🤖 AI Summary
This study addresses the challenge of cross-domain cyber threats in satellite-enabled Internet of Things (IoT) systems, where misaligned incentives between terrestrial and satellite operators and the difficulty of quantifying attack impacts hinder effective collaborative defense. To overcome these barriers, this work proposes the first tripartite security game framework that integrates both domains, aligning the defensive incentives of IoT operators and satellite service providers through a traffic pricing mechanism. Coupled with an efficient learning algorithm to optimize strategic decisions for both parties, the proposed approach mitigates incentive misalignment and significantly enhances cross-domain collaborative defense performance even under conditions of divergent interests. Experimental results demonstrate the framework’s effectiveness in improving system-wide security outcomes.
📝 Abstract
As the adoption of satellite-enabled Internet of Things (IoT) continues to rise, its intricate multidomain architecture becomes increasingly susceptible to cross-domain cyber threats. Attackers can exploit compromised IoT devices, inject malicious packets into data streams aggregated at the IoT gateway for satellite backhaul, and potentially endanger the satellite network during transmission by exploiting the hardware, software, and protocol vulnerabilities. Compared to single-domain defenses, cooperative defense at the IoT devices, IoT access network, and satellite transmission network provides fine-granularity defense against cross-domain intelligent attacks. However, quantifying cross-domain impacts and tilting incentive misalignment among different participants remain significant challenges, making systematic cooperative defense development a complex task. To address this, we develop a tripartite security game framework to characterize the impacts of attacks and defense methods across both the terrestrial and satellite domains. Leveraging this game model, we devise flow pricing to optimally motivate the IoT network operator (IoT-NO) to prevent malicious packet infiltration into the satellite domain. Subsequently, we propose efficient learning algorithms enabling both the IoT-NO to ascertain their ideal flow sampling strategies and the satellite service provider (SAT-SP) to determine optimal flow pricing. The simulation results corroborate the effectiveness of the consolidated game in counteracting cross-domain cyber attacks and facilitating cooperative defense between the IoT-NO and the SAT-SP with nonaligned incentives.