2021 18th International Conference on Privacy, Security and Trust (PST)最新文献_第6页

Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data 基于物联网流数据的局部差分隐私技术测量

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647839

Sharmin Afrose, D. Yao, O. Kotevska

引用次数: 2

Practical Protection of Binary Applications via Transparent Immunization 通过透明免疫实现二进制应用程序的实际保护

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647820

Xinyuan Wang

{"title":"Practical Protection of Binary Applications via Transparent Immunization","authors":"Xinyuan Wang","doi":"10.1109/PST52912.2021.9647820","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647820","url":null,"abstract":"In the past few years, massive data breach attacks on large organizations (e.g., Anthem Inc., Equifax) have compromised sensitive data of tens or even hundreds of millions of people. The 2017 Equifax data breach attack has compromised sensitive data of 148 million people and has costed Equifax $$ 1.4$ billion as of May 2019. Unfortunately the average time to detect, contain a data breach was 206 days and 73 days respectively in 2019. There is a pressing need to develop practical and deployable capability to detect and block previously unseen, application specific cyberattacks on vulnerable binary applications in real-time. In this paper, we present AppImmu, a practical cyber defense system that can detect and block previously unknown cyber-attacks on vulnerable binary applications in real-time with no false positive. Given a potentially vulnerable ELF binary application, AppImmu can transparently and statically immunize it into an immunized version via binary rewriting. At run-time, AppImmu uses kernel level immunization based anomaly detection techniques to detect and block previously unknown cyberattacks on immunized binary applications without any prior knowledge of the attacks. We have successfully immunized real world large binary applications such as Apache Java execution environment, bash shell, Snort in Linux and have successfully detected and blocked real world data breach attacks (e.g., Apache Strut exploit used in 2017 Equifax data breach attack, Shellshock exploit) in true real-time. Our benchmark experiments show that AppImmu incurs less than 6% run-time overhead in overall system performance, 2.1% run-time overhead for applications under typical workload.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114275312","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

Intrusion Detection in Internet of Things using Convolutional Neural Networks 基于卷积神经网络的物联网入侵检测

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647828

M. Kodys, Zhi Lu, Fok Kar Wai, V. Thing

{"title":"Intrusion Detection in Internet of Things using Convolutional Neural Networks","authors":"M. Kodys, Zhi Lu, Fok Kar Wai, V. Thing","doi":"10.1109/PST52912.2021.9647828","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647828","url":null,"abstract":"Internet of Things (IoT) has become a popular paradigm to fulfil needs of the industry such as asset tracking, resource monitoring and automation. As security mechanisms are often neglected during the deployment of IoT devices, they are more easily attacked by complicated and large volume intrusion attacks using advanced techniques. Artificial Intelligence (AI) has been used by the cyber security community in the past decade to automatically identify such attacks. However, deep learning methods have yet to be extensively explored for Intrusion Detection Systems (IDS) specifically for IoT. Most recent works are based on time sequential models like LSTM and there is short of research in CNNs as they are not naturally suited for this problem. In this article, we propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time that are useful for attacks detection by CNNs. The proposed method is integrated with two classical CNNs: ResNet and EfficientNet, where the detection performance is evaluated. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115663086","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: 3

Unmasking Privacy Leakage through Android Apps Obscured with Hidden Permissions 通过隐藏权限隐藏的Android应用揭秘隐私泄露

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647851

Pranav Kotak, S. Bhandari, A. Zemmari, Jaykrishna Joshi

引用次数: 0

Detection of Induced False Negatives in Malware Samples 恶意软件样本中诱导假阴性的检测

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647787

Adrian Wood, Michael N. Johnstone

{"title":"Detection of Induced False Negatives in Malware Samples","authors":"Adrian Wood, Michael N. Johnstone","doi":"10.1109/PST52912.2021.9647787","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647787","url":null,"abstract":"Malware detection is an important area of cyber security. Computer systems rely on malware detection applications to prevent malware attacks from succeeding. Malware detection is not a straightforward task, as new variants of malware are generated at an increasing rate. Machine learning (ML) has been utilised to generate predictive classification models to identify new malware variants which conventional malware detection methods may not detect. Machine learning, has however, been found to be vulnerable to different types of adversarial attacks, in which an attacker is able to negatively affect the classification ability of the ML model. Several defensive measures to prevent adversarial poisoning attacks have been developed, but they often rely on the use of a trusted clean dataset to help identify and remove adversarial examples from the training dataset. The defence in this paper does not require a trusted clean dataset, but instead, identifies intentional false negatives (zero day malware classified as benign) at the testing stage by examining the activation weights of the ML model. The defence was able to identify 94.07% of the successful targeted poisoning attacks.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123745780","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

