Defense Against Machine Learning Based Attacks in Multi-UAV Networks: A Network Coding Based Approach

Chen, Yu-Jia; Chen, Xiaochun; Pan, Miao

doi:10.1109/tnse.2022.3165971

Cited by 5 publications

(2 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing security measures often fall short in addressing the unique challenges posed by UAV deployments in smart cities. Conventional intrusion detection systems and privacy protection techniques are not tailored to the dynamic and resource-constrained nature of UAV networks [17,18]. This gap in existing solutions underscores the need for a specialized framework designed specifically for this context.…”

Section: Problem Statement and Motivationmentioning

confidence: 99%

“…The authors of [18] conducted an investigation into security challenges within UAV networks, specifically concerning eavesdropping attacks enabled by the broadcast nature of wireless channels and wide aerial coverage. They explored the application of machine learning techniques to decrypt encrypted locations derived from wireless data transmission.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Secure and Privacy-Preserving Intrusion Detection and Prevention in the Internet of Unmanned Aerial Vehicles

Ntizikira,

Lei,

Alblehai

et al. 2023

Sensors

View full text Add to dashboard Cite

In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model’s effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%).

show abstract

Section: Problem Statement and Motivationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%