Introduction to Data Science Introduction to Cyber Security Python Basics Statements and Loops Creating Functions Data Wrangling using Pandas and NumPy Exploratory Data Analysis Summary Statistics Data Visualization Missing Value detection Outlier detection Data Transformation Splitting the data Into Training and Test Data Machine Learning for Cyber Security Data Dimensionality Reduction PCA Statistical Intuition and Implementation Segregating data using clustering Statistical Intuition and Implementation Training an XGBoost classifier Analyzing time series using statsmodels Explanation and Implementation using statsmodels Anomaly detection with Isolation Forest Explanation and Implementation Natural language processing using a hashing vectorizer and Tf-Idf Explanation and Implementation Hyperparameter tuning with scikit-optimize Implementation Generating text using Markov chains

Cybersecurity Data Science Training Course

Q: Email Cybersecurity Threats Detection

Email Cybersecurity Threats Detection Introduction to detect spam with Perceptrons Introduction to Perceptrons Introduction to spam filters Spam filter in action Detecting spam with linear classifiers How the Perceptron learns A simple Perceptron-based spam filter Pros and cons of Perceptrons Introduction to Spam detection with SVMs SVM spam filter example Introduction to Phishing detection with logistic regression and decision trees Linear regression for spam detection introduction to Logistic regression Logistic Regression Implementation Introduction to making decisions with trees Phishing detection with decision trees Spam detection with Naive Bayes NLP with Naive Bayes Implementation Malware Threat Detection Introduction to Malware detection Malware goes by many names Malware analysis tools of the trade Static malware analysis Dynamic malware analysis Hacking the PE file format Introduction of Decision tree malware detectors Malware detection with decision trees Random Forest Malware classifier Clustering malware with K-Means K-Means steps and its advantages and disadvantages Detecting metamorphic malware with HMMs Introductions Polymorphic malware detection strategies HMM Implementation

Q: Advanced malware threat detection

Advanced malware threat detection Obfuscation Detection Python Implementation Obfuscation Detection Python Explanation Tracking malware drift Implementation Tracking malware drift Explanation Network Anomaly Detection Turning service logs into datasets Introduction to classification of network attacks Detecting botnet topology Introduction to different ML algorithms for botnet detection Introduction to Gaussian anomaly detection Gaussian anomaly detection Implementation Securing Users Authentication Introduction to Authentication abuse prevention Fake login management- reactive versus predictive Account reputation scoring User authentication with keystroke recognition Introduction User authentication with keystroke recognition Implementation Biometric authentication with facial recognition Introduction Dimensionality reduction with principal component analysis (PCA) Introduction Eigenfaces Implementation

Q: Automatic intrusion detection

Automatic intrusion detection Detecting DDos Attack Credit Card fraud detection Introduction Credit Card fraud detection Implementation Counterfeit bank note detection Implementation Ad blocking using machine learning Implementation Wireless indoor localization Implementation IoT device type identification using machine learning Deepfake recognition Securing and Attacking Data with Machine Learning Assessing password security using ML ML-based steganalysis Introduction ML-based steganalysis Implementation ML attacks on PUFs Introduction ML attacks on PUFs Implementation ML attacks on PUFs Explanation HIPAA data breaches – data exploration and visualization Projects Online Transaction Fraud Detection Fake News Detection Fake Product Review Detection Spam Email Detection Credit Card Fraud Detection

Cybersecurity Data Science Training Course

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Data Science stands out as one of today’s most sought-after careers, offering a diverse and intriguing path within the IT industry. With premium packages and high demand, it captures considerable attention. Given that a significant portion of cyber-attacks involve compromising a company’s stored data for fraudulent purposes, data security lies at the core of cybersecurity. The Cybersecurity Data Science training course from Training Clicks presents an innovative method of leveraging data science to identify, prevent, and alleviate cybersecurity threats.

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Cybersecurity Data Science Course Description

Overview

In today’s IT industry, data science stands as one of the most sought-after fields, while cybersecurity has witnessed significant technological and operational advancements. Training Clicks Data Science for Cyber Security course serves as a comprehensive resource aimed at advancing your career. Delve into the fundamentals of Data Science for Cyber Security while gaining exposure to more advanced topics.

The key to achieving an automated and intelligent security system lies in extracting security incident patterns or insights from cybersecurity data and building a data-driven model accordingly. Various scientific methodologies, machine learning techniques, procedures, and systems are utilized to comprehend and analyze real-world phenomena with data. Drawing data from reputable cybersecurity sources, analytics are employed to enhance the latest data-driven patterns, thus offering more effective security solutions.

Why Data Science for Cyber Security Training with Training Clicks?

