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Nickolaos Koroniotis
Researcher at University of New South Wales
Publications - 14
Citations - 1099
Nickolaos Koroniotis is an academic researcher from University of New South Wales. The author has contributed to research in topics: Botnet & Network forensics. The author has an hindex of 6, co-authored 10 publications receiving 413 citations. Previous affiliations of Nickolaos Koroniotis include Cooperative Research Centre & Australian Defence Force Academy.
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Journal ArticleDOI
Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
TL;DR: In this paper, the authors proposed a new dataset, called Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks, and evaluated the reliability of the dataset using different statistical and machine learning methods for forensics purposes.
Journal ArticleDOI
A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework
TL;DR: This study proposes a new network forensics framework, called a Particle Deep Framework (PDF), which describes the digital investigation phases for identifying and tracing attack behaviors in IoT networks, and results reveal a high performance of the proposed framework for discovering and tracing cyber-attack events compared with the other techniques.
Journal ArticleDOI
Forensics and Deep Learning Mechanisms for Botnets in Internet of Things: A Survey of Challenges and Solutions
TL;DR: A new definition for the IoT is provided, in addition to a taxonomy of network forensic solutions, that were developed for both conventional, as well as, the IoT settings and the applicability of deep learning in network forensics is investigated.
Book ChapterDOI
Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques
TL;DR: Investigating the role of ML techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets attacks and their tracks.
Journal ArticleDOI
A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports
TL;DR: A holistic review of existing smart airport applications and services enabled by IoT sensors and systems is presented, and several types of cyber defence tools including AI and data mining techniques are investigated, and their strengths and weaknesses are analysed in the context of smart airports.