# Security and Privacy for Modern Wireless Communication Systems, 2 nd Edition
## 2 Summary of the Contributions
Using content analysis of 56 public comments submitted in response to a U.S. Treasury Department Request for Information, the authors investigate four key areas: how CCIs are defined, how they can be mitigated, whether the current cyber insurance sector can manage such risks, and what role the government should play. The study finds that while stakeholders agree that CCIs involve large-scale financial loss, cross-sector impact, and critical infrastructure disruption, no consistent definition complicates risk modeling. Manycomments support the use of basic cybersecurity measures (e.g., MFA, patching, employee training) to mitigate CCI and suggest that insurers should mandate such practices. Most respondents believe the private insurance sector lacks the capacity or willingness to manage CCI alone, advocating for a federal backstop, modeled partly on the Terrorism Risk Insurance Program (TRIP), to stabilize the market. Concerns such as moral hazard, physical loss exclusions, and excessive regulation are acknowledged but seen as manageable through careful design. The study concludes with a proposed framework for a federal backstop and offers actionable insights for policymakers to strengthen national cybersecurity resilience. The tenth contribution, authored by [PERSON] et al., provides a comprehensive analysis of security, privacy, and trust challenges in 6G networks, structured around a layered security architecture that spans the physical, connection, and application layers. The paper begins by highlighting the transformative potential of 6G, including its ultra-low-latency capabilities, massive connectivity, and support for emerging technologies like XR, CRAS, BCI, and blockchain, while emphasizing that these advances demand a proactive approach to security from the ground up. At the physical layer, the authors detail vulnerabilities like eavesdropping, jamming, and spoofing and propose mitigations including Reconfigurable Intelligent Surfaces (RISs), Friendly Jamming, NOMA, frequency hopping, and reinforcement learning. Key risks such as DoS, MitM, and replay attacks for the connection layer are countered through Quantum Key Distribution (QKD), network slicing, and deep learning-based intrusion detection systems (IDSs). The application layer faces threats like social engineering, for which biometric authentication, Quantum Homomorphic Cryptography (QHC), and Authentication and Key Agreement (AKA) protocols are proposed. The paper also explores trust management models involving blockchain, smart contracts, and trust anchors, advocating for decentralized, transparent mechanisms. The authors offer a forward-looking blueprint for building secure, resilient, and trustworthy 6G infrastructures by integrating Zero Trust Architecture- and AI-driven defense strategies. The eleventh contribution, from [PERSON] et al., presents a novel hybrid machine learning and deep learning approach for Radio Frequency Fingerprinting (RFF) of LoRa devices, leveraging the residuals from Variational Mode Decomposition (VMD) instead of the commonly used modes. The authors argue that residual signals, which exclude common modulation components, better capture device-specific physical characteristics essential for accurate fingerprinting. The authors propose a feature-driven segmentation method to handle the computational burden of applying CNNs to spectrograms derived from these signals. This involves calculating Local Binary Pattern (LBP) and Shannon entropy to identify and extract the most discriminative regions of the spectrograms for input into a multi-head attention CNN. The method is evaluated on a public dataset of 10 LoRa devices, each contributing 500 signal bursts. Experimental results show that this residual-based and segmented spectrogram approach significantly improves classification accuracy (up to 91.9%) while reducing computational time compared to baseline methods using full spectrograms or original signals.