2021 IEEE International Conference on Cyber Security and Resilience (CSR)最新文献_第6页

Data Sanitisation and Redaction for Cyber Threat Intelligence Sharing Platforms 网络威胁情报共享平台的数据净化和编校

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527916

Cagatay Yucel, Ioannis Chalkias, Dimitrios Mallis, D. Cetinkaya, Jane Henriksen-Bulmer, Alice Cooper

{"title":"Data Sanitisation and Redaction for Cyber Threat Intelligence Sharing Platforms","authors":"Cagatay Yucel, Ioannis Chalkias, Dimitrios Mallis, D. Cetinkaya, Jane Henriksen-Bulmer, Alice Cooper","doi":"10.1109/CSR51186.2021.9527916","DOIUrl":"https://doi.org/10.1109/CSR51186.2021.9527916","url":null,"abstract":"The recent technological advances and changes in the daily human activities increased the production and sharing of data. In the ecosystem of interconnected systems, data can be circulated among systems for various reasons. This could lead to exchange of private or sensitive information between entities. Data Sanitisation involves processes and practices that remove sensitive and private information from documents before sharing them with entities that should not have access to this information. This paper presents the design and development of a data sanitisation and redaction solution for a Cyber Threat Intelligence sharing platform. The Data Sanitisation and Redaction Plugin has been designed with the purpose of operating as a plugin for the ECHO Project’s Early Warning System platform and enhancing its operative capabilities during information sharing. This plugin aims to provide automated security and privacy-based controls to the concept of CTI sharing over a ticketing system. The plugin has been successfully tested and the results are presented in this paper.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129211816","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: Autonomic Cybersecurity - Securing Future Disruptive Technologies 自主网络安全——确保未来颠覆性技术的安全

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527908

C. Rouff, Lanier A Watkins, Roy Sterritt, S. Hariri

引用次数: 1

DAHID: Domain Adaptive Host-based Intrusion Detection DAHID:基于域自适应主机的入侵检测

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527966

Oluwagbemiga Ajayi, A. Gangopadhyay

{"title":"DAHID: Domain Adaptive Host-based Intrusion Detection","authors":"Oluwagbemiga Ajayi, A. Gangopadhyay","doi":"10.1109/CSR51186.2021.9527966","DOIUrl":"https://doi.org/10.1109/CSR51186.2021.9527966","url":null,"abstract":"Cybersecurity is becoming increasingly important with the explosion of attack surfaces as more cyber-physical systems are being deployed. It is impractical to create models with acceptable performance for every single computing infrastructure and the various attack scenarios due to the cost of collecting labeled data and training models. Hence it is important to be able to develop models that can take advantage of knowledge available in an attack source domain to improve performance in a target domain with little domain specific data.In this work we proposed Domain Adaptive Host-based Intrusion Detection DAHID; an approach for detecting attacks in multiple domains for cybersecurity. Specifically, we implemented a deep learning model which utilizes a substantially smaller amount of target domain data for host-based intrusion detection.In our experiments, we used two datasets from Australian Defense Force Academy; ADFA-WD as the source domain and ADFA-WD:SAA as the target domain datasets. We recorded a significant improvement in Area Under Curve AUC from 83% to 91%, when we fine-tuned a deep learning model trained on ADFA-WD with as little as 20% of ADFA-WD:SAA. Our result shows transfer learning can help to alleviate the need of huge domain specific dataset in building host-based intrusion detection models.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115235769","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: 7

The Cyber-MAR Project: First Results and Perspectives on the Use of Hybrid Cyber Ranges for Port Cyber Risk Assessment Cyber- mar项目:混合网络范围用于港口网络风险评估的初步结果和展望

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527968

Olivier Jacq, Pablo Giménez Salazar, K. Parasuraman, Jarkko Kuusijärvi, Andriana Gkaniatsou, Evangelia Latsa, A. Amditis

引用次数: 3

Quantum Cryptography in Maritime Telecommunications 海事通信中的量子密码学

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527973

Maria Papathanasaki, P. Fountas, L. Maglaras, C. Douligeris, M. Ferrag

引用次数: 0

Understanding and Mitigating Banking Trojans: From Zeus to Emotet 了解和减轻银行木马:从Zeus到Emotet

