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The large amount of third-party packages available in fast-moving software ecosystems, such as Node.js/npm, enables attackers to compromise applications by pushing malicious updates to their package dependencies. Studying the npm repository, we observed that many packages in the npm repository that are used in Node.js applications perform only simple computations and do not need access to filesystem or network APIs. This offers the opportunity to enforce least-privilege design per package, protecting applications and package dependencies from malicious updates. We propose a lightweight permission system that protects Node.js applications by enforcing package permissions at runtime. We discuss the design space of solutions and show that our system makes a large number of packages much harder to be exploited, almost for free.
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HSKL: A Machine Learning Framework for Hyperspectral Image Analysis
A new framework for advanced machine learning-based analysis of hyperspectral datasets HSKL was built using the well-known package scikit-learn. In this paper, we describe HSKL’s structure and basic usage. We also showcase the diversity of models supported by the package by applying 17 classification algorithms and measure their baseline performance in segmenting objects with highly similar spectral properties.
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- Award ID(s):
- 1827656
- PAR ID:
- 10283590
- Date Published:
- Journal Name:
- 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
- ISSN:
- 2158-6276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
