Konrad Rieck

Artificial Intelligence · 2006–2021

A researcher in the Science corpus: 18 papers; trusts 149 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 214 citations into it; #10,643 of 35,345 by trust here, #16,471 by citations
• Signal Processing — 179 citations into it; #750 of 7,572 by trust here, #400 by citations
• Computer Networks and Communications — 159 citations into it; #604 of 9,886 by trust here, #357 by citations
• Information Systems — 83 citations into it; #996 of 8,771 by trust here, #750 by citations
• Safety, Robustness & Interpretability — 74 citations into it; #2,107 of 2,251 by trust here, #1,731 by citations
• Software — 49 citations into it; #269 of 1,227 by trust here, #95 by citations
• Privacy & Security — 46 citations into it; #346 of 709 by trust here, #194 by citations
• Computer Vision and Pattern Recognition — 40 citations into it

Whom Konrad Rieck trusts overall

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

1. Ke Wang
2. Christopher Krügel
3. Thorsten Joachims
4. George Nagy
5. Eleazar Eskin
6. Salvatore J. Stolfo
7. Pavel Laskov
8. Alex Smola
9. John Shawe-Taylor
10. Matthew V. Mahoney

Who trusts Konrad Rieck overall

1. Pavel Laskov
2. Marius Kloft
3. Nedim Srndic
4. Battista Biggio
5. Nicholas Carlini
6. Davide Maiorca
7. Roberto Perdisci
8. Daniele Sgandurra
9. Marcos Sebastián
10. Nico Görnitz

Papers from Konrad Rieck's perspective

Ordered by how strongly the researchers Konrad Rieck 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning (2020) — 83 citations
• Toward Supervised Anomaly Detection (2014) — 418 citations
• Machine Unlearning of Features and Labels (2021) — 322 citations
• Intelligent Data Analysis (2020) — 334 citations
• Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017) — 307 citations
• Comprehensive Analysis and Detection of Flash-Based Malware (2016) — 19 citations
• Modeling and Discovering Vulnerabilities with Code Property Graphs (2014) — 917 citations
• DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014) — 2,526 citations
• Poisoning behavioral malware clustering (2014) — 157 citations
• Structural detection of android malware using embedded call graphs (2013) — 349 citations
• Detection of Intrusions and Malware, and Vulnerability Assessment (2013) — 35 citations
• Automatic analysis of malware behavior using machine learning (2011) — 764 citations
• A Malware Instruction Set for Behavior-Based Analysis (2010) — 91 citations
• Forschungsberichte der Fakultät IV – Elektrotechnik und Informatik C UJO : Efficient Detection and Prevention of Drive-by-Download Attacks (2010)
• Linear-Time Computation of Similarity Measures for Sequential Data (2008) — 117 citations
• Learning and Classification of Malware Behavior (2008) — 670 citations
• Language models for detection of unknown attacks in network traffic (2007) — 119 citations
• Detecting Unknown Network Attacks Using Language Models (2006) — 72 citations