83 citations · 137 across the 10 of their papers we have counts for
17 papers
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…
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…