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CyberLearning: Effectiveness Analysis of Machine Learning Security
  Modeling to Detect Cyber-Anomalies and Multi-Attacks

CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks

Internet of Things (IoT), 2021
28 March 2021
Iqbal H. Sarker
    AAML
ArXiv (abs)PDFHTML

Papers citing "CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks"

4 / 4 papers shown
Machine Learning for Healthcare-IoT Security: A Review and Risk
  Mitigation
Machine Learning for Healthcare-IoT Security: A Review and Risk MitigationIEEE Access (IEEE Access), 2024
Mirza Akhi Khatun
S. Memon
Ciarán Eising
L. L. Dhirani
212
64
0
17 Jan 2024
Data-Driven Intelligence can Revolutionize Today's Cybersecurity World:
  A Position Paper
Data-Driven Intelligence can Revolutionize Today's Cybersecurity World: A Position Paper
Iqbal H. Sarker
Helge Janicke
Leandros A. Maglaras
S. Çamtepe
167
5
0
09 Aug 2023
Decentralized Online Federated G-Network Learning for Lightweight
  Intrusion Detection
Decentralized Online Federated G-Network Learning for Lightweight Intrusion DetectionIEEE/ACM International Symposium on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems (MASCOTS), 2023
Mert Nakıp
Baran Can Gül
Erol Gelenbe
296
14
0
22 Jun 2023
Machine and Deep Learning for IoT Security and Privacy: Applications,
  Challenges, and Future Directions
Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future DirectionsSecurity and Communication Networks (SCN), 2022
Subrato Bharati
Prajoy Podder
309
65
0
24 Oct 2022
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