Summary of 2. Cyber Network Data Processing; AI Data Architecture

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00:00:00 - 01:00:00

This video discusses the importance of data architecture in AI development, and provides an overview of data pipelines and tabular data. It explains how data can be shared efficiently between different parties, and how to keep files organized and separated from their directory structure.

  • 00:00:00 The goal of this project was to detect and classify Network attacks from real internet traffic. To do this, they had to find a dataset that was of interest. They found this dataset, called the Day in the Life of the Internet, which is reasonably large and has been going on for multiple years. The data set is 20 terabytes in size and it can't fit on a single computer or a single node.
  • 00:05:00 This video discusses cyber security issues, focusing on network data processing. It covers four different ways to analyze and exploit data: model-based, distance-based, unsupervised learning, and probing and scanning. A common attack technique, port scanning, is detailed.
  • 00:10:00 The video discusses the steps involved in data conditioning, which include decompressing the raw data, converting it into flows, and training a machine learning classifier on it.
  • 00:15:00 This video discusses cyber-network data processing, AI data architecture, and how network flows (representing the viewing of a YouTube video) can be converted into a feature space. Flow entropy is used to determine whether a flow is anomalous, and trial and error is used to find the best features to detect anomalies.
  • 00:20:00 This video discusses the research behind two neural networks that are used to detect network attacks. The first neural network is used to measure the entropy of a network flow, and the second neural network is used to predict the type and intensity of an attack. The research found that having a large amount of data was key to successfully creating the models.
  • 00:25:00 In this video, Vijay talks about how data architecture can help speed up the development of AI applications. He outlines some simple techniques that can be used to get data into a tabular format, organize it, and share it with others. He also discusses some common applications and how to design data to make it easy to share.
  • 00:30:00 The video explains the importance of data, and how it can be used to help develop AI systems. It also provides a brief overview of the data pipeline, which is a common way for AI applications to work.
  • 00:35:00 This video provides a brief overview of data analysis pipelines, highlighting the importance of using tables in a familiar and compatible format. spreadsheet programs are among the most popular tools for data analysis, due to their flexibility and widespread use.
  • 00:40:00 The video discusses how tables are used in data processing, and different software packages commonly use different terms for tables. Learning how to identify and use tables in different software can help with data analysis.
  • 00:45:00 The video discusses the importance of tabular data, how to format and name files for data storage, and the importance of keeping file sizes within a certain range.
  • 00:50:00 The video discusses the naming scheme for directories that are less than 1,000 files in size. This scheme includes hierarchical directories with file names that are separated by the year, month, day, hour, and minute. This naming scheme is easy to understand and use, and it keeps files organized and separated from their directory structure.
  • 00:55:00 This video discusses how data can be shared efficiently between different parties, and how to identify and contact the data keeper. It also discusses the importance of good OPSEC when sharing data.

01:00:00 - 01:15:00

The video discusses the importance of data architecture and how it can be used to help make AI products more successful. It also discusses the importance of data sharing and communication between different groups involved in AI development, and provides practical advice on data wrangling and data release.

  • 01:00:00 The video discusses the importance of data architecture and how it can be used to help AI products become more successful. It also discusses how to get subject matter experts (SMEs) on board with AI, and how Dana Boehner is one company that does this very well.
  • 01:05:00 The video discusses the benefits and requirements of data sharing, focusing on the role of the information security officer. It highlights the importance of data sharing between subject matter experts and data keepers, and the need for communication between these groups. It also points out the importance of data sharing between researchers and information security officers in order to ensure that data is released safely and responsibly.
  • 01:10:00 The video discusses the importance of properly communicating information security risks to subject matter experts, and how ISO members can help make this process smoother. It also provides an example of a question a subject matter expert might ask when assessing a data security risk.
  • 01:15:00 The video discusses the importance of data wrangling, AI data architecture, and how to release data effectively. It provides practical advice on how to get data analysis done, focusing on how to work with stakeholders to ensure a smooth, generalized release of data.

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