arXiv - CS - Cryptography and Security最新文献_第3页

Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments Hackphyr：用于网络安全环境的本地微调 LLM 代理

arXiv - CS - Cryptography and Security Pub Date : 2024-09-17 DOI: arxiv-2409.11276

Maria Rigaki, Carlos Catania, Sebastian Garcia

引用次数: 0

Enhancing Security Testing Software for Systems that Cannot be Subjected to the Risks of Penetration Testing Through the Incorporation of Multi-threading and and Other Capabilities 通过纳入多线程和其他功能，为无法承受渗透测试风险的系统增强安全测试软件

arXiv - CS - Cryptography and Security Pub Date : 2024-09-17 DOI: arxiv-2409.10893

Matthew Tassava, Cameron Kolodjski, Jordan Milbrath, Jeremy Straub

引用次数: 0

Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach 新型恶意数据包识别：少量学习方法

arXiv - CS - Cryptography and Security Pub Date : 2024-09-17 DOI: arxiv-2409.11254

Kyle Stein, Andrew A. Mahyari, Guillermo Francia III, Eman El-Sheikh

{"title":"Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach","authors":"Kyle Stein, Andrew A. Mahyari, Guillermo Francia III, Eman El-Sheikh","doi":"arxiv-2409.11254","DOIUrl":"https://doi.org/arxiv-2409.11254","url":null,"abstract":"As the complexity and connectivity of networks increase, the need for novel\u0000malware detection approaches becomes imperative. Traditional security defenses\u0000are becoming less effective against the advanced tactics of today's\u0000cyberattacks. Deep Packet Inspection (DPI) has emerged as a key technology in\u0000strengthening network security, offering detailed analysis of network traffic\u0000that goes beyond simple metadata analysis. DPI examines not only the packet\u0000headers but also the payload content within, offering a thorough insight into\u0000the data traversing the network. This study proposes a novel approach that\u0000leverages a large language model (LLM) and few-shot learning to accurately\u0000recognizes novel, unseen malware types with few labels samples. Our proposed\u0000approach uses a pretrained LLM on known malware types to extract the embeddings\u0000from packets. The embeddings are then used alongside few labeled samples of an\u0000unseen malware type. This technique is designed to acclimate the model to\u0000different malware representations, further enabling it to generate robust\u0000embeddings for each trained and unseen classes. Following the extraction of\u0000embeddings from the LLM, few-shot learning is utilized to enhance performance\u0000with minimal labeled data. Our evaluation, which utilized two renowned\u0000datasets, focused on identifying malware types within network traffic and\u0000Internet of Things (IoT) environments. Our approach shows promising results\u0000with an average accuracy of 86.35% and F1-Score of 86.40% on different malware\u0000types across the two datasets.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes 抖动假视频通过主动探测实时检测深度伪造视频

arXiv - CS - Cryptography and Security Pub Date : 2024-09-17 DOI: arxiv-2409.10889

Zhixin Xie, Jun Luo

{"title":"Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes","authors":"Zhixin Xie, Jun Luo","doi":"arxiv-2409.10889","DOIUrl":"https://doi.org/arxiv-2409.10889","url":null,"abstract":"Real-time deepfake, a type of generative AI, is capable of \"creating\"\u0000non-existing contents (e.g., swapping one's face with another) in a video. It\u0000has been, very unfortunately, misused to produce deepfake videos (during web\u0000conferences, video calls, and identity authentication) for malicious purposes,\u0000including financial scams and political misinformation. Deepfake detection, as\u0000the countermeasure against deepfake, has attracted considerable attention from\u0000the academic community, yet existing works typically rely on learning passive\u0000features that may perform poorly beyond seen datasets. In this paper, we\u0000propose SFake, a new real-time deepfake detection method that innovatively\u0000exploits deepfake models' inability to adapt to physical interference.\u0000Specifically, SFake actively sends probes to trigger mechanical vibrations on\u0000the smartphone, resulting in the controllable feature on the footage.\u0000Consequently, SFake determines whether the face is swapped by deepfake based on\u0000the consistency of the facial area with the probe pattern. We implement SFake,\u0000evaluate its effectiveness on a self-built dataset, and compare it with six\u0000other detection methods. The results show that SFake outperforms other\u0000detection methods with higher detection accuracy, faster process speed, and\u0000lower memory consumption.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prompt Obfuscation for Large Language Models 大型语言模型的提示混淆

arXiv - CS - Cryptography and Security Pub Date : 2024-09-17 DOI: arxiv-2409.11026

