Dr. Naghmeh Moradpoor
Edinburgh Napier University
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Academic Career Length
16Years (2011-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
YYYagmur Yigit
Yagmur Yigit
Mohamed Amine Ferrag
Mohamed Chahine Ghanem
Iqbal H. Sarker
Leandros MaglarasHJHelge Janicke
Helge Janicke +3
Critical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and introduce established benchmarks for evaluating Large Language Models (LLMs) within cybersecurity contexts. Next, we explore core cybersecurity issues, focusing on trust, privacy, resilience, and securability in these vital systems. Building on this foundation, we assess the role of Generative AI and LLMs in enhancing CIP and present insights on applying Agentic AI for proactive defense mechanisms. Finally, we outline future directions to guide the integration of advanced AI methodologies into protecting critical infrastructures. Our paper provides a strategic roadmap for researchers and practitioners committed to fortifying national infrastructures against emerging cyber threats through this synthesis of current challenges, benchmarking strategies, and innovative AI applications.
20 February 2020 in Security and Communication Networks
Journal Article
One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.
1 July 2017
Conference Paper
A phishing email is a legitimate-looking email which is designed to fool the recipient into believing that it is a genuine email, and either reveals sensitive information or downloads malicious software through clicking on malicious links contained in the body of the email. Given that phishing emails cost UK consumers £174m in 2015, this paper proposal is driven by a problem whose resolution will have a great impact on people's lives in the UK and in the world. In this paper, we proposed a Neural Network (NN)-based model for detections and classifications of phishing emails using publically available email datasets for both benign and phishing emails. The results of the experiments are presented in order to demonstrate the effectiveness of the model in terms of accuracy, true-positive rate, false-positive rate, network performance and error histogram.
25 June 2022 in Computational Intelligence and Neuroscience
Journal Article
LALulwah M. Al-Harigy
Lulwah M. Al-HarigyHAHana Alnuaim
Hana AlnuaimNaghmeh Moradpoor
Zhiyuan TanThe increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored.
1 June 2018
Conference Paper
ARAndres Robles-Durazno
Andres Robles-DuraznoNaghmeh MoradpoorJMJames McWhinnie
James McWhinnie
GRGordon Russell
Gordon Russell
Industrial Control Systems are part of our daily life in industries such as transportation, water, gas, oil, smart cities, and telecommunications. Technological development over time have improved their components including operating system platforms, hardware capabilities, and connectivity with networks inside and outside the organization. Consequently, the Industrial Control Systems components are exposed to sophisticated threats with weak security mechanism in place. This paper proposes a supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system. A testbed of such a system is implemented using the Festo MPA Control Process Rig. The machine-learning algorithms, which include SVN, KNN, and Random Forest, perform classification tasks process in three different datasets obtained from the testbed. The algorithms are compared in terms of accuracy and F-measure. The results show that Random Forest achieves 5% better performance over KNN and SVM with small datasets and 4% regarding large datasets. For the time taken to build the model, KNN presents the best performance. However, its difference with Random Forest is smaller than with SVM.
Journal Article
ARAndres Robles-Durazno
Andres Robles-DuraznoNaghmeh MoradpoorJMJames McWhinnie
James McWhinnieGRGordon Russell
Gordon RussellJPJorge Porcel-Bustamante
Jorge Porcel-BustamanteIndustrial Control Systems are an essential part of our daily lives and can be found in industries such as oil, utilities, and manufacturing. Rapid growth in technology has introduced industrial components with network capabilities that allow them to communicate with traditional computer networks, thus increasing their exposure to cyber-attacks. Current research on Industrial Control Systems suffer from lack of technical information as these systems are part of critical infrastructures. To overcome this, researchers have employed different types of testbeds to develop their mechanisms of cyber-attack detection and prevention. This manuscript describes, implements, and evaluates physical, hybrid, and virtual application of a clean water supply system developed for cybersecurity research. The results show that physical testbeds allow an understanding of the behaviour and dynamics of control components like sensors and actuators, which might be affected by external influences such as noise, vibration, temperature, and non-ideal device behaviour. Although, hybrid testbeds reduce the cost of implementation, they ignore the physical dynamics of the system as explained above. Virtual testbeds are the cheapest option in comparison with physical and hybrid testbeds; however, they provide a limited view of the control system operation that could have negative consequences when developing a detection/prevention system.
About Me
Dr. Naghmeh Moradpoor is an Associate Professor of
Cybersecurity in the School of Computing, Engineering, and the Built
Environment at Edinburgh Napier University. She is a Fellow of the Higher
Education Academy, a Senior Member of IEEE, a Certified Ethical Hacker, and has
been an active researcher since 2009.Naghmeh's research expertise includes the security analysis
and countermeasures for Industrial Control Systems and Critical Infrastructure
Protection, the detection and classification of various cybersecurity attacks,
data analytics and decision support for cybersecurity, IDs/IPs for IoT,
cyberbullying detection and prevention, inter-vehicular trust in autonomous
vehicles using Distributed Ledger Technology, and cybersecurity for Electric
Vehicles and Electric Vehicle Charging Stations.In recognition of her contributions, Naghmeh was awarded
Outstanding Woman in Cyber at the Scottish Cyber Awards in 2017. She was also
nominated for the Best Supervisor for Research category by ENSA’s
Student-Nominated Excellence Awards in 2021 and 2024. Additionally, Naghmeh is
ranked among the top 2% (top one million) most-cited authors worldwide,
according to the AD Scientific Index 2023.
She is an Academic Editor for the Journal of Security and
Communication Networks, an Associate Editor for the Journal of Ambient
Intelligence and Humanized Computing, an Academic Editor for IET Information
Security, and a Guest Editor for Computational Intelligence and Network
Security.