This paper investigates the event-triggered guaranteed-performance security control problem for nonlinear systems subject to hybrid cyber-attacks (denial-of-service (DoS) attacks and deception attacks). First, a fuzzy-model-based description approach is employed to characterize the nonlinear system, effectively capturing its complex dynamic characteristics. Second, to alleviate the communication burden in bandwidth-constrained networks, a history-dependent event-triggering mechanism incorporating historical transmission data is designed, dynamically optimizing information transmission efficiency between sensors and controllers. Third, a security control framework with prescribed performance metrics is constructed. By integrating Lyapunov functional theory and matrix decomposition techniques, sufficient conditions ensuring system stability are rigorously derived. This guarantees that the proposed guaranteed-performance security control scheme can effectively counteract malicious impacts induced by cyber-attacks. Finally, comprehensive simulation case studies validate the effectiveness of the proposed security control method, demonstrating its robustness against hybrid attack patterns while maintaining desired control performance. The results show that, compared to the traditional event-triggered mechanism (ETM) and the dynamic event-triggered mechanism (DETM), the dynamic memory event-triggered mechanism (DMETM) proposed in this paper reduces the number of triggers by approximately 63.87$\%$and 18.49$\%$, respectively; Compared to the recent Adaptive Memory Event Triggering Mechanism (AMETM), it further reduces communication overhead by approximately 8.12$\%$. Furthermore, the system maintains exponential mean-square stability under mixed attacks and achieves the specified$H\_{\infty }$performance level.

Previous learning-based parameter query optimization (PQO) methods excel in static workloads by precisely selecting optimal plans in a cache with a fixed set of representative plans. However, these methods struggle in dynamic workloads because they cannot predict over dynamically changed plan caches. These queries that fall outside the previously observed query parameter distribution have the risk of reusing suboptimal plans. Unlike traditional PQO methods that learn mappings from parametric query parameters to a fixed set of execution plans, our adaptive PQO framework ( APQO ) takes both query parameters and the plans themselves as model inputs, thereby handling variable numbers of plans in dynamic workloads. By embedding plan representations through representation learning, we pre-train a foundation model offline, enabling APQO to acquire a generalizable plan performance prediction model. Leveraging the foundation model's predictive capability along with a hybrid data augmentation strategy, we train an online calibration model with minimal training data for distribution-shifted new queries, rapidly adapting knowledge for reusing new plans. APQO is natively designed to handle the characteristics of dynamic workloads. Experimental results show that APQO outperforms existing PQO methods in dynamic workloads, achieving a higher cache hit ratio and significantly reducing query latency.