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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

Getting Started with Cloud Security and its Applications

Hari Bhutanadhu
Hari Bhutanadhu Last Updated : 02 Mar, 2023
6 min read

Introduction

A guide to securing your data and applications will be presented throughout this article. Most organizations store and process their data in the cloud. Cybersecurity threatens cloud computing resources, including data, applications, and infrastructure. This article discusses the procedures, tools, and regulations that guard these assets. As well as talking about data security, network security, cloud security, and tools that support cloud security, we’ll also talk about security concerns and dangers related to the cloud.

"

Learning Objectives

  • Learn about cloud security
  • Get to know cloud’s security threats and risks
  •  Learn about the tools and best practices

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

Table of Contents

Cloud Security Threats and Risks

Cloud security threats and risks are potential security issues. These issues can compromise the confidentiality and availability of data and applications in the cloud.

Cloud Security

Some of the most common threats and risks include:

  • Insider Threats: Insider threats refer to the dangers posed by authorized users, For example, employees, and contractors, who intentionally abuse their privileges to undermine or damage the security of the cloud environment.
  • Data Breaches: Data breaches occur when confidential, sensitive, or protected information is exposed to unauthorized individuals. The files in a data breach are viewed and shared without permission. It occurs when a hacker or unknown person outside the organization gets unauthorized access to sensitive data in the cloud, damaging the organization’s reputation.
  • Misconfigured Cloud Services: Misconfigured cloud services can lead to security vulnerabilities that attackers can use for someone else’s benefit to gain unauthorized access to data and applications.
  • Inadequate Access Controls: These allow unauthorized users to access sensitive data and resources in the cloud.
  • Lack of Visibility and Control: Organizations may lack visibility and control over their cloud environments, making detecting and responding to security threats difficult.

Authentication and Access Control

Authentication and access controls are essential components of it. Authentication involves verifying a user’s identity using a password, security token, or biometric data. Access controls restrict access to cloud resources based on the user’s identity and authorization level. These measures are implemented in different groups, such as the network, operating system, and applications, to safeguard cloud data and resources against unauthorized access.

Cloud Security

The best practices for implementing authentication and access controls in the cloud are:

  • Multi-factor Authentication: We can implement Multi-factor authentication for security, which can be performed using a multi-factor authenticator. Users must give additional verification beyond their passwords, such as a security token or a biometric characteristic.
  • Role-based Access Controls: We can Use role-based access controls (RBAC) as it ensures employees access only information when they need it. It also prevents them from accessing information that doesn’t concern them. It helps to assign users to specific roles with predetermined access privileges.

Data Security in the Cloud

Data security in the cloud refers to securing data from theft, unauthorized access, and other potential threats when it’s maintained in a cloud computing environment.

  • Data Encryption: Encryption converts data into a secret code to prevent unauthorized access. Cloud providers typically offer various encryption options, including at-rest and in-transit encryption.
  • Identity and Access Management (IAM): IAM controls access to data and resources by managing user identities and permissions. Cloud providers offer IAM solutions such as role-based access control (RBAC), multi-factor authentication (MFA), and conditional access controls.
  • Backup and Disaster Recovery: Cloud providers often offer data backup and disaster recovery solutions to ensure data availability and reduce downtime during data loss or disaster.
  • Data Loss Prevention (DLP): DLP tools prevent sensitive data from leaving the organization’s network or being shared with unauthorized users.
  • Compliance and Regulation: Cloud providers often comply with industry and government regulations such as GDPR, HIPAA, and PCI DSS to protect sensitive data.
  • Monitoring and Logging: These providers may offer monitoring and logging tools to detect and respond to security incidents in real-time.

Network Security

Network security ensures all policies, protections, and practices required to protect the infrastructure, methods, and data from unauthorized access or misuse. This includes securing the storage, databases, and network traffic between users and the cloud service.

  • Virtual Private Networks (VPNs): A VPN enables a secure online connection between a user’s device and a cloud network. Secure data transport is made possible by the encrypted connection.
  • Network Segmentation and Isolation: The terms “network segmentation” and “network isolation” relate to partitioning a network into more distinct, smaller segments and isolating those parts from one another. limiting the impact of security flaws and preventing unwanted access.
  • Firewalls and Intrusion Detection/Prevention Systems (IDS/IPS): Firewalls and IDS/IPS systems watch network traffic for irregularities and prevent illegal access attempts.
  • Cloud providers frequently provide security groups and access controls to limit network access to approved users and apps.
  • Network Logging and Monitoring: To identify and address security incidents in real time, cloud providers may additionally offer solutions for network logging and monitoring.

