MissKateHighnam

My research interests include (but definitely not limited to), in no particular order:

• machine learning
• cyber security
• causal methods/inference/discovery
• anomaly detection
• domain adaptation/concept drift
• honeypots/honeynets
• intrusion/threat detection systems
• network/information security
• transfer learning
• multi-task learning
• transfer of knowledge
• embedded systems
• continual learning

This page includes all publications and content that might not have proceedings still available online to record in formal publication systems.

Adaptive Experimental Design for Intrusion Data Collection

[POSTER] [PAPER] - ESORICS 2023

[PAPER] TBD - CAMLIS 2023

Kate Highnam, zachary Hanif, Ellie Van Vogt, Sonali Parbhoo, Sergio maffeis, Nicholas R. Jennings 

Autonomous network defence using reinforcement learning

[POSTER] [PAPER] - AsiaCCS 2022 (Best Poster Award Winner)

myles Foley, chris hicks, Kate highnam, vasilios mavroudis

BETH Dataset: Real Cyber security data for Anomaly detection Research

[TALK] [PAPER]  - TBD

Kate Highnam, Kai arulkumaran, zachary Hanif, Nicholas R. Jennings [2020]

• CAMLIS 2021

[WORKSHOP PAPER]

Kate Highnam, Kai arulkumaran, zachary Hanif, Nicholas R. Jennings [2020]

• ICML 2021 Workshop - Uncertainty and Robustness in Deep Learning

GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability

[Workshop paper]

Daniel Lengyel, Janith Petangoda, Isak Falk, Kate Highnam, Michalis Lazarou, Arinbjörn Kolbeinsson, Marc Peter Deisenroth, Nicholas R. Jennings [2020]

• NeurIPS 2020 Workshop - Differential Geometry for Deep Learning

Deep Learning for Real-time Malware Detection

[KEYNOTE/TALK/Journal]

Domenic Puzio, Kate Highnam, Song Luo [2018]

• Deep Learning World Keynote 2018
• SchmooCon 2018
• Australian Cyber Security Centre Conference 2018

Kate Highnam, Domenic Puzio, Song Luo, Nicholas R. Jennings [2021]

• SN Computer Science

Providing prioritization score for security analysts to process potentially malicious domains.

An Uncrewed Aerial Vehicle Attack Scenario and Trustworthy Repair Architecture [PDF]

Kate Highnam, Kevin Angstadt, Kevin Leach, Westley Weimer, Aaron Paulos, Patrick Hurley

• 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W)

With the growing ubiquity of uncrewed aerial vehicles (UAVs), mitigating emergent threats in such systems has become increasingly important. In this short paper, we discuss an indicative class of UVAs and a potential attack scenario in which a benign UAV completing a mission can be compromised by a malicious attacker with an antenna and a commodity computer with open-source ground station software. We attest to the relevance of such a scenario for both enterprise and defense applications. We describe a system architecture for resiliency and trustworthiness in the face of these attacks. Our system is based on the quantitative assessment of trust from domain-specific telemetry data and the application of program repair techniques to UAV flight plans. We conclude with a discussion of restoring trust in post-repair UAV mission integrity.

Dictionary DGA detector model

Abstract

Systems and methods are provided for detecting dictionary domain generation algorithm domain names using deep learning models. The system and method may comprise training and applying a model comprising a long short-term memory network, a convolutional neural network, and a feed forward neural network that accepts as input an output from the long short-term memory network and convolutional neural network. The system and method may provide a score indicating the likelihood that a domain name was generated using a dictionary domain generation algorithm domain name. The system and method may be provided as a service.