2023 IEEE International Conference on Cyber Security and Resilience (CSR)最新文献_第4页

IoT Forensics: Analysis of Ajax Systems' mobile app for the end user 物联网取证:为最终用户分析Ajax系统的移动应用程序

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224992

Evangelos Dragonas, C. Lambrinoudakis

引用次数: 0

Improving the Detection of Unknown DDoS Attacks through Continual Learning 通过持续学习提高对未知DDoS攻击的检测能力

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224989

Beny Nugraha, Krishna Yadav, Parag Patil, T. Bauschert

{"title":"Improving the Detection of Unknown DDoS Attacks through Continual Learning","authors":"Beny Nugraha, Krishna Yadav, Parag Patil, T. Bauschert","doi":"10.1109/CSR57506.2023.10224989","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224989","url":null,"abstract":"Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) are popular with network security researchers due to their good detection capability and low false alarm rate especially concerning Distributed Denial of Service (DDoS) attacks. However, as the attack pattern usually changes over time, the performance of an IDS that was trained with original data degrades. Moreover, as the changing attack pattern and the emergence of unknown DDoS attacks create more unknown or unlabeled data, a supervised learning approach is not suitable. To mitigate this effect, we propose a robust continual learning method which consists of a semi-supervised approach for pseudo-labeling the unknown data and a sliding window-based retraining scheme. The proposed method is evaluated by using the custom CIC-IDS 2017 dataset, which contains both slow DDoS and flooding DDoS attacks. Three classifiers are considered, namely K-Nearest Neighbors (KNN), XGBoost, and Multilayer Perceptron (MLP). Our evaluation shows that our method is able to improve the detection performance which verifies the quality of the generated pseudo labels.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125673867","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

Multi-party Computation for Privacy and Security in Machine Learning: a Practical Review 机器学习中隐私和安全的多方计算:一个实用回顾

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224826

Alessandro Bellini, E. Bellini, Massimo Bertini, Doaa Almhaithawi, S. Cuomo

{"title":"Multi-party Computation for Privacy and Security in Machine Learning: a Practical Review","authors":"Alessandro Bellini, E. Bellini, Massimo Bertini, Doaa Almhaithawi, S. Cuomo","doi":"10.1109/CSR57506.2023.10224826","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224826","url":null,"abstract":"Machine Learning, particularly Deep Learning, is transforming society in any of its fundamental domains - healthcare, culture, finance, transportation, education, just to mention a few. However Machine Learning suffers from serious weaknesses in privacy and security due to the large amount of data (datasets for training and parameters in trained models) and the probabilistic approximation inherent in any ML function. Multi-Party Computation (MPC) is a family of techniques and tactic with a sound scientific and operative base that can be applied to mitigate some relevant weaknesses of ML. In particular, privacy in training may be assured by MPC with federated learning techniques (these may be considered particular interpretations and implementation of a general MPC method) and also security in training and inference may be enforced by continuous model testing using MPC is a technique that allows multiple parties to evaluate a machine learning model on their private data without revealing it to each other. This brief paper is a practical and essential review on how to use MPC to mitigate privacy and security issues in ML.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127034847","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

Default Credentials Vulnerability: The Case Study of Exposed IP Cams 默认凭证漏洞:暴露的IP摄像头的案例研究

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224944

Stefano Perone, L. Faramondi, R. Setola

{"title":"Default Credentials Vulnerability: The Case Study of Exposed IP Cams","authors":"Stefano Perone, L. Faramondi, R. Setola","doi":"10.1109/CSR57506.2023.10224944","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224944","url":null,"abstract":"The spread of IoT devices poses always major challenges to the issue of network security. In this paper, the study focus on the risks linked to the usage of default credentials in IoT devices, in particular, there is a focus on IP cameras. Many cameras on the Internet, in fact, use the manufacturer's default passwords and this makes it extremely easy to access them by a malicious actor. The importance of the problem should not be underestimated. Starting from an unauthorized access to the device, an attacker has access not only to images but also to a whole series of data that can be extrapolated and that can be used as a preliminary step for criminal actions. The risks of leaving credentials by default are closely related to the lack of attention during the design phase and the resulting vulnerabilities present in tools that do not respect the security-by-design standard; the producers, in fact, take often lightly this concept, leaving the whole task of ensure device security to the user. The main issue related to this vulnerability is the lack of legal protection; there are indeed a lot of tools that make available this open data to everyone without any possible legal restriction. In this paper we propose a practical study considering two case studies showing that the number of IP cam directly connected on Internet with default credential is incredible high. The first case focus on a cheap IP Cam model widely used in several contexts. The second one focus on an IP Cam model that corresponds to an high-end security camera intended purely for high-quality video surveillance and thermal imaging.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121500256","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

