Thesis Statement:
An AI system using Semantic Technologies can be implemented to mimic human cybersecurity expert’s classification of the malicious profile of wireless access points.
Scope of Work:
The student will implement a system that classifies RAP maliciousness using Knowledge Representation and Reasoning (KR&R) techniques that mimic those of cybersecurity experts. This will require knowledge elicitation from experts and their documentation. Once elicited, the expertise will be represented in ontological languages and persisted in semantic graphs.
The AI system implemented by the student will be used to continuously monitor an enterprise’s wireless access points. Description Logics reasoners following the formal logic in the ontologies will continuously watch the access points. When an access point appears as or morphs into, a Rogue Access Point alerts will be issued.
Ramifications:
Rogue Access Points are but one of many challenges within the domain of cybersecurity. The opportunity to expand the capabilities of a cybersecurity AI system such as the one that will be implemented are seemly limitless.
Supervision:
This project is in collaboration with the cybersecurity firm DarkLight Inc. (https[:]//www.darklight.ai/)
The student will be co-supervised by Vasileios Mavroeidis - Security Researcher at UiO, Nils Gruschka - Associate Professor at UiO, and Ryan Hohimer - CTO of DarkLight
The student needs to have the resolve to work methodically and possibly produce a joint scientific publication from the conducted work.
