arXiv - CS - Cryptography and Security最新文献

A Survey-Based Quantitative Analysis of Stress Factors and Their Impacts Among Cybersecurity Professionals 基于调查的网络安全专业人员压力因素及其影响的定量分析

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.12047

Sunil Arora, John D. Hastings

{"title":"A Survey-Based Quantitative Analysis of Stress Factors and Their Impacts Among Cybersecurity Professionals","authors":"Sunil Arora, John D. Hastings","doi":"arxiv-2409.12047","DOIUrl":"https://doi.org/arxiv-2409.12047","url":null,"abstract":"This study investigates the prevalence and underlying causes of work-related\u0000stress and burnout among cybersecurity professionals using a quantitative\u0000survey approach guided by the Job Demands-Resources model. Analysis of\u0000responses from 50 cybersecurity practitioners reveals an alarming reality: 44%\u0000report experiencing severe work-related stress and burnout, while an additional\u000028% are uncertain about their condition. The demanding nature of cybersecurity\u0000roles, unrealistic expectations, and unsupportive organizational cultures\u0000emerge as primary factors fueling this crisis. Notably, 66% of respondents\u0000perceive cybersecurity jobs as more stressful than other IT positions, with 84%\u0000facing additional challenges due to the pandemic and recent high-profile\u0000breaches. The study finds that most cybersecurity experts are reluctant to\u0000report their struggles to management, perpetuating a cycle of silence and\u0000neglect. To address this critical issue, the paper recommends that\u0000organizations foster supportive work environments, implement mindfulness\u0000programs, and address systemic challenges. By prioritizing the mental health of\u0000cybersecurity professionals, organizations can cultivate a more resilient and\u0000effective workforce to protect against an ever-evolving threat landscape.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261625","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

Combating Phone Scams with LLM-based Detection: Where Do We Stand? 利用基于 LLM 的检测技术打击电话诈骗：我们的现状如何？

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.11643

Zitong Shen, Kangzhong Wang, Youqian Zhang, Grace Ngai, Eugene Y. Fu

引用次数: 0

Artemis: Efficient Commit-and-Prove SNARKs for zkML Artemis：针对 zkML 的高效承诺与证明 SNARKs

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.12055

Hidde Lycklama, Alexander Viand, Nikolay Avramov, Nicolas Küchler, Anwar Hithnawi

{"title":"Artemis: Efficient Commit-and-Prove SNARKs for zkML","authors":"Hidde Lycklama, Alexander Viand, Nikolay Avramov, Nicolas Küchler, Anwar Hithnawi","doi":"arxiv-2409.12055","DOIUrl":"https://doi.org/arxiv-2409.12055","url":null,"abstract":"The widespread adoption of machine learning (ML) in various critical\u0000applications, from healthcare to autonomous systems, has raised significant\u0000concerns about privacy, accountability, and trustworthiness. To address these\u0000concerns, recent research has focused on developing zero-knowledge machine\u0000learning (zkML) techniques that enable the verification of various aspects of\u0000ML models without revealing sensitive information. Recent advances in zkML have\u0000substantially improved efficiency; however, these efforts have primarily\u0000optimized the process of proving ML computations correct, often overlooking the\u0000substantial overhead associated with verifying the necessary commitments to the\u0000model and data. To address this gap, this paper introduces two new\u0000Commit-and-Prove SNARK (CP-SNARK) constructions (Apollo and Artemis) that\u0000effectively address the emerging challenge of commitment verification in zkML\u0000pipelines. Apollo operates on KZG commitments and requires white-box use of the\u0000underlying proof system, whereas Artemis is compatible with any homomorphic\u0000polynomial commitment and only makes black-box use of the proof system. As a\u0000result, Artemis is compatible with state-of-the-art proof systems without\u0000trusted setup. We present the first implementation of these CP-SNARKs, evaluate\u0000their performance on a diverse set of ML models, and show substantial\u0000improvements over existing methods, achieving significant reductions in prover\u0000costs and maintaining efficiency even for large-scale models. For example, for\u0000the VGG model, we reduce the overhead associated with commitment checks from\u000011.5x to 1.2x. Our results suggest that these contributions can move zkML\u0000towards practical deployment, particularly in scenarios involving large and\u0000complex ML models.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261624","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

Empowering Visual Artists with Tokenized Digital Assets with NFTs 利用代币化数字资产和 NFT 增强视觉艺术家的能力

