Rate My Professor Nickolaos Koroniotis

Koroniotis's research specializations include cybersecurity, artificial intelligence, Internet of Things security, network forensics, and detection methods for network-based attacks. He develops deep learning-based vulnerability assessment solutions for smart environments, with case studies in smart airports and Internet of Battlefield Things. He is the lead creator of the Bot-IoT dataset, detailed in his seminal 2019 publication 'Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset' (Future Generation Computer Systems, cited over 2340 times). Other major publications encompass 'Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions' (IEEE Communications Surveys & Tutorials, 2023, cited over 315 times), 'A New Network Forensic Framework based on Deep Learning for Internet of Things Networks: A Particle Deep Framework' (Future Generation Computer Systems, 2020, cited over 309 times), 'A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports' (IEEE Access, 2020), 'An Explainable Deep Learning-Enabled Intrusion Detection Framework in IoT Networks' (Information Sciences, 2023), and recent works such as 'Generative Fuzzer-Driven Vulnerability Detection in the Internet of Things Networks' (Applied Soft Computing, 2025) and 'FedCAD: Federated Cyberattack Attribution Detection for Internet of Things Forensics' (IEEE Transactions on Sustainable Computing, 2025). His research has amassed over 4274 citations on Google Scholar, demonstrating substantial impact in network forensics and IoT cybersecurity. Koroniotis continues to advance forensic frameworks and intrusion detection systems applicable to critical infrastructures.