Summary of Inherently Interpretable and Uncertainty-aware Models For Online Learning in Cyber-security Problems, by Benjamin Kolicic et al.

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems

by Benjamin Kolicic, Alberto Caron, Chris Hicks, Vasilios Mavroudis

First submitted to arxiv on: 14 Nov 2024

GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A novel pipeline is proposed for online supervised learning in cyber-security, combining Additive Gaussian Processes (AGPs) with interpretability and uncertainty quantification to improve trustworthiness. AGPs are shown to balance predictive performance with transparency, enabling security analysts to validate threat detection, troubleshoot false positives, and make informed decisions. The approach aims to scale up AGPs’ scalability limitations, making it a valuable contribution to the interpretable AI field for high-stake decision problems.
Low	GrooveSquid.com (original content)	Low Difficulty Summary In this paper, researchers create a new way to use machine learning models in cyber-security. These models can predict if something is a threat or not, but they’re hard to understand and don’t show how sure they are about their answers. The team proposes using Additive Gaussian Processes (AGPs) which are more transparent and can quantify uncertainty. This helps security analysts make better decisions by validating threat detection, fixing false positives, and making informed choices.