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.11790

Ruiqiang Li, Brian Yecies, Qin Wang, Shiping Chen, Jun Shen

{"title":"Empowering Visual Artists with Tokenized Digital Assets with NFTs","authors":"Ruiqiang Li, Brian Yecies, Qin Wang, Shiping Chen, Jun Shen","doi":"arxiv-2409.11790","DOIUrl":"https://doi.org/arxiv-2409.11790","url":null,"abstract":"The Non-Fungible Tokens (NFTs) has the transformative impact on the visual\u0000arts industry by examining the nexus between empowering art practices and\u0000leveraging blockchain technology. First, we establish the context for this\u0000study by introducing some basic but critical technological aspects and\u0000affordances of the blockchain domain. Second, we revisit the creative practices\u0000involved in producing traditional artwork, covering various types, production\u0000processes, trading, and monetization methods. Third, we introduce and define\u0000the key fundamentals of the blockchain ecosystem, including its structure,\u0000consensus algorithms, smart contracts, and digital wallets. Fourth, we narrow\u0000the focus to NFTs, detailing their history, mechanics, lifecycle, and\u0000standards, as well as their application in the art world. In particular, we\u0000outline the key processes for minting and trading NFTs in various marketplaces\u0000and discuss the relevant market dynamics and pricing. We also consider major\u0000security concerns, such as wash trading, to underscore some of the central\u0000cybersecurity issues facing this domain. Finally, we conclude by considering\u0000future research directions, emphasizing improvements in user experience,\u0000security, and privacy. Through this innovative research overview, which\u0000includes input from creative industry and cybersecurity sdomain expertise, we\u0000offer some new insights into how NFTs can empower visual artists and reshape\u0000the wider copyright industries.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261628","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

Hard-Label Cryptanalytic Extraction of Neural Network Models 神经网络模型的硬标签密码分析提取

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.11646

Yi Chen, Xiaoyang Dong, Jian Guo, Yantian Shen, Anyu Wang, Xiaoyun Wang

引用次数: 0

What to Consider When Considering Differential Privacy for Policy 考虑政策差异化隐私时应考虑的因素

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.11680

Priyanka Nanayakkara, Jessica Hullman

引用次数: 0

GReDP: A More Robust Approach for Differential Privacy Training with Gradient-Preserving Noise Reduction GReDP：更稳健的梯度保护降噪差分隐私训练方法

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.11663

Haodi Wang, Tangyu Jiang, Yu Guo, Xiaohua Jia, Chengjun Cai

{"title":"GReDP: A More Robust Approach for Differential Privacy Training with Gradient-Preserving Noise Reduction","authors":"Haodi Wang, Tangyu Jiang, Yu Guo, Xiaohua Jia, Chengjun Cai","doi":"arxiv-2409.11663","DOIUrl":"https://doi.org/arxiv-2409.11663","url":null,"abstract":"Deep learning models have been extensively adopted in various regions due to\u0000their ability to represent hierarchical features, which highly rely on the\u0000training set and procedures. Thus, protecting the training process and deep\u0000learning algorithms is paramount in privacy preservation. Although Differential\u0000Privacy (DP) as a powerful cryptographic primitive has achieved satisfying\u0000results in deep learning training, the existing schemes still fall short in\u0000preserving model utility, i.e., they either invoke a high noise scale or\u0000inevitably harm the original gradients. To address the above issues, in this\u0000paper, we present a more robust approach for DP training called GReDP.\u0000Specifically, we compute the model gradients in the frequency domain and adopt\u0000a new approach to reduce the noise level. Unlike the previous work, our GReDP\u0000only requires half of the noise scale compared to DPSGD [1] while keeping all\u0000the gradient information intact. We present a detailed analysis of our method\u0000both theoretically and empirically. The experimental results show that our\u0000GReDP works consistently better than the baselines on all models and training\u0000settings.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261629","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

Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition 放松 DARTS：放宽眼动识别的可微分架构搜索限制

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.11652

Hongyu Zhu, Xin Jin, Hongchao Liao, Yan Xiang, Mounim A. El-Yacoubi, Huafeng Qin

{"title":"Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition","authors":"Hongyu Zhu, Xin Jin, Hongchao Liao, Yan Xiang, Mounim A. El-Yacoubi, Huafeng Qin","doi":"arxiv-2409.11652","DOIUrl":"https://doi.org/arxiv-2409.11652","url":null,"abstract":"Eye movement biometrics is a secure and innovative identification method.\u0000Deep learning methods have shown good performance, but their network\u0000architecture relies on manual design and combined priori knowledge. To address\u0000these issues, we introduce automated network search (NAS) algorithms to the\u0000field of eye movement recognition and present Relax DARTS, which is an\u0000improvement of the Differentiable Architecture Search (DARTS) to realize more\u0000efficient network search and training. The key idea is to circumvent the issue\u0000of weight sharing by independently training the architecture parameters\u0000$alpha$ to achieve a more precise target architecture. Moreover, the\u0000introduction of module input weights $beta$ allows cells the flexibility to\u0000select inputs, to alleviate the overfitting phenomenon and improve the model\u0000performance. Results on four public databases demonstrate that the Relax DARTS\u0000achieves state-of-the-art recognition performance. Notably, Relax DARTS\u0000exhibits adaptability to other multi-feature temporal classification tasks.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261634","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

