405 citations · 937 across the 18 of their papers we have counts for
21 papers
A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions
Mohammed Hassanin, Nour Moustafa
The recent progression of Large Language Models (LLMs) has witnessed great success in the fields of data-centric applications. LLMs trained on massive textual datasets showed abili…
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection
Mohanad Sarhan, Siamak Layeghy, Nour Moustafa +1
The uses of Machine Learning (ML) in detection of network attacks have been effective when designed and evaluated in a single organisation. However, it has been very challenging to…
A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments
Nickolaos Koroniotis, Nour Moustafa, Benjamin Turnbull +3
The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, resulting in innovations like Industry 4.0 and smart environments that provide improvements t…
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks
Mohanad Sarhan, Siamak Layeghy, Nour Moustafa +2
A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network interr…
Hunter in the Dark: Discover Anomalous Network Activity Using Deep Ensemble Network
Shiyi Yang, Hui Guo, Nour Moustafa
Machine learning (ML)-based intrusion detection systems (IDSs) play a critical role in discovering unknown threats in a large-scale cyberspace. They have been adopted as a mainstre…
Security and Privacy for Artificial Intelligence: Opportunities and Challenges
Ayodeji Oseni, Nour Moustafa, Helge Janicke +3
The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securi…
Mitigating the Impact of Adversarial Attacks in Very Deep Networks
Mohammed Hassanin, Ibrahim Radwan, Nour Moustafa +2
Deep Neural Network (DNN) models have vulnerabilities related to security concerns, with attackers usually employing complex hacking techniques to expose their structures. Data poi…
A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models
Mohammed Hassanin, Nour Moustafa, Murat Tahtali
Deep learning algorithms have been recently targeted by attackers due to their vulnerability. Several research studies have been conducted to address this issue and build more robu…
NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems
Mohanad Sarhan, Siamak Layeghy, Nour Moustafa +1
Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have proven to become a reliable intelligence tool to protect networks against cyberattacks. Network data fe…
Federated TON_IoT Windows Datasets for Evaluating AI-based Security Applications
Nour Moustafa, Marwa Keshk, Essam Debie +1
Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intell…
Data Analytics-enabled Intrusion Detection: Evaluations of ToN_IoT Linux Datasets
Nour Moustafa, Mohiuddin Ahmed, Sherif Ahmed
With the widespread of Artificial Intelligence (AI)- enabled security applications, there is a need for collecting heterogeneous and scalable data sources for effectively evaluatin…
Densely Connected Residual Network for Attack Recognition
Peilun Wu, Nour Moustafa, Shiyi Yang +1
High false alarm rate and low detection rate are the major sticking points for unknown threat perception. To address the problems, in the paper, we present a densely connected resi…
Enhancing network forensics with particle swarm and deep learning: The particle deep framework
Nickolaos Koroniotis, Nour Moustafa
The popularity of IoT smart things is rising, due to the automation they provide and its effects on productivity. However, it has been proven that IoT devices are vulnerable to bot…
A Systemic IoT-Fog-Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing
Nour Moustafa
Abstract--- With the rapid growth of the Internet of Things (IoT), current Cloud systems face various drawbacks such as lack of mobility support, location-awareness, geo-distributi…
Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset
Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova +1
The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. S…
Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing
Nour Moustafa, Gideon Creech, Elena Sitnikova +1
With the ubiquitous computing of providing services and applications at anywhere and anytime, cloud computing is the best option as it offers flexible and pay-per-use based service…
Privacy Preservation Intrusion Detection Technique for SCADA Systems
Marwa Keshk, Nour Moustafa, Elena Sitnikova +1
Supervisory Control and Data Acquisition (SCADA) systems face the absence of a protection technique that can beat different types of intrusions and protect the data from disclosure…
Probability Risk Identification Based Intrusion Detection System for SCADA Systems
Thomas Marsden, Nour Moustafa, Elena Sitnikova +1
. As Supervisory Control and Data Acquisition (SCADA) systems control several critical infrastructures, they have connected to the internet. Consequently, SCADA systems face differ…
Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques
Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova +1
The IoT is a network of interconnected everyday objects called things that have been augmented with a small measure of computing capabilities. Lately, the IoT has been affected by…
RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis
Nour Moustafa, Jill Slay
Network forensic techniques help in tracking different types of cyber attack by monitoring and inspecting network traffic. However, with the high speed and large sizes of current n…
A hybrid feature selection for network intrusion detection systems: Central points
Nour Moustafa, Jill Slay
Network intrusion detection systems are an active area of research to identify threats that face computer networks. Network packets comprise of high dimensions which require huge e…