Spectrum coexistence between 5G and Wi-Fi in the coveted 5GHz spectrum band unleashes new possibilities for more effective spectrum utilization. While the Listen-Before-Talk-based channel access mechanism with the self-deferral-based method enhances the relative fairness of this coexistence framework, it introduces new vulnerabilities yet to be addressed. This research presents a unique attack approach, Random Channel Access Deterrence (RanCAD), that exploits a novel vulnerability in the channel access mechanism. In the proposed attack, a malicious access point deceives a victim 5G base station into deferring its access to the shared channel, resulting in higher channel access delay and lower spectrum utilization. In addition, we propose a Discrete Time Markov Chain (DTMC) to study the proposed attack model, which helps illustrate the attack's impact on the victim's performance. To our knowledge, this is the first work to introduce this vulnerability in the channel access mechanism between coexisting 5G and Wi-Fi networks in the 5GHz band.

3GPP has introduced Private 5G to support the next-generation industrial automation system (IAS) due to the versatility and flexibility of 5G architecture. Besides the 3.5GHz CBRS band, unlicensed spectrum bands, like 5GHz, are considered as an additional medium because of their free and abundant nature. However, while utilizing the unlicensed band, industrial equipment must coexist with incumbents, e.g., Wi-Fi, which could introduce new security threats and resuscitate old ones. In this paper, we propose a novel attack strategy conducted by a mobility-enabled malicious Wi-Fi access point (mmAP), namely PACMAN attack, to exploit vulnerabilities introduced by heterogeneous coexistence. A mmAP is capable of moving around the physical surface to identify mission-critical devices, hopping through the frequency domain to detect the victim's operating channel, and launching traditional MAC layer-based attacks. The multi-dimensional mobility of the attacker makes it impervious to state-of-the-art detection techniques that assume static adversaries. In addition, we propose a novel Markov Decision Process (MDP) based framework to intelligently design an attacker's multi-dimensional mobility in space and frequency. Mathematical analysis and extensive simulation results exhibit the adverse effect of the proposed mobility-powered attack.

Data privacy, a critical human right, is gaining importance as new technologies are developed, and the old ones evolve. In mobile platforms such as Android, data privacy regulations require developers to communicate data access requests using privacy policy statements (PPS). This case study cross-examines the PPS in popular social media (SM) apps --- Facebook and Twitter --- for features of language ambiguity, sensitive data requests, and whether the statements tally with the data requests made in the Manifest file. Subsequently, we conduct a comparative analysis between the PPS of these two apps to examine trends that may constitute a threat to user data privacy.