Kernel-Level Rootkits Features to Train Learning Models Against Namespace Attacks on Containers

Lee, Wonjun; Nadim, Mohammad

doi:10.1109/cscloud-edgecom49738.2020.00018

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…Miscon iguration of containers or container runtime, as well as the default privileges container runtimes, have led to privilege escalation in the host system and the eventual compromise of it. The namespace feature of the kernel can also be exploited through namespace injec-tion [27], which allows a malicious container to piggyback the hosts' isolation process and see the victim container's PID space just as the host can. In this section, we will delve into one case of container escape in detail and analyze some attacks targeting the containerization process.…”

Section: Cyberattacks On Containersmentioning

confidence: 99%

AI-driven container security approaches for 5G and beyond: A survey

Taha¹,

Kuru²,

Sever³

et al. 2023

ITU J-FET

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The rising use of microservice-based software deployment on the cloud leverages containerized software extensively. The security of applications running inside containers, as well as the container environment itself, are critical for infrastructure in cloud settings and 5G. To address security concerns, research efforts have been focused on container security with subfields such as intrusion detection, malware detection and container placement strategies. These security efforts are roughly divided into two categories: rule-based approaches and machine learning that can respond to novel threats. In this study, we survey the container security literature focusing on approaches that leverage machine learning to address security challenges.

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Section: Cyberattacks On Containersmentioning

confidence: 99%

AI-driven container security approaches for 5G and beyond: A survey

Taha¹,

Kuru²,

Sever³

et al. 2023

ITU J-FET

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“…The security of developed web portal should be evaluated using a variety of testing techniques [16,17]. Advanced malware [18,19] impact on web applications can a good scope of future research.…”

Section: Security Challengesmentioning

confidence: 99%

Community research partnership: A case study of San Antonio Research Partnership Portal

Mohammad¹,

David²,

Adolfo³

2022

Self Cite

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Research collaboration between academic researchers from universities, organizations and government local city departments can be tremendously useful to all institutions, but these collaborations involve a wide range of skill sets, making them difficult to establish and manage. For many local government city departments, finding research professionals and collaborators to solve community problems remains a big challenge. The information of research opportunities is either posted on the individual city department website or researchers are approached through a personal relationship with city department officials. As a result, a researcher interested in working with the city department would have to either navigate various websites or try to build a personal contact with city department officials. In this paper, we will look at the relevance of community research partnerships as well as the barriers that prevent them. We will also demonstrate the development of, Research Partnership Portal, a collaborative platform for academic researchers, organizations, and government city departments in San Antonio. This portal will assist academic researchers and organizations in collaborating with government city departments and using current administrative data to produce effective answers to community concerns.

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“…They demonstrate the suitability of their system using factors such as time-to-deploy, resource usage, and training metrics. Wonjun Lee et al have incorporated containers to train machine learning models that are tasked with identifying kernel-level rootkits in a system [27]. provides a layer of reproducible abstraction for Linux [29].…”

Section: Related Workmentioning

confidence: 99%

Containerization of Model Fitting Workloads over Spatial Datasets

Warushavithana

Mitra

Arabi

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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CONTAINERIZATION OF MODEL FITTING WORKLOADS OVER SPATIAL DATASETSSpatial data volumes have grown exponentially over the past several years. The number of domains in which spatial data are extensively leveraged include atmospheric sciences, environmental monitoring, ecological modeling, epidemiology, sociology, commerce, and social media among others. These data are often used to understand phenomena and inform decision making by fitting models to them. In this study, we present our methodology to fit models at scale over spatial data.Our methodology encompasses segmentation, spatial similarity based on the dataset(s) under consideration, and transfer learning schemes that are informed by the spatial similarity to train models faster while utilizing fewer resources. We consider several model fitting algorithms and execution within containerized environments as we profile the suitability of our methodology. Our benchmarks validate the suitability of our methodology to facilitate faster, resource-efficient training of models over spatial data. Chapter 5Conclusions and Future Work .

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Kernel-Level Rootkits Features to Train Learning Models Against Namespace Attacks on Containers

Cited by 7 publications

References 24 publications

AI-driven container security approaches for 5G and beyond: A survey

AI-driven container security approaches for 5G and beyond: A survey

Community research partnership: A case study of San Antonio Research Partnership Portal

Containerization of Model Fitting Workloads over Spatial Datasets

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