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共 560 条记录，以下是161-170 订阅

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

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

引用

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 ...

引用

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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SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform 21

SteemOps: Extracting and Analyzing Key Operations in Steemit...

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11th acm conference on data and application security and privacy (CODASPY)

作者： Li, Chao Palanisamy, Balaji Xu, Runhua Xu, Jinlai Wang, Jingzhe Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA USA IBM Almaden Res Ctr San Jose CA USA

ISBN: (纸本)9781450381437

Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.

关键词： blockchain social network Steem dataset

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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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Defining security Limits in Biometrics 25

Defining Security Limits in Biometrics

引用

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

作者： Axel Durbet Paul-Marie Grollemund Pascal Lafourcade Kevin Thiry-Atighehchi CNRS Clermont Auvergne INP Mines Saint-Étienne LIMOS Université Clermont Auvergne Clermont-Ferrand France CNRS Clermont Auvergne INP Mines Saint-Étienne LMBP Université Clermont Auvergne Clermont-Ferrand France

ISBN: (纸本)9798400714764

Biometric systems are widely used for authentication and identification. The False Match Rate (FMR) quantifies the probability of matching a biometric template to a non-corresponding template and serves as an indicator of the system robustness against security threats. We analyze biometric systems through two main contributions. First, we study untargeted attacks, where an adversary aims to impersonate any user in the database. We compute the number of trials needed for a successful impersonation and derive the critical population size ( i.e., the maximum database size) and critical (FMR) required to maintain security against untargeted attacks as the database grows. Second, we address the biometric birthday problem, which quantifies the probability that there exists two distinct users that collide ( i.e., can impersonate each other). We compute approximate and exact probabilities of collision and derive the associated critical population size and critical (FMR) to bound the risk of biometric collisions, particularly in large-scale databases. These thresholds provide actionable insights for designing biometric systems that mitigate the risks of impersonation and biometric collisions, particularly in large-scale databases. Nevertheless, our findings show that current systems fail to meet the required security level against untargeted attacks, even in small databases, and face significant challenges with the biometric birthday problem as databases grow.

关键词： biometric security

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Measurable and Deployable security: Gaps, Successes, and Opportunities 21

Measurable and Deployable Security: Gaps, Successes, and Opp...

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11th acm conference on data and application security and privacy (CODASPY)

作者： Yao, Danfeng (Daphne) Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)9781450381437

security measurement helps identify deployment gaps and present extremely valuable research opportunities. However, such research is often deemed as not novelty by academia. I will first share my research journey designing and producing a high precision tool CryptoGuard for scanning cryptographic vulnerabilities in large Java projects. That work led us to publish two benchmarks used for systematically assessing state-of-the-art academic and commercial solutions, as well as help Oracle Labs integrate our detection in their routine scanning. Other specific measurement and deployment cases to discuss include the Payment Card Industry data security Standard, which was involved in high-profile data breach incidents, and fine-grained Address Space Layout Randomization (ASLR). The talk will also point out the need for measurement in AI development in the context of code repair. Broadening research styles by accepting and encouraging deployment-related work will facilitate our field to progress towards maturity.

关键词： Measurement deployment security practice precision benchmark software security code scanning static program analysis cryptography implementation CryptoGuard payment card industry PCI web security address space layout randomization ASLR code repair AI machine learning

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Protecting DeFi Platforms against Non-Price Flash Loan Attacks 25

Protecting DeFi Platforms against Non-Price Flash Loan Attac...

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

作者： Abdulrahman Alhaidari Balaji Palanisamy Prashant Krishnamurthy Informatics and Networked Systems University of Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9798400714764

Smart contracts in Decentralized Finance (DeFi) platforms are attractive targets for attacks as their vulnerabilities can lead to massive amounts of financial losses. Flash loan attacks, in particular, pose a major threat to DeFi protocols that hold a Total Value Locked (TVL) exceeding 106 billion. These attacks use the atomicity property of blockchains to drain funds from smart contracts in a single transaction. While existing research primarily focuses on price manipulation attacks, such as oracle manipulation, mitigating non-price flash loan attacks that often exploit smart contracts' zero-day vulnerabilities remains largely unaddressed. These attacks are challenging to detect because of their unique patterns, time sensitivity, and complexity. In this paper, we present FlashGuard, a runtime detection and mitigation method for non-price flash loan attacks. Our approach targets smart contract function signatures to identify attacks in real-time and counterattack by disrupting the attack transaction atomicity by leveraging the short window when transactions are visible in the mempool but not yet confirmed. When FlashGuard detects an attack, it dispatches a stealthy dusting counterattack transaction to miners to change the victim contract's state which disrupts the attack's atomicity and forces the attack transaction to revert. We evaluate our approach using 20 historical attacks and several unseen attacks. FlashGuard achieves an average real-time detection latency of 150.31ms, a detection accuracy of over 99.93%, and an average disruption time of 410.92ms. FlashGuard could have potentially rescued over \405.71 million in losses if it were deployed prior to these attack instances. FlashGuard demonstrates significant potential as a DeFi security solution to mitigate and handle rising threats of non-price flash loan attacks.

关键词： DeFi security

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Poster: A privacy-Preserving Smart Contract Vulnerability Detection Framework for Permissioned Blockchain 23

Poster: A Privacy-Preserving Smart Contract Vulnerability De...

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proceedings of the 2023 acm SIGSAC conference on Computer and Communications security

作者： Wensheng Tian Lei Zhang Shuangxi Chen Hu Wang Xiao Luo Nanhu Lab Jiaxing China Jiaxing Vocational and Technical College Jiaxing China Zhejiang Big Data Development Administration Hangzhou China Zhejiang University Hangzhou China

ISBN: (纸本)9798400700507

The two main types of blockchains that are currently widely deployed are public blockchains and permissioned blockchains. The research that has been conducted for blockchain vulnerability detection is mainly oriented to public blockchains. Less consideration is given to the unique requirements of the permissioned blockchains, which cannot be directly migrated to the application scenarios of the permissioned blockchains. The permissioned blockchain is deployed between verified organizations, and its smart contracts may contain sensitive information such as the transaction flow of the contracts, transaction algorithms, etc. The sensitive information can be considered as the private information of the smart contracts themselves, which should be kept confidential to users outside the blockchain. In this paper, a privacy-preserving smart contract vulnerability detection framework is proposed. The framework leverages blockchain and confidential computing technologies to enable vulnerability detection in permissioned blockchain smart contracts while protecting the privacy of smart contracts. The framework is also able to protect the interests of vulnerability detection model owners. We experimentally validate the detection performance of our framework in a confidential computing environment.

关键词： blockchain privacy-preserving smart contract vulnerability detection

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