David Pape, Thorsten Eisenhofer, Lea Schönherr

{"title":"Prompt Obfuscation for Large Language Models","authors":"David Pape, Thorsten Eisenhofer, Lea Schönherr","doi":"arxiv-2409.11026","DOIUrl":"https://doi.org/arxiv-2409.11026","url":null,"abstract":"System prompts that include detailed instructions to describe the task\u0000performed by the underlying large language model (LLM) can easily transform\u0000foundation models into tools and services with minimal overhead. Because of\u0000their crucial impact on the utility, they are often considered intellectual\u0000property, similar to the code of a software product. However, extracting system\u0000prompts is easily possible by using prompt injection. As of today, there is no\u0000effective countermeasure to prevent the stealing of system prompts and all\u0000safeguarding efforts could be evaded with carefully crafted prompt injections\u0000that bypass all protection mechanisms.In this work, we propose an alternative\u0000to conventional system prompts. We introduce prompt obfuscation to prevent the\u0000extraction of the system prompt while maintaining the utility of the system\u0000itself with only little overhead. The core idea is to find a representation of\u0000the original system prompt that leads to the same functionality, while the\u0000obfuscated system prompt does not contain any information that allows\u0000conclusions to be drawn about the original system prompt. We implement an\u0000optimization-based method to find an obfuscated prompt representation while\u0000maintaining the functionality. To evaluate our approach, we investigate eight\u0000different metrics to compare the performance of a system using the original and\u0000the obfuscated system prompts, and we show that the obfuscated version is\u0000constantly on par with the original one. We further perform three different\u0000deobfuscation attacks and show that with access to the obfuscated prompt and\u0000the LLM itself, we are not able to consistently extract meaningful information.\u0000Overall, we showed that prompt obfuscation can be an effective method to\u0000protect intellectual property while maintaining the same utility as the\u0000original system prompt.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Execution-time opacity control for timed automata 定时自动机的执行时间不透明控制

arXiv - CS - Cryptography and Security Pub Date : 2024-09-16 DOI: arxiv-2409.10336

Étienne André, Marie Duflot, Laetitia Laversa, Engel Lefaucheux

引用次数: 0

Assessing the Impact of Sanctions in the Crypto Ecosystem: Effective Measures or Ineffective Deterrents? 评估制裁对加密货币生态系统的影响：有效措施还是无效威慑？

arXiv - CS - Cryptography and Security Pub Date : 2024-09-16 DOI: arxiv-2409.10031

Francesco Zola, Jon Ander Medina, Raul Orduna

{"title":"Assessing the Impact of Sanctions in the Crypto Ecosystem: Effective Measures or Ineffective Deterrents?","authors":"Francesco Zola, Jon Ander Medina, Raul Orduna","doi":"arxiv-2409.10031","DOIUrl":"https://doi.org/arxiv-2409.10031","url":null,"abstract":"Regulatory authorities aim to tackle illegal activities by targeting the\u0000economic incentives that drive such behaviour. This is typically achieved\u0000through the implementation of financial sanctions against the entities involved\u0000in the crimes. However, the rise of cryptocurrencies has presented new\u0000challenges, allowing entities to evade these sanctions and continue criminal\u0000operations. Consequently, enforcement measures have been expanded to include\u0000crypto assets information of sanctioned entities. Yet, due to the nature of the\u0000crypto ecosystem, blocking or freezing these digital assets is harder and, in\u0000some cases, such as with Bitcoin, unfeasible. Therefore, sanctions serve merely\u0000as deterrents. For this reason, in this study, we aim to assess the impact of\u0000these sanctions on entities' crypto activities, particularly those related to\u0000the Bitcoin ecosystem. Our objective is to shed light on the validity and\u0000effectiveness (or lack thereof) of such countermeasures. Specifically, we\u0000analyse the transactions and the amount of USD moved by punished entities that\u0000possess crypto addresses after being sanctioned by the authority agency.\u0000Results indicate that while sanctions have been effective for half of the\u0000examined entities, the others continue to move funds through sanctioned\u0000addresses. Furthermore, punished entities demonstrate a preference for\u0000utilising rapid exchange services to convert their funds, rather than employing\u0000dedicated money laundering services. To the best of our knowledge, this study\u0000offers valuable insights into how entities use crypto assets to circumvent\u0000sanctions.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble CaBaGe：使用 ClAss BAlanced Generator Ensemble 进行无数据模型提取

arXiv - CS - Cryptography and Security Pub Date : 2024-09-16 DOI: arxiv-2409.10643

