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Open to AI related Platform Engineer, DevOps, SRE Roles in Seattle(US) or Vancouver(BC) • Available for onsite/hybrid.

CS6035 Intro To Info Security

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

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