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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How do AWS S3 Buckets and Security Work?

Chetan
Chetan Last Updated : 12 Oct, 2022
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

AWS provides cloud services such as storage, analytics, blockchain, enterprise machine learning, applications, and security; within this cloud environment is Amazon S3, a cloud storage solution that brings scalability, data availability, security, and performance to companies of any size through so-called “buckets,” or data containers. S3 is a critical solution for companies like Netflix.

AWS S3 Buckets
https://awsmag.com

However, this availability and accessibility bring significant risks. As an outcome of such incidents, cloud security has become a basic necessity for companies that rely on cloud environments to reduce cybersecurity and data security risks.

Security in AWS S3

Like many cloud storage solutions, AWS S3 operates on a shared responsibility model for data protection and cybersecurity. AWS cannot ensure or enforce the use of these practices. “Cloud security” wasn’t the root cause of the various data leaks and breaches of Amazon S3 buckets, but that doesn’t mean all the responsibility falls on its users.

Although AWS currently sets new buckets to be private by default, the consensus among cybersecurity experts is that S3 is designed so that it is too easy for buckets to be inadvertently configured as public. Advanced features such as authenticated user permissions and the interplay between access control lists (ACLs) and bucket policies can confuse AWS users and lead to human configuring errors. A security service focused on detecting, classifying, and protecting information within the AWS ecosystem has greatly reduced the number of misconfigured buckets. However, these tools only address the ramifications of the AWS S3 design problem. Introducing additional features that make it easier to ensure that S3 remains private is not the same as eliminating the ability to provide public access.

Integrating S3 with Other Services

Amazon, like Apple, rewards the end user for living in the ecosystem. For Apple, when you have an iPhone, it automatically pairs with your AirPods and seamlessly syncs back to your iMac. Similarly, with Amazon, the more AWS services you use, the more integrated they become. S3 is the cornerstone of these integrations because object storage is important to almost all AWS applications. You can store CloudFormation templates in S3 to be run through Lambda, deploying compute resources based on the trigger. You can also store templates in S3 for use with SNS or SES notifications. There are many applications for S3 as cloud architecture, so let’s take a deeper look at a few.

One of the popular applications of S3 is storing web files in a way that can be easily loaded by the web or even pointing a static page to an object in S3. By combining Route53 – an AWS network service – with S3, you can route web traffic to a static website hosted on S3. This allows you to avoid devoting computing resources to handling a static file.

AWS S3 Buckets
https://aws.amazon.com

Another way to leverage S3 with other AWS services is to help replicate storage between tenants or regions. When an object is added, removed, or modified in a source bucket, you can generate a record in CloudWatch, AWS’s monitoring solution. CloudWatch could then trigger an SNS notification and call Lambda to synchronize changes between any number of replication segments and verify that the segments are now in sync with the source.

This solution would reap dividends in customer deployment of applications that require up-to-date data, such as malware signature programs. AWS rewards serverless design by providing highly scalable services at a lower cost than traditional server computing. Pairing AWS services with S3 allows you to create a serverless architecture that supports reliable, scalable storage.

Data Protection in S3

Unlike old data storage models, cloud storage solutions make it much easier to protect the infrastructure and ensure the availability of these services. However, cloud security (user responsibility) is still essential. Ignoring best practices can have serious consequences for your company. Therefore, consider the following security measures for data protection.

Classifying and Identifying Info

Identifying the amount and type of information your company stores in S3 buckets is critical to protecting sensitive information and complying with various data protection laws. Companies working with Personally Identifiable Information, cardholder data, and other personal data subject to privacy regulations should pay close attention to the location of that information and the permissions of each segment that contains it. Tools like Amazon Macie make this process easier with machine learning that automatically inspects and classifies it.

Enable Double authentication

This cybersecurity helps f the most effectively minimize the damage caused by phishing attacks or leaking employee credentials. In this case, multi-factor authentication can be activated during the user login process or when accessing specific segments and for certain actions such as deleting segments.

AWS S3 Buckets
https://aws.amazon.com

Encrypt your data

S3 encrypts your data when it writes it to disks in AWS data centers and decrypts it when you access it using server-side encryption. This practice ensures that data cannot be accessed without the correct encryption keys and minimizes the damage caused by data breaches.

The client-side encryption method means you encrypt the data before sending it to AWS and decrypt it after it is retrieved from AWS. This means that AWS does not own or manage your encryption keys.

Block Public Buckets Organization-Wide

AWS Block Public Access adds a layer of security to your account along with individual buckets, even those you create in the future. Your company can limit current public access and ensure that newly created buckets do not have public access.

Enforce least-privileging access

You should also block unwanted access to S3 by enforcing least privilege access and only granting access to accounts when certain tasks are required.

AWS offers tools for establishing least privilege access, such as IAM (identity and access management), user policies with permission boundaries, segment policies, ACLs (access control lists), and service control policies.

Enable version control

S3 Versioning allows you to store and retrieve any version of any item stored in your buckets. S3 Versioning allows you to maintain multiple versions of an object in the same bucket and can help recover assets that have been accidentally deleted or overwritten. You may consider enabling this feature for specific cases, such as S3 buckets that back up sensitive data.

ZenGRC for Minimizing Risk

ZenGRC integrated governance, risk management, and compliance solution that enables businesses to automate the self-audit process. For the exam, your company may need to create various compliance attestations as part of your cloud compliance with AWSons. ZenGRC’s SaaS platform allows you to store and retrieve all your compliance documents in one place. Many AWS users must comply with regulations and industry standards such as PCI DSS, SOC, ISO, FedRAMP, and HIPAA.

ZenGRC is the single source of truth that ensures your organization is always compliant and audit-ready. Policies and procedures are audit-driven and easily found in the document repository. Workflow management features offer easy tracking, automatic reminders, and audit trails. Clear reporting and dashboards provide insight into gaps and high-risk areas.

Conclusion

Let’s see what we have learned today; AWS currently sets new buckets to be private by default, and the consensus among cybersecurity experts is that S3 is designed so that it is too easy for buckets to be inadvertently configured as public. Advanced features such as authenticated user permissions and the interplay between access control lists (ACLs) and bucket policies can confuse AWS users and lead to human error in configuring their S3 buckets.

  • ZenGRC is the single source of truth that ensures your organization is always compliant and audit-ready. Policies and procedures are audit-driven and easily found in the document repository.
  • Companies working with PII (Personally Identifiable Information), cardholder data, and other personal data subject to privacy regulations should pay close attention to the location of that information and the permissions of each segment that contains it.
  • AWS rewards serverless design by providing highly scalable services at a lower cost than traditional server computing. Pairing AWS services with S3 allows you to create a serverless architecture that supports reliable, scalable storage.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

Chetan
Chetan

Data Analyst who love to drive insights by visualizing the data and extracting the knowledge from it. Automating various tasks using python & builds Real time Dashboard's using tech like React and node.js. Capable of Creaking complex SQL queries to fetch the accurate data.

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