Jonathan Rosenthal, Shanchao Liang, Kevin Zhang, Lin Tan

{"title":"CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble","authors":"Jonathan Rosenthal, Shanchao Liang, Kevin Zhang, Lin Tan","doi":"arxiv-2409.10643","DOIUrl":"https://doi.org/arxiv-2409.10643","url":null,"abstract":"Machine Learning as a Service (MLaaS) is often provided as a pay-per-query,\u0000black-box system to clients. Such a black-box approach not only hinders open\u0000replication, validation, and interpretation of model results, but also makes it\u0000harder for white-hat researchers to identify vulnerabilities in the MLaaS\u0000systems. Model extraction is a promising technique to address these challenges\u0000by reverse-engineering black-box models. Since training data is typically\u0000unavailable for MLaaS models, this paper focuses on the realistic version of\u0000it: data-free model extraction. We propose a data-free model extraction\u0000approach, CaBaGe, to achieve higher model extraction accuracy with a small\u0000number of queries. Our innovations include (1) a novel experience replay for\u0000focusing on difficult training samples; (2) an ensemble of generators for\u0000steadily producing diverse synthetic data; and (3) a selective filtering\u0000process for querying the victim model with harder, more balanced samples. In\u0000addition, we create a more realistic setting, for the first time, where the\u0000attacker has no knowledge of the number of classes in the victim training data,\u0000and create a solution to learn the number of classes on the fly. Our evaluation\u0000shows that CaBaGe outperforms existing techniques on seven datasets -- MNIST,\u0000FMNIST, SVHN, CIFAR-10, CIFAR-100, ImageNet-subset, and Tiny ImageNet -- with\u0000an accuracy improvement of the extracted models by up to 43.13%. Furthermore,\u0000the number of queries required to extract a clone model matching the final\u0000accuracy of prior work is reduced by up to 75.7%.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FreeMark: A Non-Invasive White-Box Watermarking for Deep Neural Networks FreeMark：用于深度神经网络的非侵入式白盒水印技术

arXiv - CS - Cryptography and Security Pub Date : 2024-09-16 DOI: arxiv-2409.09996

Yuzhang Chen, Jiangnan Zhu, Yujie Gu, Minoru Kuribayashi, Kouichi Sakurai

引用次数: 0

PrePaMS: Privacy-Preserving Participant Management System for Studies with Rewards and Prerequisites PrePaMS：用于有奖励和先决条件研究的保护隐私的参与者管理系统

arXiv - CS - Cryptography and Security Pub Date : 2024-09-16 DOI: arxiv-2409.10192

Echo Meißner, Frank Kargl, Benjamin Erb, Felix Engelmann

{"title":"PrePaMS: Privacy-Preserving Participant Management System for Studies with Rewards and Prerequisites","authors":"Echo Meißner, Frank Kargl, Benjamin Erb, Felix Engelmann","doi":"arxiv-2409.10192","DOIUrl":"https://doi.org/arxiv-2409.10192","url":null,"abstract":"Taking part in surveys, experiments, and studies is often compensated by\u0000rewards to increase the number of participants and encourage attendance. While\u0000privacy requirements are usually considered for participation, privacy aspects\u0000of the reward procedure are mostly ignored. To this end, we introduce PrePaMS,\u0000an efficient participation management system that supports prerequisite checks\u0000and participation rewards in a privacy-preserving way. Our system organizes\u0000participations with potential (dis-)qualifying dependencies and enables secure\u0000reward payoffs. By leveraging a set of proven cryptographic primitives and\u0000mechanisms such as anonymous credentials and zero-knowledge proofs,\u0000participations are protected so that service providers and organizers cannot\u0000derive the identity of participants even within the reward process. In this\u0000paper, we have designed and implemented a prototype of PrePaMS to show its\u0000effectiveness and evaluated its performance under realistic workloads. PrePaMS\u0000covers the information whether subjects have participated in surveys,\u0000experiments, or studies. When combined with other secure solutions for the\u0000actual data collection within these events, PrePaMS can represent a cornerstone\u0000for more privacy-preserving empirical research.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0