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527960

Konstantinos-Panagiotis Grammatikakis, Ioannis Koufos, N. Kolokotronis, C. Vassilakis, S. Shiaeles

{"title":"Understanding and Mitigating Banking Trojans: From Zeus to Emotet","authors":"Konstantinos-Panagiotis Grammatikakis, Ioannis Koufos, N. Kolokotronis, C. Vassilakis, S. Shiaeles","doi":"10.1109/CSR51186.2021.9527960","DOIUrl":"https://doi.org/10.1109/CSR51186.2021.9527960","url":null,"abstract":"Banking Trojans came a long way in the past decade, and the recent case of Emotet showed their enduring relevance. The evolution of the modern computing landscape can be traced through Emotet and Zeus, both representative examples from the end of the past decade. As an example of earlier malware, Zeus only needed to employ simple anti-analysis techniques to stay undetected; while the more recent Emotet had to constantly evolve to stay a step ahead. Current host-based antimalware solutions face an increasing number of obstacles to perform their function. A multi-layer approach to network security is necessary for network-based intrusion response systems to secure modern networks of heterogeneous devices. A system based on a combination of a graphical network security model and a game theoretic model of cyber attacks was tested on a testbed with Windows machines infected with Trojans; experimental results showed that the proposed system effectively blocked Trojans’ network communications effectively preventing data leakage and yielding encouraging results for future work.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125736537","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: 14

Development of a Testbed for Fully Homomorphic Encryption Solutions 全同态加密解决方案测试平台的开发

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527988

S. Marrone, A. Tortora, E. Bellini, Antonella Maione, M. Raimondo

引用次数: 2

Statistical Metamorphic Testing of Neural Network Based Intrusion Detection Systems 基于神经网络的入侵检测系统的统计变形测试

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527993

Faqeer ur Rehman, C. Izurieta

引用次数: 5

A workflow and toolchain proposal for analyzing users’ perceptions in cyber threat intelligence sharing platforms 网络威胁情报共享平台中用户感知分析的工作流程和工具链方案

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527903

Borce Stojkovski, G. Lenzini

引用次数: 2

ENAD: An Ensemble Framework for Unsupervised Network Anomaly Detection ENAD:无监督网络异常检测的集成框架

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527982

Jingyi Liao, S. Teo, P. P. Kundu, Tram Truong-Huu

{"title":"ENAD: An Ensemble Framework for Unsupervised Network Anomaly Detection","authors":"Jingyi Liao, S. Teo, P. P. Kundu, Tram Truong-Huu","doi":"10.1109/CSR51186.2021.9527982","DOIUrl":"https://doi.org/10.1109/CSR51186.2021.9527982","url":null,"abstract":"Network anomaly detection is paramount to early detect traffic anomalies and protect networks against cyber attacks such as (distributed) denial of service attacks and phishing attacks. As deep learning has succeeded in various domains, it has been adopted for network anomaly detection using a supervised learning approach. Due to the high velocity and dynamics of network traffic, labeling such voluminous network data with specific domain knowledge is difficult, and yet impossible. It makes supervised learning techniques become impractical. Several existing works have proposed unsupervised learning techniques to train detection models with unlabeled data. However, a single model cannot detect all types of attacking traffic due to the variety of their behavior. In this work, we develop an ensemble framework that uses different AutoEncoders (AEs) and generative adversarial networks (GANs) for network anomaly detection. We develop a weighting scheme that allows us to quantify the importance (goodness) of each model to each attacking traffic and then determine the final prediction score during the inference (detection) phase. We carry out extensive experiments on two recent datasets including UNSW-NB15 and CICIDS2017 to demonstrate the effectiveness of the proposed framework. The experimental results have shown that our framework significantly outperforms many state-of-the-art methods with an increase of up to 14.70% in various performance metrics such as precision, recall, F1-measure, AUROC and AUPRC.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131719685","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: 7