PAD-FT: A Lightweight Defense for Backdoor Attacks via Data Purification and Fine-Tuning PAD-FT：通过数据净化和微调实现对后门攻击的轻量级防御

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.12072

Yukai Xu, Yujie Gu, Kouichi Sakurai

{"title":"PAD-FT: A Lightweight Defense for Backdoor Attacks via Data Purification and Fine-Tuning","authors":"Yukai Xu, Yujie Gu, Kouichi Sakurai","doi":"arxiv-2409.12072","DOIUrl":"https://doi.org/arxiv-2409.12072","url":null,"abstract":"Backdoor attacks pose a significant threat to deep neural networks,\u0000particularly as recent advancements have led to increasingly subtle\u0000implantation, making the defense more challenging. Existing defense mechanisms\u0000typically rely on an additional clean dataset as a standard reference and\u0000involve retraining an auxiliary model or fine-tuning the entire victim model.\u0000However, these approaches are often computationally expensive and not always\u0000feasible in practical applications. In this paper, we propose a novel and\u0000lightweight defense mechanism, termed PAD-FT, that does not require an\u0000additional clean dataset and fine-tunes only a very small part of the model to\u0000disinfect the victim model. To achieve this, our approach first introduces a\u0000simple data purification process to identify and select the most-likely clean\u0000data from the poisoned training dataset. The self-purified clean dataset is\u0000then used for activation clipping and fine-tuning only the last classification\u0000layer of the victim model. By integrating data purification, activation\u0000clipping, and classifier fine-tuning, our mechanism PAD-FT demonstrates\u0000superior effectiveness across multiple backdoor attack methods and datasets, as\u0000confirmed through extensive experimental evaluation.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261623","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

Log2graphs: An Unsupervised Framework for Log Anomaly Detection with Efficient Feature Extraction Log2graphs：利用高效特征提取进行日志异常检测的无监督框架

arXiv - CS - Cryptography and Security Pub Date : 2024-09-18 DOI: arxiv-2409.11890

Caihong Wang, Du Xu, Zonghang Li

{"title":"Log2graphs: An Unsupervised Framework for Log Anomaly Detection with Efficient Feature Extraction","authors":"Caihong Wang, Du Xu, Zonghang Li","doi":"arxiv-2409.11890","DOIUrl":"https://doi.org/arxiv-2409.11890","url":null,"abstract":"In the era of rapid Internet development, log data has become indispensable\u0000for recording the operations of computer devices and software. These data\u0000provide valuable insights into system behavior and necessitate thorough\u0000analysis. Recent advances in text analysis have enabled deep learning to\u0000achieve significant breakthroughs in log anomaly detection. However, the high\u0000cost of manual annotation and the dynamic nature of usage scenarios present\u0000major challenges to effective log analysis. This study proposes a novel log\u0000feature extraction model called DualGCN-LogAE, designed to adapt to various\u0000scenarios. It leverages the expressive power of large models for log content\u0000analysis and the capability of graph structures to encapsulate correlations\u0000between logs. It retains key log information while integrating the causal\u0000relationships between logs to achieve effective feature extraction.\u0000Additionally, we introduce Log2graphs, an unsupervised log anomaly detection\u0000method based on the feature extractor. By employing graph clustering algorithms\u0000for log anomaly detection, Log2graphs enables the identification of abnormal\u0000logs without the need for labeled data. We comprehensively evaluate the feature\u0000extraction capability of DualGCN-LogAE and the anomaly detection performance of\u0000Log2graphs using public log datasets across five different scenarios. Our\u0000evaluation metrics include detection accuracy and graph clustering quality\u0000scores. Experimental results demonstrate that the log features extracted by\u0000DualGCN-LogAE outperform those obtained by other methods on classic\u0000classifiers. Moreover, Log2graphs surpasses existing unsupervised log detection\u0000methods, providing a robust tool for advancing log anomaly detection research.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261626","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