3-Day Training | 21-23 Nov

Applied Data Science and Machine Learning for Cyber Security [HITB+ Cyberweek 2021]

Duration 3 days
Seats Available 15
Difficulty advanced


Register Now


ATTEND ONLINE: Virtual via Zoom and LMS

DATE: 21-23 November 2021

TIME: 09:00 to 17:00 GST/GMT+4

Date Day Time Duration
21 November Sunday 09:00 to 17:00 GST/GMT+4 8 Hours
22 November Monday 09:00 to 17:00 GST/GMT+4 8 Hours
23 November Tuesday 09:00 to 17:00 GST/GMT+4 8 Hours


Post-training 30-day support by the instructor

This interactive course will teach security professionals how to use data science techniques to quickly manipulate and analyze network and security data and ultimately uncover valuable insights from this data.


The course will cover the entire data science process from data preparation, feature engineering and selection, exploratory data analysis, data visualization, machine learning, model evaluation and optimization and finally, implementing at scale—all with a focus on security related problems. Participants will learn how to read in data in a variety of common formats then write scripts to analyze and visualize that data.

Key Learning Objectives

  • Writing scripts to efficiently read and manipulate CSV, XML, and JSON files
  • Quickly and efficiently parsing executables, log files, pcap and extracting * artifacts from them
  • Making API calls to merge datasets
  • Use the Pandas library to quickly manipulate tabular data
  • Effectively visualizing data using Python
  • Preprocessing raw security data for machine learning and feature engineering
  • Building, applying and evaluating machine learning algorithms to identify potential threats
  • Automating the process of tuning and optimizing machine learning models
  • Hunting anomalous indicators of compromise and reducing false positives
  • Use supervised learning algorithms such as Random Forests, Naive Bayes, K-Nearest Neighbors (K-NN) and Support Vector Machines (SVM) to classify malicious URLs and identify SQL Injection
  • Apply unsupervised learning algorithms such as K-Means Clustering to detect anomalous behavior

Who Should Attend

This course is for anyone who wishes to incorporate automated data analysis, machine learning and data science into their work.

What Students Say About This Training

“Hands on exercises were very useful and thanks for your sharing.”

“I enjoyed seeing the different applications of ML to cybersecurity.”

Prerequisite Knowledge

Students will need to have an understanding of Python.

Hardware / Software Requirements

Students should bring a laptop with either:

  • Virtualbox (or VMWare) installed, 6GB of RAM and 10GB of storage.
  • Anaconda and IPython installed.

We strongly recommend using the virtual machine we will provide as it will give the best student experience.


Sign Up For an Account

to track your favorites

Sign Up

Want a Training Not Seen Here?

Write to Us

Contact Us