Training Clicks is a leading global provider of technology and security training and consulting services, specializing in a wide range of IT security courses and services. Our Data Science for Cyber Security training course is designed to utilize Data Science skills in Cyber Security to address security threats effectively. By choosing Training Clicks, you can access the following benefits:

We provide recorded videos after the session to each participant.
We provide post-training assistance.
We also provide a certificate of participation to each candidate.
We can help you present your qualifications and work experience for the designated profile.
We provide a flexible training schedule.

Target Audience

Individuals aspiring to pursue careers in Cyber Security and Data Science. Individuals eager to learn Data Science for Cyber Security from the ground up.

Pre-Requisites

Basic knowledge of programming languages Basic understanding of network essentials, core concepts including server and network components

Cybersecurity Data Science Course Content

Python Basics

Introduction to Data Science

Introduction to Cyber Security

Python Basics

Statements and Loops
Creating Functions
Data Wrangling using Pandas and NumPy

Exploratory Data Analysis

Summary Statistics
Data Visualization
Missing Value detection
Outlier detection
Data Transformation
Splitting the data Into Training and Test Data

Machine Learning for Cyber Security

Data Dimensionality Reduction PCA Statistical Intuition and Implementation
Segregating data using clustering Statistical Intuition and Implementation
Training an XGBoost classifier
Analyzing time series using statsmodels Explanation and Implementation using statsmodels
Anomaly detection with Isolation Forest Explanation and Implementation
Natural language processing using a hashing vectorizer and Tf-Idf Explanation and Implementation
Hyperparameter tuning with scikit-optimize Implementation
Generating text using Markov chains

Email Cybersecurity Threats Detection

Email Cybersecurity Threats Detection

Introduction to detect spam with Perceptrons
Introduction to Perceptrons
Introduction to spam filters
Spam filter in action
Detecting spam with linear classifiers
How the Perceptron learns
A simple Perceptron-based spam filter
Pros and cons of Perceptrons
Introduction to Spam detection with SVMs
SVM spam filter example
Introduction to Phishing detection with logistic regression and decision trees
Linear regression for spam detection
introduction to Logistic regression
Logistic Regression Implementation
Introduction to making decisions with trees
Phishing detection with decision trees
Spam detection with Naive Bayes
NLP with Naive Bayes Implementation

Malware Threat Detection

Introduction to Malware detection
Malware goes by many names
Malware analysis tools of the trade
Static malware analysis
Dynamic malware analysis
Hacking the PE file format
Introduction of Decision tree malware detectors
Malware detection with decision trees
Random Forest Malware classifier
Clustering malware with K-Means
K-Means steps and its advantages and disadvantages
Detecting metamorphic malware with HMMs Introductions
Polymorphic malware detection strategies
HMM Implementation

Advanced malware threat detection

Advanced malware threat detection

Obfuscation Detection Python Implementation
Obfuscation Detection Python Explanation
Tracking malware drift Implementation
Tracking malware drift Explanation

Network Anomaly Detection

Turning service logs into datasets
Introduction to classification of network attacks
Detecting botnet topology
Introduction to different ML algorithms for botnet detection
Introduction to Gaussian anomaly detection
Gaussian anomaly detection Implementation

Securing Users Authentication

Introduction to Authentication abuse prevention
Fake login management- reactive versus predictive
Account reputation scoring
User authentication with keystroke recognition Introduction
User authentication with keystroke recognition Implementation
Biometric authentication with facial recognition Introduction
Dimensionality reduction with principal component analysis (PCA) Introduction
Eigenfaces Implementation

Automatic intrusion detection

Automatic intrusion detection

Detecting DDos Attack
Credit Card fraud detection Introduction
Credit Card fraud detection Implementation
Counterfeit bank note detection Implementation
Ad blocking using machine learning Implementation
Wireless indoor localization Implementation
IoT device type identification using machine learning
Deepfake recognition

Securing and Attacking Data with Machine Learning

Assessing password security using ML
ML-based steganalysis Introduction
ML-based steganalysis Implementation
ML attacks on PUFs Introduction
ML attacks on PUFs Implementation
ML attacks on PUFs Explanation
HIPAA data breaches – data exploration and visualization

Projects

Online Transaction Fraud Detection
Fake News Detection
Fake Product Review Detection
Spam Email Detection

Credit Card Fraud Detection

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Cybersecurity Data Science Training Course

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Benefits of Getting Training Clicks Certified

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Career Advancement

Employer Confidence

Cybersecurity Data Science Course Description

Target Audience

Pre-Requisites

Cybersecurity Data Science Course Content

Need Customized Curriculum?

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Related Courses

Review's Of Clients

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