Security and Safety最新文献

On Cultivation of Cybersecurity and Safety talents and Responsible Developers 关于培养网络安全人才和负责任的开发人员

Security and Safety Pub Date : 2024-07-19 DOI: 10.1051/sands/2024010

Jiaxi Chen, Hong Zou, Jiangxing Wu, Fan Zhang, Yuting Shang, Xinsheng JI

{"title":"On Cultivation of Cybersecurity and Safety talents and Responsible Developers","authors":"Jiaxi Chen, Hong Zou, Jiangxing Wu, Fan Zhang, Yuting Shang, Xinsheng JI","doi":"10.1051/sands/2024010","DOIUrl":"https://doi.org/10.1051/sands/2024010","url":null,"abstract":"To address the serious imbalance between the supply and demand of cybersecurity workforce, this paper proposes to embrace the latest trend of a fundamental shift in the \"underlying dynamics of the digital ecosystem\", focusing on a shared liability for cybersecurity between the application side and the manufacturing side. Assuming that product providers shall take more responsibility by implementing secure defaults, this paper explores the establishment of an S&S talent cultivation system to strike the right balance of cybersecurity liabilities by nurturing more responsible developers. This paper proposes a Knowledge, Skill, and Awareness(KSA) model for Security and Safety (S&S) talent cultivation, and proves the feasibility of this model by analyzing the theoretical, disciplinary, methodological, practical, and societal foundations of S&S talent cultivation. Additionally, this paper also proposes principles and strategies for building a S&S talent cultivation system based on its unique characteristics and patterns. It gives a talent cultivation scheme, supported by \"Independent Knowledge System, Education and Cultivation System, Practice and Training system, Evaluation and Certification system, and Awareness Popularization System\". Finally, this paper puts forward a proposal for coordinating efforts and adopting multiple measures to accelerate the cultivation of S&S talents.","PeriodicalId":513337,"journal":{"name":"Security and Safety","volume":"117 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822172","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

RiskTree: Decision Trees for Asset and Process Risk Assessment Quantification in Big Data Platforms RiskTree：在大数据平台中量化资产和流程风险评估的决策树

Security and Safety Pub Date : 2024-07-03 DOI: 10.1051/sands/2024009

Zhenyang Guo, Haomou Zhan, Jiawei Yang, Jin Cao, Wei You, X. Zhao, Hui Li, Dong Zhang

{"title":"RiskTree: Decision Trees for Asset and Process Risk Assessment Quantification in Big Data Platforms","authors":"Zhenyang Guo, Haomou Zhan, Jiawei Yang, Jin Cao, Wei You, X. Zhao, Hui Li, Dong Zhang","doi":"10.1051/sands/2024009","DOIUrl":"https://doi.org/10.1051/sands/2024009","url":null,"abstract":"The inherent characteristics of big data lies in its voluminous scale, varied data formats, and swift processing velocity. The intrinsic characteristics of big data undermine the efficacy of conventional data security techniques and data management standards, consequently compromising the security of big data. As a consequence, big data possesses susceptibilities to security incidents, including unauthorized data access, data manipulation, and data compromise throughout the transmission, storage, and processing stages. Conventional information system security risk assessment methodologies are constrained by human resources and computational techniques, rendering them unsuitable for direct application to big data platforms. Consequently, there is an urgent necessity to develop a risk assessment framework tailored specifically for big data environments, capable of quantifying potential risks and losses. In response to this need, we have devised an automated risk assessment theory that assimilates the unique characteristics of big data with traditional quantitative methods, introducing a risk metric system suited to the big data context. Utilizing the risk-related data generated during operations on the big data platform, we train a decision tree model to derive the weights for each risk indicator. These weights are then employed to conduct a weighted summation of the operational risk indicators, thereby achieving a quantitative evaluation of the platform's risk profile. To substantiate the proposed framework, experiments were conducted on a simulated big data platform. The experimental outcomes demonstrate that, compared to existing quantitative risk assessment methodologies, our approach enables an automatic, objective, and efficient assessment and quantification of the risks associated with tangible assets and data processing operations within the big data platform.","PeriodicalId":513337,"journal":{"name":"Security and Safety","volume":"186 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681648","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

Preface: Security and Privacy for Space-Air-Ground Integrated Networks 前言：天-空-地一体化网络的安全与隐私

Security and Safety Pub Date : 2024-05-07 DOI: 10.1051/sands/2024008

Cheng Huang, Jiangzhou Wang, Yue Gao, Haojin Zhu

引用次数: 0

VAEFL: Integrating Variational Autoencoders for Privacy Preservation and Performance Retention in Federated Learning VAEFL：在联合学习中整合变异自动编码器以保护隐私和保持性能

Security and Safety Pub Date : 2024-04-25 DOI: 10.1051/sands/2024005

Zhixin Li, Yicun Liu, Jiale Li, Guangnan Ye, Hongfeng Chai, Zhihui Lu, Jie Wu

{"title":"VAEFL: Integrating Variational Autoencoders for Privacy Preservation and Performance Retention in Federated Learning","authors":"Zhixin Li, Yicun Liu, Jiale Li, Guangnan Ye, Hongfeng Chai, Zhihui Lu, Jie Wu","doi":"10.1051/sands/2024005","DOIUrl":"https://doi.org/10.1051/sands/2024005","url":null,"abstract":"Federated Learning (FL) heralds a paradigm shift in the training of artificial intelligence (AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of artificial intelligence technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional Federated Learning methods are vulnerable to Deep Leakage from Gradients (DLG) attacks, and typical defensive strategies often result in excessive computational costs or substantial decreases in model accuracy. To navigate these challenges, this research introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders (VAEs) to bolster privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, offering a secure and efficient federated learning approach for the sensitive application of FL in the financial domain.","PeriodicalId":513337,"journal":{"name":"Security and Safety","volume":"10 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140658619","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

