What is he best known for?
The fields of study he is best known for:
- Operating system
- The Internet
- Programming language
The scientist’s investigation covers issues in Computer security, Artificial intelligence, Malware, Machine learning and Static analysis.
Konrad Rieck works mostly in the field of Computer security, limiting it down to concerns involving The Internet and, occasionally, Computer virus, Computer network, High availability and Host.
His work in Artificial intelligence tackles topics such as Pattern recognition which are related to areas like Pairwise comparison, String and Data structure.
His study in Malware is interdisciplinary in nature, drawing from both Android and Evasion.
His study looks at the intersection of Machine learning and topics like Anomaly detection with Intrusion detection system, Language model, Process and Key.
His Static analysis study also includes fields such as
- Source code which connect with Code, Representation and Data mining,
- Software which connect with Data validation, Discriminative model and Buffer overflow.
His most cited work include:
- DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. (1114 citations)
- Automatic analysis of malware behavior using machine learning (503 citations)
- Learning and Classification of Malware Behavior (458 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Computer security, Artificial intelligence, Malware, Machine learning and Intrusion detection system.
His research on Computer security also deals with topics like
- The Internet together with Host and Computer virus,
- Software together with Static analysis and Graph database.
His Artificial intelligence research is multidisciplinary, incorporating elements of Adversary, Identification, Optimization problem and Pattern recognition.
Konrad Rieck studies Malware, focusing on Cryptovirology in particular.
His Machine learning study also includes
- Source code that intertwine with fields like Integer,
- Exploit which intersects with area such as Computer network.
Konrad Rieck has researched Intrusion detection system in several fields, including Anomaly detection, Distributed computing and Taxonomy.
He most often published in these fields:
- Computer security (42.86%)
- Artificial intelligence (30.16%)
- Malware (26.98%)
What were the highlights of his more recent work (between 2019-2021)?
- Computer security (42.86%)
- Artificial neural network (6.35%)
- Artificial intelligence (30.16%)
In recent papers he was focusing on the following fields of study:
Konrad Rieck spends much of his time researching Computer security, Artificial neural network, Artificial intelligence, Malware and Image scaling.
His research is interdisciplinary, bridging the disciplines of Variety and Computer security.
Konrad Rieck frequently studies issues relating to Behavioral pattern and Artificial neural network.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Vulnerability discovery.
Many of his research projects under Machine learning are closely connected to Work and TRACE with Work and TRACE, tying the diverse disciplines of science together.
The Vulnerability discovery study combines topics in areas such as Deep learning and Robustness.
Between 2019 and 2021, his most popular works were:
- Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning. (9 citations)
- Evaluating Explanation Methods for Deep Learning in Security (6 citations)
- Backdooring and Poisoning Neural Networks with Image-Scaling Attacks (6 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- The Internet
Konrad Rieck focuses on Image scaling, Artificial neural network, Artificial intelligence, Backdoor and Computer security.
His study of Image scaling brings together topics like Preprocessor, Machine learning and Adversarial system.
Konrad Rieck combines subjects such as Vulnerability discovery, Deep learning, Malware, Robustness and Data science with his study of Artificial neural network.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
