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Artificial Intelligence for Cybersecurity

(AI-CYBSEC.AJ1) / आईएसबीएन : 978-1-64459-483-4
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इस कोर्स के बारे में

आज के तेजी से विकसित हो रहे डिजिटल परिदृश्य में, कृत्रिम बुद्धिमत्ता (AI) और साइबर सुरक्षा का मिलन लगातार बढ़ते साइबर खतरों से संगठनों की सुरक्षा के लिए महत्वपूर्ण है। यह पाठ्यक्रम आपको साइबर सुरक्षा उपायों को बढ़ाने के लिए AI तकनीकों का लाभ उठाने के लिए आवश्यक ज्ञान और कौशल से लैस करने के लिए डिज़ाइन किया गया है। यह कोर्स आपको आर्टिफिशियल इंटेलिजेंस के मूल सिद्धांतों और साइबर सुरक्षा में इसके अनुप्रयोगों को समझने में मदद करेगा।

कौशल जो आपको प्राप्त होंगे

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पाठ

11+ पाठ | 155+ अभ्यास | 105+ प्रश्नोत्तरी | 63+ फ़्लैशकार्ड | 63+ पारिभाषिक शब्दावली

टेस्टप्रेप

50+ पूर्व मूल्यांकन प्रश्न | 50+ मूल्यांकन के बाद के प्रश्न |

व्यावहारिक प्रयोगशालाएँ

27+ लाइवलैब | 00+ Minutes

पाठ्यक्रम रूपरेखा डाउनलोड करें

1

Preface

  • Who this course is for
  • What this course covers
2

Introduction to AI for Cybersecurity Professionals

  • Applying AI in cybersecurity
  • Evolution in AI: from expert systems to data mining
  • Types of machine learning
  • Algorithm training and optimization
  • Getting to know Python's libraries
  • AI in the context of cybersecurity
  • Summary
3

Setting Up Your AI for Cybersecurity Arsenal

  • Getting to know Python for AI and cybersecurity
  • Python libraries for cybersecurity
  • Enter Anaconda – the data scientist's environment of choice
  • Playing with Jupyter Notebooks
  • Installing DL libraries
  • Summary
4

Ham or Spam? Detecting Email Cybersecurity Threats with AI

  • Detecting spam with Perceptrons
  • Spam detection with SVMs
  • Phishing detection with logistic regression and decision trees
  • Spam detection with Naive Bayes
  • NLP to the rescue
  • Summary
5

Malware Threat Detection

  • Malware analysis at a glance
  • Telling different malware families apart
  • Decision tree malware detectors
  • Detecting metamorphic malware with HMMs
  • Advanced malware detection with deep learning
  • Summary
6

Network Anomaly Detection with AI

  • Network anomaly detection techniques
  • How to classify network attacks
  • Detecting botnet topology
  • Different ML algorithms for botnet detection
  • Summary
7

Securing User Authentication

  • Authentication abuse prevention
  • Account reputation scoring
  • User authentication with keystroke recognition
  • Biometric authentication with facial recognition
  • Summary
8

Fraud Prevention with Cloud AI Solutions

  • Introducing fraud detection algorithms
  • Predictive analytics for credit card fraud detection
  • Getting to know IBM Watson Cloud solutions
  • Importing sample data and running Jupyter Notebook in the cloud
  • Evaluating the quality of our predictions
  • Summary
9

GANs - Attacks and Defenses

  • GANs in a nutshell
  • GAN Python tools and libraries
  • Network attack via model substitution
  • IDS evasion via GAN
  • Facial recognition attacks with GAN
  • Summary
10

Evaluating Algorithms

  • Best practices of feature engineering
  • Evaluating a detector's performance with ROC
  • How to split data into training and test sets
  • Using cross validation for algorithms
  • Summary
11

Assessing your AI Arsenal

  • Evading ML detectors
  • Challenging ML anomaly detection
  • Testing for data and model quality
  • Ensuring security and reliability
  • Summary

1

Introduction to AI for Cybersecurity Professionals

  • Creating a Linear Regression Model
  • Creating a Clustering Model
  • Using Neural Networks for Spam Filtering
2

Setting Up Your AI for Cybersecurity Arsenal

  • Performing Matrix Operations
  • Using a Linear Regression Model for Prediction
3

Ham or Spam? Detecting Email Cybersecurity Threats with AI

  • Creating a Perceptron-based Spam Filter
  • Creating an SVM Spam Filter
  • Creating a Phishing Detector with Logistic Regression
  • Creating a Phishing Detector with Decision Trees
  • Creating a Spam Detector with NLTK
4

Malware Threat Detection

  • Using the k-Means Clustering Algorithm for Malware Detection
  • Creating a Decision Tree and a Random Forest Malware Classifier
  • Detecting Malware using an HMM Model
5

Network Anomaly Detection with AI

  • Detecting Botnet
  • Performing Gaussian Anomaly Detection
6

Securing User Authentication

  • Detecting Anomaly Using Keystrokes
  • Creating an Image Classification Model
  • Understanding Covariance Matrix
7

Fraud Prevention with Cloud AI Solutions

  • Performing Oversampling and Undersampling
  • Comparing Different Models for Detecting Credit Card Frauds
9

Evaluating Algorithms

  • Performing Feature Normalization
  • Dealing with Categorical Data
  • Using Different Measures to Evaluate Algorithms
  • Creating a Learning Curve to Measure Performance of an Algorithm
  • Performing K-Folds Cross Validation
10

Assessing your AI Arsenal

  • Handling Missing Values in a Dataset
  • Performing Hyperparameter Optimization

Artificial Intelligence for Cybersecurity

$279.99

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