Cloud Provider Security

The term “cloud provider security” describes the security procedures and policies used by service providers to safeguard their clients’ data and online applications. The security of their servers, storage, and networks, as well as the protection of their customers’ data from theft, unauthorized access, and other risks, are the responsibility of the service providers.

Among the technical and practical safeguards used by cloud providers are:

  • Physical Security: Cloud providers should ensure the data center’s safety and facilities from unauthorized access, natural disasters, and other physical threats.
  • Network Security: To prevent unauthorized access to their customers’ data, cloud providers utilize firewalls, intrusion detection, and prevention systems (IDS/IPS), and virtual private networks (VPNs).
  • Identity and Access Management (IAM): Service providers utilize strict IAM policies and procedures to guarantee that only authorized users can access the data and resources of their clients.
  • Cloud providers ensure data security via encryption, key management, and other techniques.
  • Disaster Recovery and Incident Response: These providers must have plans to ensure they can respond to security problems quickly and effectively and lessen the impact on their clients.
  • Compliance and Regulation: To protect the data of its clients, cloud providers are required to adhere to a number of industry and governmental policies and standards, including GDPR, HIPAA, and PCI DSS.

Cloud Security Tools and Best Practices

  • Cloudflare Access: Access is a tool for securing access to your web applications and APIs. You can use access to enforce access controls and authenticate users based on their identity.
  • Amazon GuardDuty: GuardDuty is a threat detection service that monitors malicious activity and unauthorized behavior in your AWS environment. You can use the AWS Management Console or AWS CLI to manage GuardDuty.
  • Example code to enable GuardDuty using AWS CLI:
aws guardduty create-detector --enable1lua#import csv
  • IAM: Identity and Access Management (IAM) tools manage access to cloud services and resources, such as controlling user permissions and authentication methods.
  • Example code to create an IAM user using AWS CLI:
aws iam create-user--user-name <username>1sql#import csv
  • Cloud Security Posture Management (CSPM): CSPM tools continuously monitor infrastructure and help organizations identify and fix configuration and security issues before they can be exploited.

Cloud Security Best Practices

  • Implement a Strong Identity and Access Management (IAM) Policy: Use multi-factor authentication, least privilege access, and access controls to ensure only authorized users can access cloud resources.
  • Encrypt Sensitive Data: Use encryption for all sensitive data at rest and in transit. This includes data stored in the cloud and transmitted to and from the cloud.
    Cloud Security
    Source: Liquid-Web
  • Monitor your Cloud Environment: Use CSPM tools to monitor your environment for potential security threats, such as unauthorized access, configuration errors, and suspicious activity.
  • Backup and Disaster Recovery: Develop and implement a backup and disaster recovery plan to ensure that critical data and services are always available.
  • Conduct Regular Security Assessments: Regular penetration testing to identify and remediate vulnerabilities in your cloud environment.

By following these best practices, organizations can ensure the security of their data and applications in the cloud.

Conclusion

In conclusion, cloud security is critical for organizations that rely on cloud computing services for their data storage and processing needs. Implementing proper security measures to protect against various cybersecurity threats, including data breaches, insider threats, and denial-of-service attacks, is essential. The shared responsibility model between service providers and customers outlines specific security responsibilities for each party.

Key Takeaways

  • Cloud provider security refers to the methods and policies that cloud service providers use to protect their clients’ data and online applications.
  • Network security ensures all policies, protections, and practices required to protect the infrastructure, methods, and data from unauthorized access or misuse.
  • Security challenges include threats and dangers. These concerns can jeopardize the security and availability of cloud data and applications.

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

Hari Bhutanadhu
Hari Bhutanadhu

My self Bhutanadhu Hari, 2023 Graduated from Indian Institute of Technology Jodhpur ( IITJ ) . I am interested in Web Development and Machine Learning and most passionate about exploring Artificial Intelligence.

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