Asaf Shabtai

Artificial Intelligence · 2009–2018

A researcher in the Science corpus: 7 papers; trusts 156 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #30,738 of 35,345 by trust here, #9,465 by citations
• Computer Networks and Communications — 40 citations into it
• Signal Processing — 34 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Model Architectures — 19 citations into it
• Control and Systems Engineering — 10 citations into it
• Information Systems — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Asaf Shabtai trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Robert E. Schapire
5. J. Ross Quinlan
6. Karen Simonyan
7. Yoshua Bengio
8. Alex Krizhevsky
9. Christian Szegedy
10. Yann LeCun

Who trusts Asaf Shabtai overall

1. Martina Lindorfer
2. M. L. Polla
3. Yanfang Ye
4. Kimberly Tam
5. Taeguen Kim
6. Thijs Van Ede
7. Matthias Neugschwandtner
8. Fabio Martinelli
9. Tao Li
10. Ali Feizollah

Papers from Asaf Shabtai's perspective

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

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

Their papers

• Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection (2018) — 1,004 citations
• Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. (2018) — 472 citations
• N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders (2018) — 1,390 citations
• Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks (2018) — 329 citations
• ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis (2017) — 407 citations
• “Andromaly”: a behavioral malware detection framework for android devices (2012) — 825 citations
• Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey (2009) — 332 citations