SoK: Static Configuration Analysis in Infrastructure as Code Scripts 作为代码脚本的基础架构中的静态配置分析

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224925

Pandu Ranga Reddy Konala, Vimal Kumar, D. Bainbridge

引用次数: 0

Evaluation of Applying LDA to Redacted Documents in Security and Safety Analysis LDA在编校文件安全分析中的应用评价

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/csr57506.2023.10224991

K. Umezawa, Sven Wohlgemuth, Keisuke Hasegawa, K. Takaragi

{"title":"Evaluation of Applying LDA to Redacted Documents in Security and Safety Analysis","authors":"K. Umezawa, Sven Wohlgemuth, Keisuke Hasegawa, K. Takaragi","doi":"10.1109/csr57506.2023.10224991","DOIUrl":"https://doi.org/10.1109/csr57506.2023.10224991","url":null,"abstract":"Cyber attacks are often executed by imitating existing attacks and combining them. Using existing vulnerability databases, we have presented a way to semi-automatically determine the presence of vulnerabilities in the design documents of products under development. We have calculated the similarity between documents using the Latent Dirichlet Allocation (LDA) technology and compared the design document of the new product with the vulnerability database. When this comparison processing is conducted by a third party as a service, it may be desirable to not inadvertently disclose a part of the design document of the new product to the third party. In this study, we used the LDA technique to experimentally verify that the calculated similarity value does not deteriorate even when a portion of the design document is encrypted or obfuscated. In conclusion, we discovered no substantial difference in similarity with the original document; however, there are changes in numerical values depending on the words to be encrypted/obfuscated. In particular, the degradation of similarity is very small when the version number is encrypted/obfuscated.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122184003","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

Randomized Addressing Countermeasures are Inefficient Against Address-Bit SCA 随机寻址对策对地址位SCA是无效的

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224968

I. Kabin, Z. Dyka, Peter Langendoerfer

引用次数: 0

Breaking AES-128: Machine Learning-Based SCA Under Different Scenarios and Devices 破解AES-128:不同场景和设备下基于机器学习的SCA

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10225009

Sara Tehranipoor, Nima Karimian, Jacky Edmonds

引用次数: 0

A Taxonomy for Tsunami Security Scanner Plugins 海啸安全扫描器插件的分类

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224998

G. Lima, Vitor Hugo Gonçalves, Pedro Pinto

{"title":"A Taxonomy for Tsunami Security Scanner Plugins","authors":"G. Lima, Vitor Hugo Gonçalves, Pedro Pinto","doi":"10.1109/CSR57506.2023.10224998","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224998","url":null,"abstract":"Vulnerability scanning tools are essential in detecting systems weaknesses caused by vulnerabilities in their components or wrong configurations. Corporations may use these tools to assess a system in advance and fix its vulnerabilities, thus preventing or mitigating the impact of real attacks. A set of these tools are organized by plugins, each intended to check a specific vulnerability, such as the case of the Tsunami Security Scanner tool released in 2020 by Google. Multiple plugins for this tool were proposed in a community-based approach and thus, it is important for the users and research community to have these plugins in a framework consistently categorized across multiple sources and types. This paper proposes a comprehensive taxonomy for all the 61 plugins available, hierarchically sorted into 2 main categories, 4 categories, 4 subcategories, and 7 types. An analysis and a discussion on statistics by categories and types over time are also provided. The analysis shows that, so far, there are 4 main contributors, being Google, Community, Facebook, and Govtech. The Google source is still the top contributor counting 39 out of 61 plugins and the highest number of plugins available are in the RCE subcategory. The plugins available are mainly focused on critical and high vulnerabilities.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129967941","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

Understanding Resilience: Looking at Frameworks & Standards - A Systematic Study from Cyber Perspective 理解弹性:看框架和标准-从网络角度的系统研究

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224958

Nishanth Laxman, Daniel Krohmer, Markus Damm, R. Schwarz, P. Antonino

引用次数: 0