TEE-based Selective Testing of Local Workers in Federated Learning Systems 基于tee的联邦学习系统中本地工人的选择性测试

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-11-04 DOI: 10.1109/PST52912.2021.9647773

Wensheng Zhang, Trent Muhr

{"title":"TEE-based Selective Testing of Local Workers in Federated Learning Systems","authors":"Wensheng Zhang, Trent Muhr","doi":"10.1109/PST52912.2021.9647773","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647773","url":null,"abstract":"This paper considers a federated learning system consisting of a central aggregation server and multiple distributed local workers, all having access to trusted execution environments (TEEs). For the local workers, which are untrusted but economically-rational, to conduct local learning honestly, we propose a TEE-based selective testing scheme that also combines techniques from applied cryptography, game theory and smart contract. Theoretical analysis of the scheme indicates that only a small number of tests are needed to enforce honest execution by the local workers. Implementation-based experiments compare the cost of the proposed scheme against two reference schemes (i.e., the original scheme without security measure and the all-SGX scheme which conducts training completely in an SGX enclave). The results show that, our proposed scheme incurs much lower cost at the SGX enclave though introducing a higher cost at the untrusted execution environment. We argue that this tradeoff is appropriate given that computing in the untrusted environment can access more resources and is cheaper than in the trusted environment. The experiment results also show that, the increase of the cost in the untrusted execution environment get smaller as the size of the training model increases, which demonstrates the scalability of the scheme.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129911561","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: 1

Introducing a Framework to Enable Anonymous Secure Multi-Party Computation in Practice 在实践中引入一个实现匿名安全多方计算的框架

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-11-03 DOI: 10.1109/PST52912.2021.9647793

Malte Breuer, Ulrike Meyer, S. Wetzel

引用次数: 1

Dazed and Confused: What’s Wrong with Crypto Libraries? 茫然和困惑:加密库有什么问题?

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-11-02 DOI: 10.1109/PST52912.2021.9647786

Mohammadreza Hazhirpasand, Oscar Nierstrasz, Mohammad Ghafari

{"title":"Dazed and Confused: What’s Wrong with Crypto Libraries?","authors":"Mohammadreza Hazhirpasand, Oscar Nierstrasz, Mohammad Ghafari","doi":"10.1109/PST52912.2021.9647786","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647786","url":null,"abstract":"Recent studies have shown that developers have difficulties in using cryptographic APIs, which often led to security flaws. We are interested to tackle this matter by looking into what types of problems exist in various crypto libraries. We manually studied 500 posts on Stack Overflow associated with 20 popular crypto libraries. We realized there are 10 themes in the discussions. Interestingly, there were only two questions related to attacks against cryptography. There were 63 discussions in which developers had interoperability issues when working with more than a crypto library. The majority of posts (112) were about encryption/decryption problems and 111 were about installation/compilation issues of crypto libraries. Overall, we realize that the crypto libraries are frequently involved in more than five themes of discussions. We believe the current initial findings can help team leaders and experienced developers to correctly guide the team members in the domain of cryptography. Moreover, future research should investigate the similarity of problems at the API level among popular crypto libraries.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124782187","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: 1

Long Passphrases: Potentials and Limits 长密码短语:潜力和限制

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-10-18 DOI: 10.1109/PST52912.2021.9647800

Christopher Bonk, Zach Parish, Julie Thorpe, Amirali Salehi-Abari

引用次数: 2

A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI PyPI中Python包的大规模面向安全的静态分析

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-07-27 DOI: 10.1109/PST52912.2021.9647791

Jukka Ruohonen, Kalle Hjerppe, Kalle Rindell

{"title":"A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI","authors":"Jukka Ruohonen, Kalle Hjerppe, Kalle Rindell","doi":"10.1109/PST52912.2021.9647791","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647791","url":null,"abstract":"Different security issues are a common problem for open source packages archived to and delivered through software ecosystems. These often manifest themselves as software weaknesses that may lead to concrete software vulnerabilities. This paper examines various security issues in Python packages with static analysis. The dataset is based on a snapshot of all packages stored to the Python Package Index (PyPI). In total, over 197 thousand packages and over 749 thousand security issues are covered. Even under the constraints imposed by static analysis, (a) the results indicate prevalence of security issues; at least one issue is present for about 46% of the Python packages. In terms of the issue types, (b) exception handling and different code injections have been the most common issues. The subprocess module stands out in this regard. Reflecting the generally small size of the packages, (c) software size metrics do not predict well the amount of issues revealed through static analysis. With these results and the accompanying discussion, the paper contributes to the field of large-scale empirical studies for better understanding security problems in software ecosystems.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124760345","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: 8