Skip to main content

CS6035 Intro To Info Security

· 3 min read

Lectures are optional. This course is projects based: 7 mandatory projects, 1 bonus projects. Overall, this is ha ands-on course. I practiced my skills across various topics about software securities.

Projects, score percentage and its spent time

Man in the Middle 13% - 11 hrs

In this project, we need to analyze the Wireshark captured network packages to do Internet Relay Chat(IRC) analysis, manually and programatically via PyShark, The traffic may involve TCP, DNS, HTTP, IRC, etc.

We may use CyberChef to decipher some code.

Database Security 13% - 12 hrs, 5 hrs review lectures

We will analyze SQL injection, Database, Spreadsheet information leak.

Malware Analysis 13% - 7.5 hrs

Here we analyze various malware reports: including:

  • Data obfuscation
  • Defense evasion
  • Network indicators
  • Host based indicators
  • Malware family associations
  • Data theft and exfiltration
  • Persistence mechanisms

API Security 13% - 8 hrs

We will try to exploit REST API for information. The topics covered:

Cryptography 16% - 13 hrs

Using Python to study cryptography and symmetric and asymmetric crypto algorithms.

Binary Exploitation 16% - 11 hrs

In this project, we're using C Code to exploit C Memory handling with respect to Stack , Heap via pwndbg and GDB .

Background:

Binary and Hexadecimal Numbering Systems

ASCII Text

Capture The Flag style competition

Log4Shell 16% - 7 hrs

We're using JNDI/LDAP knowledge in Java and exploit via

https://github.gatech.edu/pages/cs6035-tools/cs6035-tools.github.io/Projects/Log4Shell/

[NIST CVE Overview] [Randori: What is Log4Shell]

Log4Shell Reference Materials

Machine Learning in Cybersecurity 2.5% - 0.5 hr

Learning Goals of this Project

  • Learning Basic Pandas Dataframe Manipulations
  • Learning more about Machine Learning (ML) Classification models and how they are used in a Cybersecurity Context.
  • Learning about basic Data pipelines and Transformations
  • Learning how to write and use Unit Tests when developing Python code

ML Reference Materials