What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Computer network
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Data mining, Malware and Computer security.
His Classifier study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Static program analysis, bridging the gap between disciplines.
Yuval Elovici works mostly in the field of Machine learning, limiting it down to concerns involving Host and, occasionally, Network intrusion detection, Channel network, Local area network, Feature extraction and False positive rate.
The various areas that Yuval Elovici examines in his Data mining study include Active learning, Process, Statistical classification, Task and Data set.
His work deals with themes such as Android, Anomaly detection, Mobile device and Behavioral pattern, which intersect with Malware.
His Computer security research incorporates elements of Software, Identity, The Internet and Internet privacy.
His most cited work include:
- Andromaly: a behavioral malware detection framework for android devices (588 citations)
- Endoscopic device comprising linear staplers and a video camera on its distal end (458 citations)
- Google Android: A Comprehensive Security Assessment (335 citations)
What are the main themes of his work throughout his whole career to date?
Computer security, Artificial intelligence, Malware, Data mining and Machine learning are his primary areas of study.
Yuval Elovici combines subjects such as Anomaly detection, The Internet, World Wide Web, Botnet and Internet privacy with his study of Computer security.
Much of his study explores Artificial intelligence relationship to Pattern recognition.
The Malware study which covers Mobile device that intersects with Mobile computing.
His Data mining study frequently draws connections between adjacent fields such as Statistical classification.
His Machine learning study integrates concerns from other disciplines, such as Computer worm and Robustness.
He most often published in these fields:
- Computer security (28.76%)
- Artificial intelligence (21.80%)
- Malware (17.48%)
What were the highlights of his more recent work (between 2017-2021)?
- Computer security (28.76%)
- Artificial intelligence (21.80%)
- Malware (17.48%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Computer security, Artificial intelligence, Malware, Machine learning and Computer network.
His study in the fields of Attack surface under the domain of Computer security overlaps with other disciplines such as Drone.
As a part of the same scientific study, Yuval Elovici usually deals with the Artificial intelligence, concentrating on Pattern recognition and frequently concerns with Detector.
His work in Malware addresses issues such as Covert channel, which are connected to fields such as Password and Electrical engineering.
His Network security and Server study in the realm of Computer network connects with subjects such as Digital watermarking.
His Deep learning research includes themes of Convolutional neural network, Data mining and Authentication.
Between 2017 and 2021, his most popular works were:
- N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders (219 citations)
- Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. (137 citations)
- N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders (108 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
Yuval Elovici mostly deals with Artificial intelligence, Malware, Computer security, Machine learning and Anomaly detection.
In Artificial intelligence, Yuval Elovici works on issues like Pattern recognition, which are connected to Biometrics.
His Malware study combines topics from a wide range of disciplines, such as Air gap, Software, Cloud computing and Executable.
Computer security and Life-critical system are frequently intertwined in his study.
His research integrates issues of Adversarial system, Internet access and Robustness in his study of Machine learning.
His work carried out in the field of Anomaly detection brings together such families of science as False positive paradox, Autoencoder, Pipeline, Blockchain and Botnet.
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