Ali Dehghantanha

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 12 papers; trusts 11 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it; #25,793 of 35,345 by trust here, #10,667 by citations
• Computer Networks and Communications — 119 citations into it; #6,893 of 9,886 by trust here, #2,044 by citations
• Signal Processing — 66 citations into it; #4,179 of 7,572 by trust here, #1,783 by citations
• Information Systems — 62 citations into it; #6,772 of 8,771 by trust here, #7,386 by citations
• Privacy & Security — 30 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Optimization & Training — 16 citations into it

Whom Ali Dehghantanha trusts overall

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

1. Ron Kohavi
2. Daniel Bilar
3. George Forman
4. Yoav Freund
5. Joshua Saxe
6. Justin Sahs
7. Zhenlong Yuan
8. Igor Santos
9. Razvan Pascanu
10. Lakshmanan Nataraj

Who trusts Ali Dehghantanha overall

1. Nour Moustafa
2. Thanh Thi Nguyen
3. Fatima Hussain

Papers from Ali Dehghantanha's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. Deep Learning
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks (2024) — 325 citations
• A survey on security and privacy of federated learning (2021) — 1,391 citations
• Federated-Learning-Based Anomaly Detection for IoT Security Attacks (2021) — 590 citations
• A systematic literature review of blockchain cyber security (2020) — 511 citations
• Sidechain technologies in blockchain networks: An examination and state-of-the-art review (2020) — 316 citations
• Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning (2019) — 307 citations
• A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks (2019) — 452 citations
• A survey on internet of things security: Requirements, challenges, and solutions (2019) — 308 citations
• Internet of Things security and forensics: Challenges and opportunities (2018) — 632 citations
• A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting (2018) — 417 citations
• Machine learning aided Android malware classification (2017) — 328 citations
• Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing (2016) — 328 citations