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Integrated sensing and communication (ISAC) is considered an emerging technology for 6th-generation (6G) wireless and mobile networks. It is expected to enable a wide variety of vertical applications, ranging from unmanned aerial vehicles (UAVs) detection for critical infrastructure protection to physiological sensing for mobile healthcare. Despite its significant socioeconomic benefits, ISAC technology also raises unique challenges in system security and user privacy. Being aware of the security and privacy challenges, understanding the trade-off between security and communication performance, and exploring potential countermeasures in practical systems are critical to a wide adoption of this technology in various application scenarios. This talk will discuss various security and privacy threats in emerging ISAC systems with a focus on communication-centric ISAC systems, that is, using the cellular or WiFi infrastructure for sensing. We will then examine potential mechanisms to secure ISAC systems and protect user privacy at the physical and data layers under different sensing modes. At the wireless physical (PHY) layer, an ISAC system is subject to both passive and active attacks, such as unauthorized passive sensing, unauthorized active sensing, signal spoofing, and jamming. Potential countermeasures include wireless channel/radio frequency (RF) environment obfuscation, waveform randomization, anti-jamming communication, and spectrum/RF monitoring. At the data layer, user privacy could be compromised during data collection, sharing, storage, and usage. For sensing systems powered by artificial intelligence (AI), user privacy could also be compromised during the model training and inference stages. An attacker could falsify the sensing data to achieve a malicious goal. Potential countermeasures include the application of privacy enhancing technologies (PETs), such as data anonymization, differential privacy, homomorphic encryption, trusted execution, and data synthesis. 

In intelligent IoT networks, an IoT user is capable of sensing the spectrum and learning from its observation to dynamically access the wireless channels without interfering with the primary user’s signal. The network, however, is potentially subject to primary user emulation and jamming attacks. In the existing works, various attacks and defense mechanisms for spectrum sharing in IoT networks have been proposed. This paper systematically conducts a targeted survey of these efforts and proposes new approaches for future studies to strengthen the communication of IoT users. Our proposed methods involve the development of intelligent IoT devices that go beyond existing solutions, enabling them not only to share the spectrum with licensed users but also to effectively thwart potential attackers. First, considering practical aspects of imperfect spectrum sensing and delay, we propose to utilize online machine learning-based approaches to design spectrum sharing attack policies. We also investigate the attacker’s channel observation/sensing capabilities to design attack policies using time-varying feedback graph models. Second, taking into account the IoT devices’ practical characteristics of channel switching delay, we propose online learning-based channel access policies for optimal defense by the IoT device to guarantee the maximum network capacity. We then highlight future research directions, focusing on the defense of IoT devices against adaptive attackers. Finally, aided by concepts from intelligence and statistical factor analysis tools, we provide a workflow which can be utilized for devices’ intelligence factors impact analysis on the defense performance.