Next generation cellular networks such as 5G and 6G promise to support emerging applications such as enhanced mobile broadband, mission critical applications for the first responder, remote surgery, and industrial IOT among others. While Network Function Virtualization and Software Defined Networking open up the door for programmable networks and rapid service creation, these also offer both security opportunities, and introduces additional challenges and complexities. The talk focuses on various security challenges and opportunities introduced by 5G enablers such as Hypervisor, Virtual Network Functions (VNFs), SDN controller, orchestrator, network slicing, cloud RAN, edge cloud, and virtual security function. This talk introduces threat taxonomy for 5G security from an end-to-end system perspective including, interfaces, protocols, potential threats introduced by these enablers, and associated mitigation techniques. Additionally, this talk highlights how AI/ML can help enhance security features of these networks and elaborates some adverse effects of AI/ML.  Finally, the talk introduces some of the ongoing activities within various standards communities including open source consortiums, large scale NSF testbeds, and illustrates a few deployment use case scenarios.

The rapid rise in the use of small unmanned aerial vehicles (UAVs) introduces both technological benefits and significant security concerns, particularly in scenarios where accurately differentiating drones from birds is essential. This study proposes a structured methodology for distinguishing between drones and birds by analyzing simulated radar micro-Doppler signatures using a Support Vector Machine (SVM) classifier. Synthetic radar signals are generated by modeling the unique motion patterns of drones—specifically blade rotation—and the flapping of bird wings, capturing their distinct micro-Doppler features. In addition to extracting spectrogram-based time-frequency features, this work applies the frequency domain Gramian Angular Field (GAF) method to convert the spectral information into a two-dimensional image representation that preserves temporal dependencies. These features are condensed into average spectral profiles and GAF-based patterns for use in classification. The proposed SVM classifier was trained on features with embedded variability in drone and bird motion parameters to enhance generalization. Results show that with training signals based on a drone rotor speed of 3,000 rpm (16.7% noise) and body speed of 10m/s (40% noise), the classifier achieved nearly 100% accuracy for test cases with rotor speeds as low as 1,200 rpm and body speeds down to 6 m/s, demonstrating robust performance under realistic variations in flight dynamics. The results confirm the effectiveness of combining SVM with spectrogram and frequency domain GAF features for airborne object discrimination, while maintaining computational efficiency suitable for real- time surveillance scenarios. Furthermore, the approach holds promise for extension to real- world radar data applications.