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1
Intro
2
What is data science
3
Data exploration preparation principles
4
Applying the algorithms
5
Communication
6
Machine Learning
7
Strong Foundations
8
Measure
9
Naming conventions
10
Detect
11
Analysis
12
New detection
13
New vulnerabilities
14
Metadata
15
Data quirks
16
Check new detection
17
How to communicate
18
Data flow in infosec
19
Balance between caveats and usability
20
Different perspectives on the data
21
Vulnerability assets
22
Actual insight
23
Actionable insight
24
Beyond your data set
25
Other data sets
26
Get more data
27
The takeaway
28
Importance of basic statistics
29
Common mistakes
30
Lessons learned
31
Measuring risk
Description:
Explore data science security principles and best practices in this 57-minute conference talk from BSidesLV 2016. Delve into data exploration preparation, algorithm application, and effective communication techniques. Learn how to establish strong foundations, implement proper naming conventions, and conduct thorough analysis. Discover methods for detecting new vulnerabilities, handling metadata, and addressing data quirks. Gain insights on balancing caveats with usability, understanding different data perspectives, and extracting actionable insights from vulnerability assets. Examine the importance of basic statistics, common pitfalls to avoid, and lessons learned in measuring risk. Enhance your ability to secure data science projects and communicate findings effectively across various infosec contexts.

How to Make Sure Your Data Science Isn't Vulnerable to Attack

BSidesLV
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