Biblio
Filters: Keyword is use cases [Clear All Filters]
Quantum Computing for Military Applications. 2021 International Conference on Military Communication and Information Systems (ICMCIS). :1–8.
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2021. Quantum computers have the potential to outshine classical alternatives in solving specific problems, under the assumption of mature enough hardware. A specific subset of these problems relate to military applications. In this paper we consider the state-of-the-art of quantum technologies and different applications of this technology. Additionally, four use-cases of quantum computing specific for military applications are presented. These use-cases are directly in line with the 2021 AI strategic agenda of the Netherlands Ministry of Defense.
Security Audit of Docker Container Images in Cloud Architecture. 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC). :202—207.
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2021. Containers technology radically changed the ways for packaging applications and deploying them as services in cloud environments. According to the recent report on security predictions of 2020 by Trend Micro, the vulnerabilities in container components deployed with cloud architecture have been one of the top security concerns for development and operations teams in enterprises. Docker is one of the leading container technologies that automate the deployment of applications into containers. Docker Hub is a public repository by Docker for storing and sharing the Docker images. These Docker images are pulled from the Docker Hub repository and the security of images being used from the repositories in any cloud environment could be at risk. Vulnerabilities in Docker images could have a detrimental effect on enterprise applications. In this paper, the focus is on securing the Docker images using vulnerability centric approach (VCA) to detect the vulnerabilities. A set of use cases compliant with the NIST SP 800-190 Application Container Security Guide is developed for audit compliance of Docker container images with the OWASP Container Security Verification Standards (CSVS). In this paper, firs vulnerabilities of Docker container images are identified and assessed using the VCA. Then, a set of use cases to identify presence of the vulnerabilities is developed to facilitate the security audit of the container images. Finally, it is illustrated how the proposed use cases can be mapped with the requirements of the OWASP Container Security Verification Standards. The use cases can serve as a security auditing tool during the development, deployment, and maintenance of cloud microservices applications.
Towards Consolidating Industrial Use Cases on a Common Fog Computing Platform. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 1:172–179.
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2020. Converging Information Technology (IT) and Operations Technology (OT) in modern factories remains a challenging task. Several approaches such as Cloud, Fog or Edge computing aim to provide possible solutions for bridging OT that requires strict real-time processing with IT that targets computing functionality. In this context, this paper contributes to ongoing Fog computing research by presenting three industrial use cases with a specific focus on consolidation of functionality. Each use case exemplifies scenarios on how to use the computational resources closer to the edge of the network provided by a Fog Computing Platform (FCP). All use-cases utilize the same proposed FCP, which allows drawing a set of requirements on future FCPs, e.g. hardware, virtualization, security, communication and resource management. The central element of the FCP is the Fog Node (FN), built upon commercial off-the-shelf (COTS) multicore processors (MCPs) and virtualization support. Resource management tools, advanced security features and state of the art communication protocols complete the FCP. The paper concludes by outlining future research challenges by comparing the proposed FCP with the identified requirements.
A Privacy and Authentication Scheme for IoT Environments Using ECC and Fuzzy Extractor. 2020 International Conference on Intelligent Systems and Computer Vision (ISCV). :1–5.
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2020. The internet of things (IoT) is consisting of many complementary elements which have their own specificities and capacities. These elements are gaining new application and use cases in our lives. Nevertheless, they open a negative horizon of security and privacy issues which must be treated delicately before the deployment of any IoT. Recently, different works emerged dealing with the same branch of issues, like the work of Yuwen Chen et al. that is called LightPriAuth. LightPriAuth has several drawbacks and weakness against various popular attacks such as Insider attack and stolen smart card. Our objective in this paper is to propose a novel solution which is “authentication scheme with three factor using ECC and fuzzy extractor” to ensure security and privacy. The obtained results had proven the superiority of our scheme's performances compared to that of LightPriAuth which, additionally, had defeated the weaknesses left by LightPriAuth.
Towards a neural language model for signature extraction from forensic logs. 2017 5th International Symposium on Digital Forensic and Security (ISDFS). :1–6.
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2017. Signature extraction is a critical preprocessing step in forensic log analysis because it enables sophisticated analysis techniques to be applied to logs. Currently, most signature extraction frameworks either use rule-based approaches or handcrafted algorithms. Rule-based systems are error-prone and require high maintenance effort. Hand-crafted algorithms use heuristics and tend to work well only for specialized use cases. In this paper we present a novel approach to extract signatures from forensic logs that is based on a neural language model. This language model learns to identify mutable and non-mutable parts in a log message. We use this information to extract signatures. Neural language models have shown to work extremely well for learning complex relationships in natural language text. We experimentally demonstrate that our model can detect which parts are mutable with an accuracy of 86.4%. We also show how extracted signatures can be used for clustering log lines.