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With the proliferation of electronic devices and electricity-based mobility solutions, the significance of wireless power transfer technology has increased substantially. However, ensuring secure and reliable power transmission to authorized users remains a significant challenge. Addressing this complex issue requires an integrated approach that balances efficiency, stability, and security considerations. While current efforts primarily focus on improving charging efficiency and user convenience, integrating robust security measures into wireless charging infrastructure is challenging due to its inherently open nature and susceptibility to external interference. Technical advancements are required to strengthen the security of the wireless charging infrastructure; however, these should be balanced with power loss management. This study tackles two core issues: the increasing hardware requirements for billing system authentication protocols and the interception of wireless charging signals by unauthorized users, leading to power theft and subsequent losses. To address these challenges, we propose a mechanism termed "LazyFrog". This mechanism dynamically adjusts the frequency hopping schedule, activating frequency changes only in response to detected threats during remote charging or upon identifying unauthorized access attempts. The proposed mechanism compares the expected power reception at the device with the actual power supplied by the charging station, enabling the detection of abnormal power losses. By minimizing unnecessary frequency changes and optimizing energy consumption, LazyFrog reduces hardware requirements. Moreover, we have implemented a relative distance estimation mechanism to facilitate efficient power transfer as wireless devices move within the charging environment. With these features, LazyFrog demonstrates a secure, flexible, and energy-efficient wireless charging system ready for practical application. [ABSTRACT FROM AUTHOR]

With the advancement in information and communication technology, modern society has relied on various computing systems in areas closely related to human life. However, cyberattacks are also becoming more diverse and intelligent, with personal information and human lives being threatened. The moving target defense (MTD) strategy was designed to protect mission-critical systems from cyberattacks. The MTD strategy shifted the paradigm from passive to active system defense. However, there is a lack of indicators that can be used as a reference when deriving general system components, making it difficult to configure a systematic MTD strategy. Additionally, even when selecting system components, a method to confirm whether the systematic components are selected to respond to actual cyberattacks is needed. Therefore, in this study, we surveyed and analyzed existing cyberattack information and MTD strategy research results to configure a component dataset. Next, we found the correlation between the cyberattack information and MTD strategy component datasets and used this to design and implement the MTD-Diorama data visualization engine to configure a systematic MTD strategy. Through this, researchers can conveniently identify the attack surface contained in cyberattack information and the MTD strategies that can respond to each attack surface. Furthermore, it will allow researchers to configure more systematic MTD strategies that can be used universally without being limited to specific computing systems. [ABSTRACT FROM AUTHOR]