Major events, such as the Olympic Games, pose unique cybersecurity challenges due to their high visibility, short duration, and complex system environments, making them prime targets for nation-state-level advanced persistent threat (APT). With sufficient preparation time, attackers often conduct early reconnaissance, among which large-scale password guessing against event-related systems is a recurrent preparatory behavior frequently observed before and during major events. Traditional defense mechanisms, such as firewalls and honeypots, can capture direct intrusion attempts but often overlook these pre-attack behaviors, missing valuable opportunities for early detection. To address this gap, we propose a no-answer puzzle scheme, a lightweight and non-loginable password-interaction system tailored for major-event scenarios to capture pre-attack preparations from attackers. Based on motivational psychology, the persistent and covert behaviors of APT attacks are all driven by a strong curiosity towards the target systems. Therefore, based on the captured interaction data, we define an attacker curiosity metric, design a computational update mechanism, and develop a fuzzy-logic-based model for inferring attacker motivation. Practical application in cybersecurity protection for major events has demonstrated that the system can effectively detect and capture potential attackers at an early stage without disrupting the normal operation of protected systems, thereby supporting the goal of zero cybersecurity incidents during major events.
Chenlu Zhuansun, Yiji Lin, Yuan Liu et al.· Scientia Sinica Informationi...· 0 citations
Single-cell drug perturbation models are increasingly used to predict how compounds remodel cellular states, but they are still largely assessed by expression reconstruction. Whether high expression similarity reflects preservation of drug-response signatures remains unclear. Here we present scDrugPerturb-Bench, a mechanism-annotated benchmark that links matched control and drug-treated single-cell RNA-sequencing profiles to literature-curated directional key-gene evidence. The resource covers 181 datasets, 423 annotated response cases, 717 unique key genes and 2.5 million cells. We introduce the Mechanism Fidelity Score (MFS) to evaluate key-gene direction, effect-size recovery, gene-set co-herence, mechanism specificity and pathway-level response polarity. Across 12 perturbation-prediction models, 3 baselines and 10 data splits, expression-similarity metrics were weakly aligned with MFS and selected different model configurations. Mechanism-aware selection improved early drug retrieval in a transcriptome-based drug design evaluation, indicating that MFS provides practical information beyond benchmark reporting. Systematic benchmarking revealed limited fidelity to drug-response signatures across cell-line and source-integrated settings. Frozen single-cell foundation model embeddings produced local, metric-dependent gains rather than universal improvements, and source context substantially reshaped model assessment. Hard-negative tests further showed that plausible perturbation responses can arise from non-specific transcriptional shortcuts. These results show that expression reconstruction is an insufficient proxy for preserving drug-response signatures and establish scDrugPerturb-Bench as a benchmark for mechanism-aware evaluation of single-cell drug perturbation models.
