Automated Attack Hypothesis and Testflow Generation

In the field of cybersecurity, it is increasingly important to actively find and stop security threats, a process called threat hunting. The threat hunting is often performed manually, which is a tough process the requires deep knowledge, lots of experience, and time, which can lead to missed attacks and slow responses, affecting companies and countries. This research suggests using an automated system to make threat hunting faster and more efficient. The goal is to change how threat hunting is performed by using a system that automatically creates hypotheses and test plans. It will improve how quickly and accurately threats are found and dealt with, making cybersecurity stronger. The plan is to build a new system using advanced techniques to automatically suggest possible threats from network data and user activities, then create test plans to check these threats. This system will mix methods to understand complex data and use machine reasoning to think like humans. The aim is to create a smart system that adjusts to new cyber threats…

Faculty Supervisor:

Mourad Debbabi

Student:

Partner:

Ericsson Canada Inc (Quebec)

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Concordia University

Program: