Dong Seong Kim

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Associate Professor

University of Queensland

Australia

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About

Dong Seong Kim is an Associate Professor at The University of Queensland, Australia. His research areas include cybersecurity risk assessment, Internet of Things (IoT) security, and adversarial defense mechanisms. He has authored several recent articles focusing on cybersecurity issues related to autonomous driving and extended reality applications, as well as developing innovative frameworks for threat analysis and simulation in IoT environments.

Recent Grants

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Security Risk Modeling and Assessment of Cloud Computing

Open Date: 2016-05-01

Close Date: 2019-04-30

Articles (20)

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Collaborators (11)

Jennifer Fleming

University of Queensland

AUSTRALIA

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Mengmeng Ge

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Dong Seong Kim

Research Interests

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About

Recent Grants

Articles (20)

MTD-AD: Moving Target Defense as Adversarial Defense

Model-based structural and behavioural cybersecurity risk assessment in system designs

Unveiling the evolution of IoT threats: Trends, tactics, and simulation analysis

MTD-AD: Moving Target Defense as Adversarial Defense

Securing End-to-End Reinforcement Learning-Driven Autonomous Driving: A Control Command Utility-based Intrusion Response System

Cyber Security and Privacy Issues in Extended Reality Healthcare Applications: Scoping Review (Preprint)

HD-FUZZ: Hardware dependency-aware firmware fuzzing via hybrid MMIO modeling

NIDS-Vis: Improving the generalized adversarial robustness of network intrusion detection system

Graphical security modelling for Autonomous Vehicles: A novel approach to threat analysis and defense evaluation

IoTSecSim: A framework for modelling and simulation of security in Internet of things

Time-Based Moving Target Defense Using Bayesian Attack Graph Analysis

A Survey on Cyber Situation-awareness Systems: Framework, Techniques, and Insights

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

DIVERGENCE: Deep Reinforcement Learning-Based Adaptive Traffic Inspection and Moving Target Defense Countermeasure Framework

Evaluating the Security and Economic Effects of Moving Target Defense Techniques on the Cloud

Performability evaluation of switch-over Moving Target Defence mechanisms in a Software Defined Networking using stochastic reward nets

DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework

Panop: Mimicry-Resistant ANN-Based Distributed NIDS for IoT Networks

Privacy-Preserving Reinforcement Learning Using Homomorphic Encryption in Cloud Computing Infrastructures

Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

Collaborators (11)

Jennifer Fleming

Mengmeng Ge

Harith Al-Sahaf

Hooman Alavizadeh

Julian Jang-Jaccard

Joobeom Yun

Muhammad Rizwan Asghar

Nilufar Baghaei

Guangdong Bai

Seunghyun Yoon

Hyuk Lim

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