Autonomous detection of malicious events using machine learning models in drone networks
Author:
Affiliation:
1. The University of New South Wales at ADFA, Canberra, ACT, Australia
2. Macquarie University, Sydney, NSW, Australia
Funder
Spitfire Memorial Defence Fellowship
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3414045.3415951
Reference17 articles.
1. Intrusion Detection Systems for Networked Unmanned Aerial Vehicles: A Survey
2. Reza Fotohi Eslam Nazemi and Fereidoon Shams Aliee. 2020. An Agent-Based Self-Protective Method to Secure Communication between UAVs in Unmanned Aerial Vehicle Networks. Vehicular Communications (2020) 100267. Reza Fotohi Eslam Nazemi and Fereidoon Shams Aliee. 2020. An Agent-Based Self-Protective Method to Secure Communication between UAVs in Unmanned Aerial Vehicle Networks. Vehicular Communications (2020) 100267.
3. Communication Security of Unmanned Aerial Vehicles
4. Editorial: First Quarter 2016 IEEE Communications Surveys and Tutorials
5. Cyber security threat analysis and modeling of an unmanned aerial vehicle system
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