no code implementations • 27 Feb 2024 • Tosin Ige, Christopher Kiekintveld, Aritran Piplai
The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack.
no code implementations • 26 Feb 2024 • Tosin Ige, Christopher Kiekintveld, Aritran Piplai
In this research, we analyzed the suitability of each of the current state-of-the-art machine learning models for various cyberattack detection from the past 5 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack.
no code implementations • 28 Nov 2023 • David Milec, Viliam Lisý, Christopher Kiekintveld
Attackers in the real world are predominantly human actors, and the protection methods often incorporate opponent models to improve the performance when facing humans.
no code implementations • 22 Aug 2023 • Tosin Ige, Christopher Kiekintveld
Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world application.
no code implementations • 1 Jan 2021 • Anthony Ortiz, Kris Sankaran, Olac Fuentes, Christopher Kiekintveld, Pascal Vincent, Yoshua Bengio, Doina Precup
In this work we tackle the problem of out-of-distribution generalization through conditional computation.
1 code implementation • CVPR 2020 • Anthony Ortiz, Caleb Robinson, Dan Morris, Olac Fuentes, Christopher Kiekintveld, Md Mahmudulla Hassan, Nebojsa Jojic
In many vision applications the local spatial context of the features is important, but most common normalization schemes including Group Normalization (GN), Instance Normalization (IN), and Layer Normalization (LN) normalize over the entire spatial dimension of a feature.