Nour Moustafa

Computer Networks and Communications · 2015–2021

A researcher in the Science corpus: 10 papers; trusts 42 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 130 citations into it; #445 of 9,886 by trust here, #843 by citations
• Artificial Intelligence — 100 citations into it; #3,952 of 35,345 by trust here, #7,241 by citations
• Signal Processing — 67 citations into it; #609 of 7,572 by trust here, #1,055 by citations
• Information Systems — 39 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Model Architectures — 10 citations into it

Whom Nour Moustafa trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jill Slay
2. Mahbod Tavallaee
3. Iman Sharafaldin
4. Mrutyunjaya Panda
5. Anil K. Jain
6. Geoffrey E. Hinton
7. Sepp Hochreiter
8. Ana Justel
9. Ali Akbar Ghorbani
10. John McHugh

Who trusts Nour Moustafa overall

1. Mohanad Sarhan
2. Iqbal H. Sarker
3. Siamak Layeghy
4. Sydney Mambwe Kasongo
5. Ansam Khraisat
6. Chaouki Khammassi
7. Nadia Chaabouni
8. Wai Weng Lo
9. Zhibo Zhang
10. Hongyu Liu

Papers from Nour Moustafa's perspective

Ordered by how strongly the researchers Nour Moustafa trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets (2021) — 636 citations
• A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks (2021) — 353 citations
• ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets (2021) — 363 citations
• A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020) — 296 citations
• TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020) — 684 citations
• NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems (2020) — 325 citations
• An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things (2019) — 472 citations
• Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset (2018) — 1,683 citations
• The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set (2016) — 1,076 citations
• UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) (2015) — 3,775 citations