN data encrypted in DRAM;when DNN data get referenced during DNN execution, they get loaded to the SRAM and get decrypted by a CPU core. Unfortunately, using the SRAM with demand paging greatly increasesDNN execution time due to the inefficient use of the SRAM and the high CPU consumption of data encryption/decryption. In this paper, we present GuardiaNN, a fast and secure DNN framework which greatly accelerates DNN execution without sacrificing security guarantees. To accelerate secure DNN execution, GuardiaNN first reduces slow DRAM accesses with direct convolutions and maximizes the reuse of SRAM-stored data with DNNfriendly SRAM management. Then, aimed at dedicating the limited CPU resources to DNN execution, GuardiaNN offloads DNN data encryption/decryption onto secure cryptographic hardware and employs pipelining to overlap DNN execution with the encryption/decryption. For eight DNNs chosen from five representative mobile/embedded application domains, our implementation of GuardiaNN on STM32MP157C-DK2 development board achieves a geomean speedup of 15.3x and a geomean energy efficiency improvement of 15.2x over a baseline secure DNN framework which employs demand-paged SRAM to secure sensitive data.

关键词： on-device inference security ARM TrustZone DRAM encryption embedded SRAM cryptographic hardware

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CCIOT 2022 - 2022 7th International conference on Cloud Computing and Internet of Things

CCIOT 2022 - 2022 7th International Conference on Cloud Comp...

引用

7th International conference on Cloud Computing and Internet of Things, CCIOT 2022

ISBN: (纸本)9781450396738

The proceedings contain 11 papers. The topics discussed include: fast multi-label tumor classification based on homomorphic encryption;SecMesh: an efficient information security method for stream processing in edge-fog-cloud;a network-elastic scalable blockchain for privacy-preserving federated learning in cloud-edge collaboration industrial Internet of Things;SSL VPN over TCP and UDP tunnels: performance evaluation with different server-side congestion control;research on improved DV-hop algorithm based on multiple communication radius and hop distance correction;decentralized smart city of things: a blockchain tokenization-enabled architecture for digitization and authentication of assets in smart cities;software development and design of a cybersecurity system based on big data analysis technology;and research on blended teaching mode under the background of ‘Internet Plus’: application based on chaoxing learning pass platform.

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An Intelligent Doorbell Design Using Federated Deep Learning 21

An Intelligent Doorbell Design Using Federated Deep Learning

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3rd acm India Joint International conference on data Science and Management of data (CODS-COMAD)

作者： Patel, Vatsal Kanani, Sarth Pathak, Tapan Patel, Pankesh Ali, Muhammad Intizar Breslin, John Pandit Deendayal Petr Univ Gandhinagar Gujarat India NUI Galway Data Sci Inst Confirm SFI Res Ctr Smart Mfg Galway Ireland

ISBN: (纸本)9781450388177

Smart doorbells have been playing an important role in protecting our modern homes. Existing approaches of sending video streams to a centralized server (or Cloud) for video analytics have been facing many challenges such as latency, bandwidth cost and more importantly users' privacy concerns. To address these challenges, this paper showcases the ability of an intelligent smart doorbell based on Federated Deep Learning, which can deploy and manage video analytics applications such as a smart doorbell across Edge and Cloud resources. This platform can scale, work with multiple devices, seamlessly manage online orchestration of the application components. The proposed framework is implemented using state-of-the-art technology. We implement the Federated Server using the Flask framework, containerized using Nginx and Gunicorn, which is deployed on AWS EC2 and AWS Serverless architecture.

关键词： Federated Learning Internet of Things Video Analytics Artificial Intelligence Deep Learning Machine Learning privacy security

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OAuth 2.0-based authentication solution for FPGA-enabled cloud computing 14

OAuth 2.0-based authentication solution for FPGA-enabled clo...

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IEEE/acm 14th International conference on Utility and Cloud Computing

作者： Ince, Semih Espes, David Gogniat, Guy Lallet, Julien Santoro, Renaud Nokia Bell Labs Murray Hill NJ 07974 USA Univ Bretagne Occidentale Brest France Univ Bretagne Sud Lorient France

ISBN: (纸本)9781450391634

FPGA-enabled cloud computing is getting more and more common as cloud providers offer hardware accelerated solutions. In this context, clients need confidential remote computing. However Intellectual Properties and data are being used and communicated. So current security models require the client to trust the cloud provider blindly by disclosing sensitive information. In addition, the lack of strong authentication and access control mechanisms, for both the client and the provided FPGA in current solutions, is a major security drawback. To enhance security measures and privacy between the client, the cloud provider and the FPGA, an additional entity needs to be introduced: the trusted authority. Its role is to authenticate the client-FPGA pair and isolate them from the cloud provider. With our novel OAuth 2.0-based access delegation solution for FPGA-accelerated clouds, a remote confidential FPGA environment with a token-based access can be created for the client. Our solution allows to manage and securely allocate heterogeneous resource pools with enhanced privacy & confidentiality for the client. Our formal analysis shows that our protocol adds a very small latency which is suitable for real-time application.

关键词： Cloud FPGA security authentication OAuth

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