Privacy-Preserving Location Authentication for Low-altitude UAVs: A Blockchain-based Approach 低空无人机的隐私保护定位认证：基于区块链的方法

Security and Safety Pub Date : 2024-03-19 DOI: 10.1051/sands/2024004

Hengchang Pan, Yuanshuo Wang, Wei Wang, Ping Cao, Fangwei Ye, Qihui Wu

引用次数: 0

Robust Object Detection for Autonomous Driving Based on Semi-supervised Learning 基于半监督学习的自主驾驶鲁棒性目标检测

Security and Safety Pub Date : 2024-02-05 DOI: 10.1051/sands/2024002

Huilin Yin, Wenwen Chen, Jun Yan, Weiquan Huang, Wancheng Ge, Huaping Liu

{"title":"Robust Object Detection for Autonomous Driving Based on Semi-supervised Learning","authors":"Huilin Yin, Wenwen Chen, Jun Yan, Weiquan Huang, Wancheng Ge, Huaping Liu","doi":"10.1051/sands/2024002","DOIUrl":"https://doi.org/10.1051/sands/2024002","url":null,"abstract":"Deep learning based on labeled data has brought massive success in computer vision, speech recognition, and natural language processing. Nevertheless, labeled data is just a drop in the ocean compared with unlabeled data. How can people utilize the unlabeled data effectively? Research has focused on unsupervised and semi-supervised learning to solve such a problem. Some theoretical and empirical studies have proved that unlabeled data can help boost the generalization ability and robustness under adversarial attacks.\u0000However, current theoretical research on the relationship between robustness and unlabeled data limits its scope to toy datasets. Meanwhile, the visual models in autonomous driving need a significant improvement in robustness to guarantee security and safety. This paper proposes a semi-supervised learning framework for object detection in autonomous vehicles, improving the robustness with unlabeled data. Firstly, we build a baseline with the transfer learning of an unsupervised contrastive learning method—Momentum Contrast\u0000(MoCo). Secondly, we propose a semi-supervised co-training method to label the unlabeled data for retraining, which improves generalization on the autonomous driving dataset. Thirdly, we apply the unsupervised Bounding Box data augmentation (BBAug) method based on a search algorithm, which uses reinforcement learning to improve the robustness of object detection for autonomous driving. We present an empirical study on the KITTI dataset with diverse adversarial attack methods. Our proposed method realizes\u0000the state-of-the-art generalization and robustness under white-box attacks (DPatch and Contextual Patch) and black-box attacks (Gaussian noise, Rain, Fog, and so on). Our proposed method and empirical study show that using more unlabeled data benefits the robustness of perception systems in all-weather autonomous driving. Code is available at: https://github.com/CHENWenwen19/co-training_for_autonomous-driving.","PeriodicalId":513337,"journal":{"name":"Security and Safety","volume":"30 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864975","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

Robust Object Detection for Autonomous Driving Based on Semi-supervised Learning 基于半监督学习的自主驾驶鲁棒性目标检测

Security and Safety Pub Date : 2024-02-05 DOI: 10.1051/sands/2024002

Huilin Yin, Wenwen Chen, Jun Yan, Weiquan Huang, Wancheng Ge, Huaping Liu

{"title":"Robust Object Detection for Autonomous Driving Based on Semi-supervised Learning","authors":"Huilin Yin, Wenwen Chen, Jun Yan, Weiquan Huang, Wancheng Ge, Huaping Liu","doi":"10.1051/sands/2024002","DOIUrl":"https://doi.org/10.1051/sands/2024002","url":null,"abstract":"Deep learning based on labeled data has brought massive success in computer vision, speech recognition, and natural language processing. Nevertheless, labeled data is just a drop in the ocean compared with unlabeled data. How can people utilize the unlabeled data effectively? Research has focused on unsupervised and semi-supervised learning to solve such a problem. Some theoretical and empirical studies have proved that unlabeled data can help boost the generalization ability and robustness under adversarial attacks.\u0000However, current theoretical research on the relationship between robustness and unlabeled data limits its scope to toy datasets. Meanwhile, the visual models in autonomous driving need a significant improvement in robustness to guarantee security and safety. This paper proposes a semi-supervised learning framework for object detection in autonomous vehicles, improving the robustness with unlabeled data. Firstly, we build a baseline with the transfer learning of an unsupervised contrastive learning method—Momentum Contrast\u0000(MoCo). Secondly, we propose a semi-supervised co-training method to label the unlabeled data for retraining, which improves generalization on the autonomous driving dataset. Thirdly, we apply the unsupervised Bounding Box data augmentation (BBAug) method based on a search algorithm, which uses reinforcement learning to improve the robustness of object detection for autonomous driving. We present an empirical study on the KITTI dataset with diverse adversarial attack methods. Our proposed method realizes\u0000the state-of-the-art generalization and robustness under white-box attacks (DPatch and Contextual Patch) and black-box attacks (Gaussian noise, Rain, Fog, and so on). Our proposed method and empirical study show that using more unlabeled data benefits the robustness of perception systems in all-weather autonomous driving. Code is available at: https://github.com/CHENWenwen19/co-training_for_autonomous-driving.","PeriodicalId":513337,"journal":{"name":"Security and Safety","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805107","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

Preface: Security and safety in physical layer systems 前言物理层系统的安全保障

Security and Safety Pub Date : 2024-01-19 DOI: 10.1051/sands/2024001

Aiqun Hu, Liang Jin, Xiangyun Zhou, Feng Shu, Xiangwei Zhou

引用次数: 0