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Build 3 Network Applications with Python and Machine Learning
SECTION 1: Getting Started
What's This Course All About? (4:19)
Join My PRIVATE PYTHON CLASSROOM!
Installing VirtualBox and Kali Linux (2:07)
Troubleshooting Kali Linux in VirtualBox (0:26)
Installing Python and Jupyter Notebook (2:48)
Best Way of Going Forward (0:52)
SECTION 2: Learning Python From Scratch
Introduction to Jupyter Notebook (6:39)
Quick Note on the Concepts in this Section (1:04)
Variables. Data Types. Keywords (4:33)
Strings. String Operations (11:16)
Strings. Additional Tip 1 (1:57)
Strings. Additional Tip 2 (1:52)
Strings. Additional Tip 3 (2:33)
Strings. Additional Tip 4 (3:34)
Numbers. Numeric Operations (4:52)
Numbers. Additional Tip (1:46)
Booleans. Boolean Operations (5:43)
Lists. List Operations (8:57)
Sets. Set Operations (6:45)
Tuples. Tuple Operations (5:56)
Ranges. Range Operations (5:29)
Dictionaries. Dictionary Operations (7:24)
Dictionaries. Additional Tip 1 (1:47)
Dictionaries. Additional Tip 2 (3:27)
If-Elif-Else Conditionals (9:20)
For-Else Loops (9:38)
While-Else Loops (9:07)
List-Set-Dictionary Comprehensions (6:47)
Break. Continue. Pass (4:04)
Try. Except. Else. Finally (10:32)
Exceptions. Additional Tip 1 (4:06)
Exceptions. Additional Tip 2 (3:46)
Functions. Parameters. Arguments (8:41)
Functions. Additional Tip 1 (1:39)
Functions. Additional Tip 2 (3:27)
Namespaces. Modules (9:33)
File Operations (8:49)
Regular Expressions (10:28)
Regular Expressions. Additional Tip (2:54)
Additional Useful Python Concepts (10:42)
Special Functions: Map. Filter. Reduce. Lambda (12:21)
Special Functions: Zipping and Unzipping Lists (7:39)
DOWNLOAD: Regular Expressions Notebook
DOWNLOAD: Python Primer Notebook
PRACTICE: Multiple-Choice Quizzes
PRACTICE: Coding Exercises
SECTION 3: Basic-to-Intermediate Pandas
Introduction to Pandas. Basic Operations (11:17)
Intermediate to Advanced Pandas Operations (12:06)
Handling CSV Files with Pandas (9:48)
SAMPLE: CSV File for Visualization
Pandas and Data Visualization (17:18)
[EXTRA] Pandas Series
DOWNLOAD: Pandas Notebook
SECTION 4: Building a Network Vulnerability Map
Application Development Plan (1:55)
Installing the Necessary Libraries (1:28)
Installing the Ubuntu VMs in VirtualBox (4:19)
Preparing the Network Hosts: Host 1 (6:47)
Preparing the Network Hosts: Host 2 (2:14)
Preparing the Network Hosts: Host 3 (1:56)
VirtualBox Guest Additions for Ubuntu
Re-Adding All Devices to the LAN (1:53)
Important Note on Network Scanning (0:46)
Introduction to NMAP Scanning (10:06)
The World of NMAP NSE Scripts (9:55)
DOWNLOAD: NMAP Notebook
Defining the Initial Variables and Actions (11:13)
Parsing the Scan Results per Host (10:33)
Extracting the Number of Exploits Available (11:06)
Writing Code for Brute-Force Attacks (9:14)
SAMPLE: Username/Password File
Organizing the Scan and Attack Data (8:41)
Plotting the Hosts and Data in the Final Graph (13:09)
Sending the Vulnerability Data via Email (6:50)
Recap of Full Application Code (3:43)
Testing the Application on the Network (2:49)
Testing the Application on an Extended Network (4:24)
Automating the Scan on a Daily Basis (3:28)
DOWNLOAD APPLICATION 1: Full Code
SECTION 5: Unsupervised ML in Networking
Application Development Plan (2:00)
Introduction to Machine Learning (6:31)
K-Means Clustering Basics (12:16)
Goals. Preparing the Data Set (5:22)
DOWNLOAD: Sample Data Set
Analyzing the ML Python Code (9:15)
Note on the Core ML Functionality
Testing the ML Model on the Data Set (4:48)
Adding Correlation Functionality (3:52)
Testing the Correlation Feature (2:36)
DOWNLOAD APPLICATION 2: Full Code
Brute-Force Attacks in Traffic Captures (6:08)
Using Machine Learning on PCAP Files
Analyzing the ML Python Code (15:02)
Note on the Core ML Functionality
DOWNLOAD: Sample PCAP File
DOWNLOAD APPLICATION 3: Full Code
[EXTRA] Using the DBSCAN ML Algorithm (8:31)
[EXTRA] DOWNLOAD: DBSCAN Code
[EXTRA] Using the Hierarchical Clustering ML Algorithm (10:53)
[EXTRA] DOWNLOAD: Hierarchical Clustering Code
SECTION 6: Supervised ML in Networking
Application Development Plan (2:31)
What are Decision Trees in ML (8:01)
The Random Forest Algorithm (8:17)
Generating Data for Training and Predictions (16:11)
DOWNLOAD: Code for Data Generation
Analyzing the ML Python Code: Splitting & Training (11:12)
Summary of Splitting the Train-Test Data
Analyzing the ML Python Code: Loading & Executing (9:22)
[EXTRA] Analyzing Feature Importance (2:44)
Testing the Application on the New Data (7:50)
DOWNLOAD: ML with Feature Importance Code
Generating nmap -sV Traffic for Training & Predicting (4:55)
DOWNLOAD: Training Data CSV & Prediction PCAP
Analyzing the ML Python Code: Extracting Data (5:53)
DOWNLOAD: Code for Converting PCAP Files to CSV
Analyzing the ML Python Code: Training the Model (5:09)
Analyzing the ML Python Code: Making Predictions (4:13)
Testing the Application on a New Traffic Capture (2:34)
DOWNLOAD BONUS APPLICATION: Full Code
Network Engineering: Supervised vs. Unsupervised ML (3:00)
Final Notes on ML Applications (2:54)
Closing Thoughts (0:48)
Let's Connect!
Testing the ML Model on the Data Set
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