Danielle Hanson

Matthew Tassava, Cameron Kolodjski, Jordan Milbrath et al.

This paper presents and evaluates work on the development of an artificial intelligence (AI) anti-bullying system. The system is designed to identify coordinated bullying attacks via social media and other mechanisms, characterize them and propose remediation and response activities to them. In particular, a large language model (LLM) is used to populate an enhanced expert system-based network model of a bullying attack. This facilitates analysis and remediation activity - such as generating report messages to social media companies - determination. The system is described and the efficacy of the LLM for populating the model is analyzed herein.

Danielle Hanson, Jeremy Straub

The use of Internet of Things (IoT) devices is growing at a rapid rate. While much of this growth is consumer devices, IoT devices are also commonly found in corporate and industrial environments, as well. These devices can be organization-owned and managed by an information technology unit, deployed organizationally without the knowledge and involvement of technology staff or brought in to the corporate environment by user-owners. In each case, these devices may have access to corporate networks and data and are, thus, important to consider as part of organizational cybersecurity risk assessment. Despite the prevalence of these devices, there is little literature about how to audit their security. This paper presents a risk-based auditing framework which can be used by both internal and external auditors, of any experience level and in any industry, to assess IoT devices.