Követés
Yair Meidan
Yair Meidan
Experienced Data Scientist / Applied Machine Learning Researcher
E-mail megerősítve itt: post.bgu.ac.il - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Y Meidan, M Bohadana, Y Mathov, Y Mirsky, A Shabtai, D Breitenbacher, ...
IEEE Pervasive Computing 17 (3), 12-22, 2018
15352018
ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis
Y Meidan, M Bohadana, A Shabtai, JD Guarnizo, M Ochoa, ...
Proceedings of the Symposium on Applied Computing, 506-509, 2017
5252017
Detection of Unauthorized IoT Devices Using Machine Learning Techniques
Y Meidan, M Bohadana, A Shabtai, M Ochoa, NO Tippenhauer, ...
arXiv preprint arXiv:1709.04647, 2017
3182017
Security testbed for Internet-of-Things devices
S Siboni, V Sachidananda, Y Meidan, M Bohadana, Y Mathov, S Bhairav, ...
IEEE Transactions on Reliability 68 (1), 23-44, 2019
2272019
Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining
Y Meidan, B Lerner, G Rabinowitz, M Hassoun
IEEE Transactions on Semiconductor Manufacturing 24 (2), 237-248, 2011
1152011
A novel approach for detecting vulnerable IoT devices connected behind a home NAT
Y Meidan, V Sachidananda, H Peng, R Sagron, Y Elovici, A Shabtai
Computers & Security 97, 101968, 2020
94*2020
DeepStream: Autoencoder-based stream temporal clustering and anomaly detection
S Harush, Y Meidan, A Shabtai
Computers & Security 106, 102276, 2021
362021
Data Mining for Cycle Time Key Factor Identification and Prediction in Semiconductor Manufacturing
Y Meidan, B Lerner, M Hassoun, G Rabinowitz
IFAC Proceedings Volumes 42 (4), 217-222, 2009
142009
METHOD AND APPARATUS FOR DETERMINING AN IDENTITY OF AN UNKNOWN INTERNET-OF-THINGS (IoT) DEVICE IN A COMMUNICATION NETWORK
M Ochoa, NO Tippenhauer, J Guarnizo, Y Elovici, A Shabtai, M Bohadana, ...
US Patent App. 16/489,691, 2020
132020
CADeSH: Collaborative anomaly detection for smart homes
Y Meidan, D Avraham, H Libhaber, A Shabtai
IEEE Internet of Things Journal 10 (10), 8514-8532, 2022
112022
D-Score: An expert-based method for assessing the detectability of IoT-related cyber-attacks
Y Meidan, D Benatar, R Bitton, D Avraham, A Shabtai
Computers & Security 126, 103073, 2023
92023
Piping Botnet-Turning Green Technology into a Water Disaster
B Nassi, M Sror, I Lavi, Y Meidan, A Shabtai, Y Elovici
arXiv preprint arXiv:1808.02131, 2018
42018
GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation
E Malul, Y Meidan, D Mimran, Y Elovici, A Shabtai
arXiv preprint arXiv:2405.19954, 2024
22024
DeepStream: autoencoder-based stream temporal clustering
S Harush, Y Meidan, A Shabtai
Proceedings of the 36th Annual ACM Symposium on Applied Computing, 445-448, 2021
22021
IoT-deNAT: Outbound flow-based network traffic data of IoT and non-IoT devices behind a home NAT
Y Meidan, V Sachidananda, H Peng, R Sagron, Y Elovici, A Shabtai
https://zenodo.org/record/3924770, 2020
22020
CADeSH Dataset: Collaborative Anomaly Detection for Smart Homes
Y Meidan, D Avraham, H Libhaber, A Shabtai
https://doi.org/10.5281/zenodo.6406052, 2022
2022
Dataset: Responses to AHP-style questionnaires regarding the publication "D-Score: A Novel Expert-Based Method for Assessing the Detectability of IoT-Related Cyber-Attacks"
Y Meidan, D Benatar, R Biton, A Shabtai
https://zenodo.org/record/4018614, 2020
2020
Botnet IND: About Botnets of Botless IoT Devices
B Nassi, Y Meidan, D Nassi, A Shabtai, Y Elovici
Cryptology ePrint Archive, 2020
2020
Identifying and Quantifying Factors Affecting Waiting Time and Its Prediction in Manufacturing Fabs Using Machine Learning
Y Meidan
Ben Gurion University of the Negev, 2009
2009
A rendszer jelenleg nem tudja elvégezni a műveletet. Próbálkozzon újra később.
Cikkek 1–19